Quant for 5.0

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -3,69 +3,164 @@ dataset: Thermostatic/flowers
 license: other
 license_name: gemma-terms-of-use
 license_link: https://ai.google.dev/gemma/terms
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
 
-## Exllama v2 Quantizations of gemma-orchid-7b-dpo
+# Gemma Orchid 7b
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization.
+<div align="center">  
 
-## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/7pqiroePJW0WWm6JxwBoO.webp)
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+</div>
 
-Conversion was done using the default calibration dataset.
+This model is the second checkpoint of a future project. Its capable of function calling as well as having a strong base in communicational skills.
 
-Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
+This model has been finetuned on roughly 80k samples so far.
 
-Original model: https://huggingface.co/macadeliccc/gemma-orchid-7b-dpo
+# Training
 
++ Time to complete: ~20 hours
++ Datasets: Thermostatic/flowers, Intel/orca_dpo_pairs, jondurbin/truthy-dpo-v0.1, glaiveai/glaive_function_calling_v2
++ Cost: ~$20 in H100 hours
++ Evaluation loss: 0.69
++ Method: LoRa
++ Prompt Format: ChatML
 
-<a href="https://huggingface.co/bartowski/gemma-orchid-7b-dpo-exl2/tree/8_0">8.0 bits per weight</a>
+Thermostatic/flowers is a blend of open source model generations formatted in ShareGPT. It also includes all of capybara. 
 
-<a href="https://huggingface.co/bartowski/gemma-orchid-7b-dpo-exl2/tree/6_5">6.5 bits per weight</a>
+#### Running the model on a CPU
 
-<a href="https://huggingface.co/bartowski/gemma-orchid-7b-dpo-exl2/tree/5_0">5.0 bits per weight</a>
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-<a href="https://huggingface.co/bartowski/gemma-orchid-7b-dpo-exl2/tree/4_25">4.25 bits per weight</a>
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
 
-<a href="https://huggingface.co/bartowski/gemma-orchid-7b-dpo-exl2/tree/3_5">3.5 bits per weight</a>
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt")
 
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a single / multi GPU
+
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a GPU using different precisions
+
+* _Using `torch.float16`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.float16)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-## Download instructions
+* _Using `torch.bfloat16`_
 
-With git:
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/gemma-orchid-7b-dpo-exl2
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
 ```
 
-With huggingface hub (credit to TheBloke for instructions):
+#### Quantized Versions through `bitsandbytes`
+
+* _Using 8-bit precision (int8)_
+
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
 
-```shell
-pip3 install huggingface-hub
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
 ```
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `gemma-orchid-7b-dpo-exl2`:
+* _Using 4-bit precision_
+
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
 
-```shell
-mkdir gemma-orchid-7b-dpo-exl2
-huggingface-cli download bartowski/gemma-orchid-7b-dpo-exl2 --local-dir gemma-orchid-7b-dpo-exl2 --local-dir-use-symlinks False
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
 ```
 
-To download from a different branch, add the `--revision` parameter:
 
-Linux:
+#### Other optimizations
+
+* _Flash Attention 2_
+
+First make sure to install `flash-attn` in your environment `pip install flash-attn`
 
-```shell
-mkdir gemma-orchid-7b-dpo-exl2-6_5
-huggingface-cli download bartowski/gemma-orchid-7b-dpo-exl2 --revision 6_5 --local-dir gemma-orchid-7b-dpo-exl2-6_5 --local-dir-use-symlinks False
+```diff
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
++   attn_implementation="flash_attention_2"
+).to(0)
 ```
 
-Windows (which apparently doesn't like _ in folders sometimes?):
+### Inputs and outputs
+
+*   **Input:** Text string, such as a question, a prompt, or a document to be
+    summarized.
+*   **Output:** Generated English-language text in response to the input, such
+    as an answer to a question, or a summary of a document.
+
+## Evaluations 
+
+In progress
+
+## GGUF + iMatrix
 
-```shell
-mkdir gemma-orchid-7b-dpo-exl2-6.5
-huggingface-cli download bartowski/gemma-orchid-7b-dpo-exl2 --revision 6_5 --local-dir gemma-orchid-7b-dpo-exl2-6.5 --local-dir-use-symlinks False
-```
+In progress

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "macadeliccc/gemma-orchid-7b",
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 24576,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 16,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.38.1"
+}

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