File size: 4,536 Bytes
5d0d1e5 d5ec288 37a71b7 d5ec288 37a71b7 5d0d1e5 d5ec288 37a71b7 d5ec288 9d26c10 4f94bf4 d5ec288 9d26c10 37a71b7 4f94bf4 37a71b7 5e99797 d5ec288 37a71b7 d5ec288 37a71b7 d5ec288 5d0d1e5 d5ec288 5d0d1e5 37a71b7 d5ec288 37a71b7 d5ec288 5d0d1e5 d5ec288 5d0d1e5 37a71b7 d5ec288 37a71b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
---
base_model: meta-llama/Llama-2-13b-chat-hf
tags:
- generated_from_trainer
- trl
metrics:
- accuracy
model-index:
- name: llama-2-13b-reward-oasst1
results: []
datasets:
- tasksource/oasst1_pairwise_rlhf_reward
library_name: peft
pipeline_tag: text-classification
---
# llama-2-13b-reward-oasst1
This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on the [tasksource/oasst1_pairwise_rlhf_reward](https://huggingface.co/datasets/tasksource/oasst1_pairwise_rlhf_reward) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4810
- Accuracy: 0.7869
See also [vincentmin/llama-2-7b-reward-oasst1](https://huggingface.co/vincentmin/llama-2-7b-reward-oasst1) for a 7b version of this model.
## Model description
This is a reward model trained with QLoRA in 4bit precision. The base model is [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) for which you need to have accepted the license in order to be able use it. Once you've been given permission, you can load the reward model as follows:
```
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSequenceClassification, AutoTokenizer
peft_model_id = "vincentmin/llama-2-13b-reward-oasst1"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSequenceClassification.from_pretrained(
config.base_model_name_or_path,
num_labels=1,
load_in_4bit=True,
torch_dtype=torch.float16,
)
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, use_auth_token=True)
model.eval()
with torch.no_grad():
reward = model(**tokenizer("prompter: hello world. assistant: foo bar", return_tensors='pt')).logits
reward
```
For best results, one should use the prompt format used during training:
```
prompt = "prompter: <prompt_1> assistant: <response_1> prompter: <prompt_2> ..."
```
Please use a version of peft where [#755](https://github.com/huggingface/peft/pull/755) has been merged to make sure the model is loaded correctly. You can install `peft` with `pip install git+https://github.com/huggingface/peft.git` to make sure this is the case.
## Intended uses & limitations
Since the model was trained on oasst1 data, the reward will reflect any biases present in the oasst1 data.
## Training and evaluation data
The model was trained using QLoRA and the `trl` library's `RewardTrainer` on the [tasksource/oasst1_pairwise_rlhf_reward](https://huggingface.co/datasets/tasksource/oasst1_pairwise_rlhf_reward) dataset where examples with more than 512 tokens were filtered out from both the training and eval data.
## Training procedure
### Training hyperparameters
The following `bitsandbytes` quantization config was used during training:
- 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
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- max_seq_length: 512
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5602 | 0.08 | 250 | 0.5436 | 0.7388 |
| 0.6166 | 0.17 | 500 | 0.5340 | 0.7468 |
| 0.6545 | 0.25 | 750 | 0.4899 | 0.7644 |
| 0.5635 | 0.33 | 1000 | 0.4877 | 0.7532 |
| 0.5933 | 0.42 | 1250 | 0.4930 | 0.7660 |
| 0.5758 | 0.5 | 1500 | 0.4851 | 0.7740 |
| 0.5212 | 0.58 | 1750 | 0.5021 | 0.7788 |
| 0.5251 | 0.67 | 2000 | 0.4893 | 0.7804 |
| 0.5145 | 0.75 | 2250 | 0.4924 | 0.7853 |
| 0.5085 | 0.83 | 2500 | 0.4934 | 0.7853 |
| 0.617 | 0.92 | 2750 | 0.4803 | 0.7821 |
| 0.5525 | 1.0 | 3000 | 0.4810 | 0.7869 |
### Framework versions
- PEFT 0.5.0.dev0 (with https://github.com/huggingface/peft/pull/755)
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3 |