metadata
license: cc-by-nc-4.0
This reward function can be used for RLHF, including PPO, iterative SFT, iterative DPO.
Training
The base model is meta-llama/Meta-Llama-3-8B-Instruct.
We use the training script at https://github.com/WeiXiongUST/RLHF-Reward-Modeling.
Uses
from transformers import AutoTokenizer, pipeline
rm_tokenizer = AutoTokenizer.from_pretrained("sfairXC/FsfairX-LLaMA3-RM-v0.1")
device = 0 # accelerator.device
rm_pipe = pipeline(
"sentiment-analysis",
model="sfairXC/FsfairX-LLaMA3-RM-v0.1",
#device="auto",
device=device,
tokenizer=rm_tokenizer,
model_kwargs={"torch_dtype": torch.bfloat16}
)
pipe_kwargs = {
"return_all_scores": True,
"function_to_apply": "none",
"batch_size": 1
}
chat = [
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
test_texts = [tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False).replace(tokenizer.bos_token, "")]
pipe_outputs = rm_pipe(test_texts, **pipe_kwargs)
rewards = [output[0]["score"] for output in pipe_outputs]
Results
This Reward model is the SOTA open-source RM (Apr 20, 2024) on Reward-Bench.
| Metric | Score |
|---|---|
| Chat | 99.44 |
| Chat Hard | 65.13 |
| Safety | 88.76 |
| Reasoning | 88.3 |
References
The repo was part of the iterative rejection sampling fine-tuning and iterative DPO. If you find the content of this repo useful in your work, please consider cite it as follows:
@article{dong2023raft,
title={Raft: Reward ranked finetuning for generative foundation model alignment},
author={Dong, Hanze and Xiong, Wei and Goyal, Deepanshu and Pan, Rui and Diao, Shizhe and Zhang, Jipeng and Shum, Kashun and Zhang, Tong},
journal={arXiv preprint arXiv:2304.06767},
year={2023}
}
@misc{xiong2024iterative,
title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint},
author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang},
year={2024},
eprint={2312.11456},
archivePrefix={arXiv},
primaryClass={cs.LG}
}