Reward model for HH-RLHF

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In this repo, we present a reward model trained by the framework LMFlow. The reward model is for the HH-RLHF dataset, and is trained from the base model GPT-Neo-2.7B.

It achieves ~69 evaluation accuracy on the validation set. You may also check the reward model based on Open-llama-3b.

Reference

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If you found this model useful, please cite our framework and paper using the following BibTeX:

@article{diao2023lmflow,
  title={Lmflow: An extensible toolkit for finetuning and inference of large foundation models},
  author={Diao, Shizhe and Pan, Rui and Dong, Hanze and Shum, Ka Shun and Zhang, Jipeng and Xiong, Wei and Zhang, Tong},
  journal={arXiv preprint arXiv:2306.12420},
  year={2023}
}

@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}
}