RLHFlow/LLaMA3-SFT

Model

10

stars

9

commits

2

repos using this model

1

linked in READMEs

Nov 3, 2024

updated

conversational
endpoints_compatible
llama
safetensors
text-generation
text-generation-inference
transformers

README

This is the SFT checkpoint used for the project RLHFlow/Online-RLHF

The model is trained from meta-llama/Meta-Llama-3-8B on a mixture of diverse open-source high-quality data for 1 epoch with detailed parameters in the report. It has not been trained by RLHF and can serve as a good starting point for the RLHF research.

Academic Benchmarks

We use ToRA script to evaluate GSM8K and MATH, Evalplut for HumanEval, and lm-evaluation-harness for other benchmarks. The model is evaluated in zero-shot setting so the results here may be slightly different from that reported in the technical report.

ModelSizeMethodLC AlpacaEvalMT-BenchGSM-8KMMLUHumanEvalTruthfulQAARCMBPP
LLaMA-3-8B-it8BRS+DPO+PPO22.98.1679.666.061.643.959.561.1
Ours (SFT baseline)8BSFT10.27.6974.230.064.663.453.558.6
Ours (Iterative RLHF)8BIterative DPO37.28.4680.765.364.660.464.360.8

Citation

Please cite our techical report if you find our model is useful for your research or product.

@misc{dong2024rlhf,
      title={RLHF Workflow: From Reward Modeling to Online RLHF}, 
      author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang},
      year={2024},
      eprint={2405.07863},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contributors

weqweasdas

7 commits

Haoxiang-Wang

2 commits

RLHFlow/LLaMA3-SFT

Model

10

stars

9

commits

2

repos using this model

1

linked in READMEs

Nov 3, 2024

updated

conversational
endpoints_compatible
llama
safetensors
text-generation
text-generation-inference
transformers

README

This is the SFT checkpoint used for the project RLHFlow/Online-RLHF

The model is trained from meta-llama/Meta-Llama-3-8B on a mixture of diverse open-source high-quality data for 1 epoch with detailed parameters in the report. It has not been trained by RLHF and can serve as a good starting point for the RLHF research.

Academic Benchmarks

We use ToRA script to evaluate GSM8K and MATH, Evalplut for HumanEval, and lm-evaluation-harness for other benchmarks. The model is evaluated in zero-shot setting so the results here may be slightly different from that reported in the technical report.

ModelSizeMethodLC AlpacaEvalMT-BenchGSM-8KMMLUHumanEvalTruthfulQAARCMBPP
LLaMA-3-8B-it8BRS+DPO+PPO22.98.1679.666.061.643.959.561.1
Ours (SFT baseline)8BSFT10.27.6974.230.064.663.453.558.6
Ours (Iterative RLHF)8BIterative DPO37.28.4680.765.364.660.464.360.8

Citation

Please cite our techical report if you find our model is useful for your research or product.

@misc{dong2024rlhf,
      title={RLHF Workflow: From Reward Modeling to Online RLHF}, 
      author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang},
      year={2024},
      eprint={2405.07863},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contributors

weqweasdas

7 commits

Haoxiang-Wang

2 commits