---
library_name: transformers
tags: []
---

This is the SFT checkpoint used for the project [RLHFlow/Online-RLHF](https://github.com/RLHFlow/Online-RLHF)

* **Paper**: [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/pdf/2405.07863) (Published in TMLR, 2024)
* **Authors**: Hanze Dong*, Wei Xiong*, Bo Pang*, Haoxiang Wang*, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
* **Code**: https://github.com/RLHFlow/Online-RLHF 

The model is trained from [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [RLHFlow/RLHFlow-SFT-Dataset-ver2](https://huggingface.co/datasets/RLHFlow/RLHFlow-SFT-Dataset-ver2) for 2 epochs. We use a global batch size of 128 and a learning rate of 2e-5, where we pack the samples and split them into chunks of 8192 token. See more training details at https://github.com/RLHFlow/Online-RLHF/blob/main/sft/llama3-8b-it.yaml .


## 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.

| **Model**                  | **Size** | **Method**      | **LC AlpacaEval** | **MT-Bench** | **GSM-8K** | **MATH** | **MMLU** | **HumanEval** | **TruthfulQA** | **ARC**  |
|----------------------------|----------|-----------------|------------|------------|------------|----------|---------------|----------------|---------|----------|
| LLaMA-3-8B-it              | 8B       | RS+DPO+PPO      |22.9|8.16| 79.6 |   26.3    | 66.0     | 61.6          | 43.9           | 59.5    | 
|  RLHFlow/LLaMA3-SFT       | 8B       | SFT             |10.2|7.69| 74.2  |   30.0  | 64.6     | 63.4          | 53.5           | 58.6    | 
| RLHFlow/LLaMA3-SFT-v2     | 8B       | SFT   |12.66|-| 83.4 | 41.1      | 64.8     | 66.5          | 53.9           | 60.0    | 



## 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}
}

```