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LLaMA3-iterative-DPO-final

Introduction

We release an unofficial checkpoint of a state-of-the-art instruct model of its class, LLaMA3-iterative-DPO-final. On all three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, Chat-Arena-Hard, our model outperforms all models of similar size (e.g., LLaMA-3-8B-it), most large open-sourced models (e.g., Mixtral-8x7B-it), and strong proprietary models (e.g., GPT-3.5-turbo-0613). The model is trained with open-sourced datasets without any additional human-/GPT4-labeling.

Even better, we provide a detailed recipe to reproduce the model. Enjoy!

Model Releases

See the collection of the training set, reward/preference model, SFT model.

  • SFT model
  • Reward model
  • This model is more like the concise version in the report. We are still working on the model realeasing due to some license issue....

Dataset

Training methods

We have developed a simple and efficient online RLHF recipe for LLM instruct training. Our recipe is DPO-based and thus much cheaper and simpler to train and tune compared to PPO-based approaches. Unlike widely-used offline DPO, the online component of our approach effectively mitigates distribution shifts during policy optimization. For a detailed exposition, please refer to our accompanying technical report.

Chat Benchmarks

Model Size Method LC Alpaca-Eval-V2 MT-Bench Chat-Arena-Hard
Small Open-Sourced Models
Gemma-7B-it 7B SFT 10.4 6.38 7.5
Zephyr-7B-beta 7B Vanilla DPO 13.1 7.34 -
Mistral-7B-v0.2-it 7B SFT 17.1 7.51 12.6
Open-Chat-0106 7B SFT 15.6 7.8 -
Starling-7B-beta 7B PPO 25.8 8.12 23.0
LLaMA-3-8B-it 8B RS+DPO+PPO 22.9 8.16 20.6
Ours
Ours (SFT baseline) 8B SFT 10.2 7.69 5.6
Ours (DPO baseline) 8B Vanilla DPO 22.5 8.17 22.4
Ours (Online RLHF) 8B Iterative DPO 37.2 8.46 29.1
Large Open-Sourced Models
Vicuna-33b-v1.3 33B SFT 17.6 7.12 8.6
Yi-34B-Chat 34B SFT 27.2 - 23.1
Mixtral-8x7B-it 45B* SFT 23.7 8.30 23.4
Tulu-2-DPO-70B 70B Vanilla DPO 21.2 7.89 15.0
LLaMA-3-70B-it 70B RS+DPO+PPO 34.4 8.95 41.1
Mixtral-8x22B-it 141B* SFT 30.9 8.66 36.4
Proprietary Models
GPT-3.5-turbo-1106 - - 19.3 8.35 18.9
GPT-3.5-turbo-0613 - - 22.7 8.39 24.8
GPT-4-0613 - - 30.2 9.18 37.9
Claude-3-Opus - - 40.5 9.00 60.4
GPT-4 Turbo (04/09) - - 55.0 - 82.6

Academic Benchmarks

Model Size Method GSM-8K MMLU HumanEval TruthfulQA ARC MBPP
LLaMA-3-8B-it 8B RS+DPO+PPO 79.6 66.0 61.6 43.9 59.5 61.1
Ours (SFT baseline) 8B SFT 74.2 64.7 65.2 53.4 61.4 62.3
Ours (DPO baseline) 8B Vanilla DPO 79.8 64.5 63.4 61.8 65.2 60.3
Ours (Iterative RLHF) 8B Iterative DPO 80.7 65.3 64.6 60.4 64.3 60.8

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" 

model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final")
tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final")

messages = [
    {"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"},
]

model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = model_inputs.to(device)
model.to(device)

output_tokens = model.generate(model_inputs, max_new_tokens=1024, do_sample=True)
model_outputs = tokenizer.batch_decode(output_tokens)
print(model_outputs[0])

Limitations

RLHFlow/LLaMA3-iterative-DPO-final is an unofficial checkpoint developed to illustrate the power of online iterative RLHF and is for research purpose. While safety and ethical considerations are integral to our alignment process, there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions. We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage.

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}
}

@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}
}
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