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LLaVA-RLHF represents the first open-source RLHF-trained large multimodal model for general-purpose visual and language understanding, achieving impressive visual reasoning and perception capabilities. For comprehensive details and insights, we kindly direct you to our project page and paper.

To deploy or play with our model, please refer to the demo directory.

We propose a new alignment algorithm called Factually Augmented RLHF (Fact-RLHF) that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options, which alleviates the reward hacking phenomenon in RLHF and further improves the performance.

LLaVA-RLHF is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps accordingly. Always keep the global batch size the same: per_device_train_batch_size x gradient_accumulation_steps x num_gpus.

The SFT training pipeline is provided in the SFT directory, and the RLHF training pipeline is provided in the RLHF directory.

If you find this repo useful for your research, please consider citing the paper

LLaVA-RLHF:

@article{sun2023aligning,
  title={Aligning large multimodal models with factually augmented rlhf},
  author={Sun, Zhiqing and Shen, Sheng and Cao, Shengcao and Liu, Haotian and Li, Chunyuan and Shen, Yikang and Gan, Chuang and Gui, Liang-Yan and Wang, Yu-Xiong and Yang, Yiming and others},
  journal={arXiv preprint arXiv:2309.14525},
  year={2023}
}

LLaVA:

@misc{liu2023llava,
      title={Visual Instruction Tuning},
      author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
      publisher={arXiv:2304.08485},
      year={2023},
}

SALMON:

@article{sun2023salmon,
  title={SALMON: Self-Alignment with Principle-Following Reward Models},
  author={Sun, Zhiqing and Shen, Yikang and Zhang, Hongxin and Zhou, Qinhong and Chen, Zhenfang and Cox, David and Yang, Yiming and Gan, Chuang},
  journal={arXiv preprint arXiv:2310.05910},
  year={2023}
}

We thank Meta LLaMA team, Standford Alpaca team, Vicuna team, LLaVA team, QLoRA team, Hugging Face PEFT, and AlpacaFarm team for their open-source efforts in democratizing large language models.