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UMDdawn/
baseline-RLAIF-V-llava-7B

Name: baseline-RLAIF-V-llava-7B
Availability: InStock

safetensors

llava_llama

openbmb/RLAIF-V-Dataset

2405.17220

2312.00849

apache-2.0

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Model Card for RLAIF-V

GitHub | Paper

RLAIF-V-7B is trained based on LLaVA 1.5 7B with the novel RLAIF-V framework. By aligning with human preference via large scale AI feedback, the model achieves super GPT-4V trustworthiness. RLAIF-V maximally exploits the open-source feedback from two key perspectives, including high-quality feedback data and an online feedback learning algorithm.

Model Details

Key Features

📈 Most trustworthy LLaVA 1.5: By learning from open-source AI feedback, specifically, the feedback from LLaVA-NeXT-34B, RLAIF-V-7B achieves the best trustworthiness improvement on LLaVA-v1.5 compared to other hallucination reduction methods.
💪 Maintaining Well Performance on General Abilities: On benchmarks evaluating general capabilities (e.g. LLaVA Bench, MMStar), RLAIF-V-7B also exhibits good performance.

Examples

Model Description

Trained from model: llava-v1.5-7B
Trained on data: RLAIF-V-Dataset

Usage

Please look at GitHub for more details about usage.

Citation

If you find our model/code/paper helpful, please consider cite our papers 📝:

bibtex

@article{yu2023rlhf,
  title={Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback},
  author={Yu, Tianyu and Yao, Yuan and Zhang, Haoye and He, Taiwen and Han, Yifeng and Cui, Ganqu and Hu, Jinyi and Liu, Zhiyuan and Zheng, Hai-Tao and Sun, Maosong and others},
  journal={arXiv preprint arXiv:2312.00849},
  year={2023}
}

@article{yu2024rlaifv,
  title={RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness}, 
  author={Yu, Tianyu and Zhang, Haoye and Yao, Yuan and Dang, Yunkai and Chen, Da and Lu, Xiaoman and Cui, Ganqu and He, Taiwen and Liu, Zhiyuan and Chua, Tat-Seng and Sun, Maosong},
  journal={arXiv preprint arXiv:2405.17220},
  year={2024},
}

UMDdawn/baseline-RLAIF-V-llava-7B