--- license: apache-2.0 datasets: - openbmb/RLAIF-V-Dataset language: - en --- # Model Card for RLAIF-V [GitHub ](https://github.com/RLHF-V/RLAIF-V) | [Paper](https://arxiv.org/abs/2405.17220) **RLAIF-V-7B** is trained based on LLaVA 1.5 7B with the novel [RLAIF-V](https://github.com/RLHF-V/RLAIF-V) framework. By aligning with human preference via large scale [AI feedback](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset), 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](https://huggingface.co/liuhaotian/llava-v1.5-7b) - **Trained on data:** [RLAIF-V-Dataset](https://huggingface.co/datasets/HaoyeZhang/RLAIF-V-Dataset) ## Usage Please look at [GitHub](https://github.com/RLHF-V/RLAIF-V) 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}, } ```