---
license: apache-2.0
datasets:
- Yirany/UniMM-Chat
- HaoyeZhang/RLHF-V-Dataset
language:
- en
library_name: transformers
---

# Model Card for RLHF-V

[Project Page](https://rlhf-v.github.io/) | [GitHub ](https://github.com/RLHF-V/RLHF-V) | [Demo](http://120.92.209.146:8081/) | [Paper](https://arxiv.org/abs/2312.00849) 

RLHF-V is an open-source multimodal large language model with the **lowest hallucination rate** on both long-form instructions and short-form questions. 

RLHF-V is trained on [RLHF-V-Dataset](https://huggingface.co/datasets/HaoyeZhang/RLHF-V-Dataset), which contains **fine-grained segment-level human corrections** on diverse instructions. The base model is trained on [UniMM-Chat](https://huggingface.co/datasets/Yirany/UniMM-Chat), which is a high-quality knowledge-intensive SFT dataset. We introduce a new method **Dense Direct Preference Optimization (DDPO)** that can make better use of the fine-grained annotations. 

For more details, please refer to our [paper](https://arxiv.org/abs/2312.00849).

![Illustration of the RLHF-V frmework](https://rlhf-v.github.io/images/rlhf-v_framework.jpg)

## Model Details

### Model Description
- **Trained from model:** Based on Vicuna-13B
- **Trained on data:** [RLHF-V-Dataset](https://huggingface.co/datasets/HaoyeZhang/RLHF-V-Dataset)

### Model Sources

- **Project Page:** https://rlhf-v.github.io
- **GitHub Repository:** https://github.com/RLHF-V/RLHF-V
- **Demo:** http://120.92.209.146:8081
- **Paper:** https://arxiv.org/abs/2312.00849

## Performance

Low hallucination rate while being informative:

![fig2](https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/7xJEdKXeW33iKdHqJwvNN.png)

More resistant to over-generalization, even compared to GPT-4V:

![img](https://rlhf-v.github.io/images/over-generalization.jpg)

## Citation

If you find RLHF-V is useful in your work, please cite it with:

```
@article{2023rlhf-v,
  author      = {Tianyu Yu and Yuan Yao and Haoye Zhang and Taiwen He and Yifeng Han and Ganqu Cui and Jinyi Hu and Zhiyuan Liu and Hai-Tao Zheng and Maosong Sun and Tat-Seng Chua},
  title       = {RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback},
  journal      = {arxiv},
  year         = {2023},
}
```