README.md

---
pipeline_tag: visual-question-answering
datasets:
- HaoyeZhang/RLHF-V-Dataset
- Yirany/UniMM-Chat
---


## OmniLMM 12B

[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [Demo](http://120.92.209.146:8081/)

**OmniLMM-12B** is the most capable version of OmniLMM currently. The model is built based on EVA02-5B and Zephyr-7B-β, connected with a perceiver resampler layer, and trained on multimodal data in a curriculum fashion. The model has three notable features:

- 🔥 **Strong Performance.** 

  OmniLMM-12B achieves **leading performance** among models with comparable sizes, surpassing established LMMs on multiple benchmarks (including MME, MMBench, SEED-Bench, etc). The model also endows rich multi-modal world knowledge.

- 🏆 **Trustworthy Behavior.** 

  LMMs are known for suffering from hallucination, often generating text that is not factually grounded in images (e.g., faithfully describing non-existing objects in images). OmniLMM-12B is **the first state-of-the-art open-source LMM aligned via multimodal RLHF for trustworthy behavior** (using the recent [RLHF-V](https://rlhf-v.github.io/) technique). It **ranks #1** among open-source models on [MMHal-Bench](https://huggingface.co./datasets/Shengcao1006/MMHal-Bench), and **outperforms GPT-4V** on [Object HalBench](https://arxiv.org/abs/2312.00849).

- 🕹 **Real-time Multimodal Interaction.** 

  We combine the OmniLMM-12B and GPT-3.5 (text-only) into a **real-time multimodal interactive assistant**. The assistant accepts video streams from the camera and speech streams from the microphone and emits speech output. While still primary, we find the model can **replicate some of the fun cases shown in the Gemini Demo video, without any video edition**.


## Evaluation


<div align="center">
    <img src=https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/LuKikSY4CJiqtHocGP_xu.png width=66% />
</div>
<details>
<summary>Click to view results on MME, MMBench, MMMU, MMBench, MMHal-Bench, Object HalBench, SeedBench, LLaVA Bench W, MathVista. </summary>

<table>
<thead>
  <tr>
    <th align="left">Model</th>
    <th>Size</th>
    <th>MME</th>
    <th nowrap="nowrap">MMB dev (en)</th>
    <th nowrap="nowrap" >MMMU val</th>
    <th nowrap="nowrap" >MMHal-Bench</th>
    <th nowrap="nowrap" >Object HalBench</th>
    <th nowrap="nowrap" >SeedBench-I</th>
    <th>MathVista</th>
    <th nowrap="nowrap" >LLaVA Bench W</th>
  </tr>
</thead>
<tbody align="center">
  <tr>
    <td align="left">GPT-4V†</td>
    <td>-</td>
    <td>1409</td>
    <td>75.1 </td>
    <td>56.8</td>
    <td>3.53 / 70.8</td>
    <td>86.4 / 92.7</td>
    <td>71.6 </td>
    <td>47.8 </td>
    <td>93.1 </td>
  </tr>
  <tr>
    <td nowrap="nowrap" align="left">Qwen-VL-Plus†</td>
    <td>-</td>
    <td>1681</td>
    <td>66.2 </td>
    <td>45.2</td>
    <td>- </td>
    <td>- </td>
    <td>65.7 </td>
    <td>36.0 </td>
    <td>73.7 </td>
  </tr>
  <tr>
    <td align="left">Yi-VL 6B</td>
    <td align="right">6.7B </td>
    <td>- </td>
    <td>68.2 </td>
    <td>39.1 </td>
    <td>- </td>
    <td>- </td>
    <td>66.1 </td>
    <td>28.0 </td>
    <td>39.9 </td>
  </tr>
  <tr>
    <td nowrap="nowrap" align="left" >Qwen-VL-Chat</td>
    <td align="right">9.6B</td>
    <td>1488</td>
    <td>60.6 </td>
    <td>35.9</td>
    <td>2.93 / 59.4</td>
    <td>56.2 / 80.0</td>
    <td>64.8 </td>
    <td>33.8 </td>
    <td>67.7 </td>
  </tr>
  <tr>
    <td align="left" >CogVLM</td>
    <td align="right">17.4B</td>
    <td>1438</td>
    <td>63.7 </td>
    <td>32.1 </td>
    <td>2.68 / 52.1 </td>
    <td>73.6 / 87.4 </td>
    <td>68.8 </td>
    <td>34.7 </td>
    <td>73.9 </td>
  </tr>
  <tr>
    <td align="left" >LLaVA 1.5</td>
    <td align="right">13.6B </td>
    <td>1531 </td>
    <td>68.2 </td>
    <td>36.4 </td>
    <td>2.71 / 51.0 </td>
    <td>53.7 / 77.4 </td>
    <td>68.1 </td>
    <td>26.4 </td>
    <td>64.6 </td>
  </tr>
  <tr>
    <td nowrap="nowrap" align="left" ><b>OmniLMM-12B</b></td>
    <td align="right">11.6B </td>
    <td>1637 </td>
    <td>71.6 </td>
    <td>40.7 </td>
    <td>3.45 / 68.8 </td>
    <td>90.3 / 95.5 </td>
    <td>71.1 </td>
    <td>34.9 </td>
    <td>72.0 </td>
  </tr>
</tbody>
</table>
<small>†: Proprietary models</small>
<br>
</details>


## Demo
Click here to try out the Demo of [OmniLMM-12B](http://120.92.209.146:8081).

## Usage
Please look at [GitHub](https://github.com/OpenBMB/OmniLMM) for more detail about usage.



## License
#### Model License
* The code in this repo is released according to [Apache-2.0](https://github.com/OpenBMB/OmniLMM/blob/main/LICENSE)
* The usage of OmniLMM's parameters is subject to ["General Model License Agreement - Source Notes - Publicity Restrictions - Commercial License"](https://github.com/OpenBMB/General-Model-License/blob/main/)
* The parameters are fully open to acedemic research
* Please contact [email protected] to obtain a written authorization for commercial uses. Free commercial use is also allowed after registration.

#### Statement
* As LMMs, OmniLMM generates contents by learning a large mount of texts, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by OmniLMM does not represent the views and positions of the model developers
* We will not be liable for any problems arising from the use of the OmniLMM open Source model, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.

## Multimodal Projects of Our Team <!-- omit in toc -->

[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD)

## Citation

If you find our work helpful, please consider citing the following papers

```bib
@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{viscpm,
    title={Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages}, 
    author={Jinyi Hu and Yuan Yao and Chongyi Wang and Shan Wang and Yinxu Pan and Qianyu Chen and Tianyu Yu and Hanghao Wu and Yue Zhao and Haoye Zhang and Xu Han and Yankai Lin and Jiao Xue and Dahai Li and Zhiyuan Liu and Maosong Sun},
    journal={arXiv preprint arXiv:2308.12038},
    year={2023}
}
@article{xu2024llava-uhd,
  title={{LLaVA-UHD}: an LMM Perceiving Any Aspect Ratio and High-Resolution Images},
  author={Xu, Ruyi and Yao, Yuan and Guo, Zonghao and Cui, Junbo and Ni, Zanlin and Ge, Chunjiang and Chua, Tat-Seng and Liu, Zhiyuan and Huang, Gao},
  journal={arXiv preprint arXiv:2403.11703},
  year={2024}
}
```