Text Generation
Transformers
Safetensors
English
omnilmm
conversational
Inference Endpoints
File size: 3,127 Bytes
6f9efaf
 
 
0e6260c
6f9efaf
 
28ffeb7
6f9efaf
 
 
 
28ffeb7
6f9efaf
e0c7f4a
0e6260c
e0c7f4a
6f9efaf
23d9724
dc43a94
23d9724
6f9efaf
 
 
fe64c14
0e6260c
e0c7f4a
 
0e6260c
 
34adf95
0e6260c
 
 
 
 
d39cc4e
2235170
0e6260c
 
6f9efaf
0e6260c
23d9724
 
 
8b5c19b
 
 
 
 
 
a006514
8b5c19b
 
a006514
 
 
 
 
 
 
8b5c19b
a006514
 
 
 
8b5c19b
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
---
license: apache-2.0
datasets:
- openbmb/RLAIF-V-Dataset
language:
- en
paper:
---

# Model Card for RLAIF-V

[GitHub ](https://github.com/RLHF-V/RLAIF-V) | [Paper](https://arxiv.org/abs/2405.17220)

**RLAIF-V-12B** is a multimodal large language model (MLLM) that exhibits **super GPT-4V trustworthiness**. The model is built up on OmniLMM from the [MiniCPM-V](https://github.com/OpenBMB/MiniCPM-V) series. 

We utilize a novel framework, [RLAIF-V](https://github.com/RLHF-V/RLAIF-V), which **aligns MLLMs in a fully open-source paradigm**. This framework maximally exploits the [open-source feedback](https://huggingface.co/datasets/HaoyeZhang/RLAIF-V-Dataset) from two key perspectives, including **high-quality feedback data** and an **online feedback learning algorithm**. 

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/T4hALrgNdXKHnkvb-27bA.png" alt="fig1" width="85%"/>
</p>

## Model Details

### Key Features

* 🏅 **Super GPT-4V Trustworthiness**: By learning from open-source AI feedback, RLAIF-V-12B achieves super GPT-4V trustworthiness in both generative and discriminative tasks.
* 💪 **Maintaining Well Performance on General Abilities**: On benchmarks tested with the general abilities (e.g. LLaVA Bench, MMStar), RLAIF-V-12B also exhibits good performance.

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/ypXZxb4HE-jDPJU9115bi.png" alt="fig1" width="90%"/>
</p>
<!-- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/ypXZxb4HE-jDPJU9115bi.png) -->

### Examples
<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/yg-Ksp9qi8AodURSmX769.png" alt="fig2-1" width="81%"/>
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6566e0c493e30c8a60048eb3/NSEkeBmH99B44rX8GTZig.png" alt="fig2-1" width="80%"/>
</p>

### Model Description
- **Related model:** [OmniLMM-12B](https://huggingface.co/openbmb/OmniLMM-12B)
- **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},
}
```