---
license: apache-2.0
language:
- en
- zh
tags:
- multimodal
library_name: transformers
datasets:
- BAAI/Infinity-MM
- BAAI/Infinity-Instruct
- BAAI/Infinity-Preference
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
- google/siglip-so400m-patch14-384
pipeline_tag: image-text-to-text
---


# Introduction

The **Aquila-VL-2B** model is a vision-language model (VLM) trained based on the [LLava-one-vision](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/) framework. The [Qwen2.5-1.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model is chose as the LLM, while [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) is utilized as the vision tower.

The model was trained on our self-built Infinity-MM dataset, which contains approximately 40 million image-text pairs. This dataset is a combination of open-source data collected from the internet and synthetic instruction data generated using open-source VLM models.


We have open-sourced [Infinity-MM](https://huggingface.co/datasets/BAAI/Infinity-MM) dataset and related resources. We hope you enjoy using them!

## News 
- `2024/10/25`:  The [Aquila-VL-2B](https://huggingface.co/BAAI/Aquila-VL-2B-llava-qwen) model and [Infinity-MM](https://huggingface.co/datasets/BAAI/Infinity-MM) dataset are now available.  We have also released the [technical report](https://arxiv.org/abs/2410.18558) simultaneously.

# Evaluation

We evaluated the model using the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) tool. Whenever possible, we prioritized using the OpenAI API for test sets that support API-based evaluation.

| Benchmark                    | MiniCPM-V-2 | InternVL2-2B | XinYuan-VL-2B | Qwen2-VL-2B-Instruct | Aquila-VL-2B |
| :--------------------------- | :---------: | :----------: | :-----------: | :------------------: | :----------: |
| MMBench-EN<sub>test</sub>    |    69.4     |     73.4     |   **78.9**    |         74.9         |     78.8     |
| MMBench-CN<sub>test</sub>    |    65.9     |     70.9     |     76.1      |         73.9         |   **76.4**   |
| MMBench_V1.1<sub>test</sub>  |    65.2     |     69.7     |   **75.4**    |         72.7         |     75.2     |
| MMT-Bench<sub>test</sub>     |    54.5     |     53.3     |     57.2      |         54.8         |   **58.2**   |
| RealWorldQA                  |    55.4     |     57.3     |     63.9      |         62.6         |   **63.9**   |
| HallusionBench               |    36.8     |     38.1     |     36.0      |         41.5         |   **43.0**   |
| SEEDBench2<sub>plus</sub>    |    51.8     |     60.0     |     63.0      |         62.4         |   **63.0**   |
| LLaVABench                   |    66.1     |     64.8     |     42.4      |         52.5         |   **68.4**   |
| MMStar                       |    41.6     |     50.2     |     51.9      |         47.8         |   **54.9**   |
| POPE                         |    86.6     |     85.3     |   **89.4**    |         88.0         |     83.6     |
| MMVet                        |    44.0     |     41.1     |     42.7      |       **50.7**       |     44.3     |
| MMMU<sub>val</sub>           |    39.6     |     34.9     |     43.6      |         41.7         |   **47.4**   |
| ScienceQA<sub>test</sub>     |    80.4     |     94.1     |     86.6      |         78.1         |   **95.2**   |
| AI2D<sub>test</sub>          |    64.8     |     74.4     |     74.2      |         74.6         |   **75.0**   |
| MathVista<sub>testmini</sub> |    39.0     |     45.0     |     47.1      |         47.9         |   **59.0**   |
| MathVerse<sub>testmini</sub> |    19.8     |     24.7     |     22.2      |         21.0         |   **26.2**   |
| MathVision                   |    15.4     |     12.6     |     16.3      |         17.5         |   **18.4**   |
| DocVQA<sub>test</sub>        |    71.0     |     86.9     |     87.6      |       **89.9**       |     85.0     |
| InfoVQA<sub>test</sub>       |    40.0     |     59.5     |     59.1      |       **65.4**       |     58.3     |
| ChartQA<sub>test</sub>       |    59.6     |     71.4     |     57.1      |         73.5         |   **76.5**   |
| TextVQA<sub>val</sub>        |    74.3     |     73.5     |     77.6      |       **79.9**       |     76.4     |
| OCRVQA<sub>testcore</sub>    |    54.4     |     40.2     |     67.6      |       **68.7**       |     64.0     |
| VCR<sub>en easy</sub>        |    27.6     |     51.6     |     67.7      |         68.3         |   **70.0**   |
| OCRBench                     |     613     |     784      |      782      |       **810**        |     772      |
| Average                      |    53.5     |     58.8     |     60.9      |         62.1         |   **64.1**   |



For comparison models, evaluations were conducted in a local environment, so the scores may differ slightly from those reported in papers or on the official VLMEvalKit leaderboard.

# Future Plan

* We plan to train models of various sizes.
* Future training will incorporate multi-image and video data.


## **Citation**
If you find this dataset useful, please cite the following work
```
@misc{gu2024infinitymmscalingmultimodalperformance,
      title={Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data}, 
      author={Shuhao Gu and Jialing Zhang and Siyuan Zhou and Kevin Yu and Zhaohu Xing and Liangdong Wang and Zhou Cao and Jintao Jia and Zhuoyi Zhang and Yixuan Wang and Zhenchong Hu and Bo-Wen Zhang and Jijie Li and Dong Liang and Yingli Zhao and Yulong Ao and Yaoqi Liu and Fangxiang Feng and Guang Liu},
      year={2024},
      eprint={2410.18558},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.18558}, 
}


# Disclaimer
The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced the model is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.[]()