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 framework. The Qwen2.5-1.5B-instruct model is chose as the LLM, while 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 dataset and related resources. We hope you enjoy using them!
News
2024/10/25: The Aquila-VL-2B model and Infinity-MM dataset are now available. We have also released the technical report simultaneously.
Evaluation
We evaluated the model using the 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-ENtest | 69.4 | 73.4 | 78.9 | 74.9 | 78.8 |
| MMBench-CNtest | 65.9 | 70.9 | 76.1 | 73.9 | 76.4 |
| MMBench_V1.1test | 65.2 | 69.7 | 75.4 | 72.7 | 75.2 |
| MMT-Benchtest | 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 |
| SEEDBench2plus | 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 |
| MMMUval | 39.6 | 34.9 | 43.6 | 41.7 | 47.4 |
| ScienceQAtest | 80.4 | 94.1 | 86.6 | 78.1 | 95.2 |
| AI2Dtest | 64.8 | 74.4 | 74.2 | 74.6 | 75.0 |
| MathVistatestmini | 39.0 | 45.0 | 47.1 | 47.9 | 59.0 |
| MathVersetestmini | 19.8 | 24.7 | 22.2 | 21.0 | 26.2 |
| MathVision | 15.4 | 12.6 | 16.3 | 17.5 | 18.4 |
| DocVQAtest | 71.0 | 86.9 | 87.6 | 89.9 | 85.0 |
| InfoVQAtest | 40.0 | 59.5 | 59.1 | 65.4 | 58.3 |
| ChartQAtest | 59.6 | 71.4 | 57.1 | 73.5 | 76.5 |
| TextVQAval | 74.3 | 73.5 | 77.6 | 79.9 | 76.4 |
| OCRVQAtestcore | 54.4 | 40.2 | 67.6 | 68.7 | 64.0 |
| VCRen easy | 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.
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.