InternVL-Data

# InternVL-Data

[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/papers/2411.10442) [\[📜 InternVL3\]](https://huggingface.co/papers/2504.10479)

[\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/)

## Introduction

Welcome to the InternVL3 Open Dataset! This dataset is designed to support research and development in the field of multimodal large language models (MLLMs), specifically for tasks involving image, text, and video understanding. The dataset is composed of data collected from various sources, including curated open-source datasets, self-synthesized datasets, and data gathered from the internet.

Our first phase plan is to release the SFT data for InternVL2.5 and InternVL3. We will continue uploading the data over the next two to four weeks, starting with the SFT data for InternVL2.5, followed by the SFT data for InternVL3. Once the data upload is complete, we will release the data distribution, detailing the proportion each dataset represents within the overall dataset. We kindly ask for your patience as we continue to release the data in the coming weeks.

## Data List

### InternVL2.5-SFT

TODO

### InternVL3-SFT

TODO

## License

This dataset is released under the CC BY 4.0 License.

## Citation

If you find this project useful in your research, please consider citing:

```BibTeX
@article{zhu2025internvl3,
title={InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models},
author={Zhu, Jinguo and Wang, Weiyun and Chen, Zhe and Liu, Zhaoyang and Ye, Shenglong and Gu, Lixin and Duan, Yuchen and Tian, Hao and Su, Weijie and Shao, Jie and others},
journal={arXiv preprint arXiv:2504.10479},
year={2025}
}
@article{chen2024expanding,
title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
journal={arXiv preprint arXiv:2412.05271},
year={2024}
}
@article{chen2024far,
title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
journal={arXiv preprint arXiv:2404.16821},
year={2024}
}
@inproceedings{chen2024internvl,
title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={24185--24198},
year={2024}
}
```

# InternVL数据集

[📂 GitHub仓库](https://github.com/OpenGVLab/InternVL) [📜 InternVL 1.0](https://huggingface.co/papers/2312.14238) [📜 InternVL 1.5](https://huggingface.co/papers/2404.16821) [📜 InternVL 2.5](https://huggingface.co/papers/2412.05271) [📜 InternVL2.5-MPO](https://huggingface.co/papers/2411.10442) [📜 InternVL3](https://huggingface.co/papers/2504.10479)

[🆕 官方博客](https://internvl.github.io/blog/) [🗨️ 对话演示](https://internvl.opengvlab.com/) [🤗 Hugging Face演示](https://huggingface.co/spaces/OpenGVLab/InternVL) [🚀 快速上手](#quick-start) [📖 官方文档](https://internvl.readthedocs.io/en/latest/)

## 简介

欢迎使用InternVL3开源数据集！本数据集旨在为多模态大语言模型（multimodal large language models, MLLMs）领域的研究与开发提供支撑，尤其适配图像、文本与视频理解类任务。本数据集的数据来源涵盖精选开源数据集、自主合成数据集以及互联网采集数据。

我们的第一阶段计划将发布InternVL2.5与InternVL3的监督微调（Supervised Fine-Tuning, SFT）数据。我们将在未来2至4周内持续推进数据上传工作，优先发布InternVL2.5的监督微调数据，随后发布InternVL3的监督微调数据。待全部数据上传完毕后，我们将公布数据集分布详情，逐一说明各子集在总数据集中的占比。我们恳请各位用户在后续数据发布周期内给予理解与耐心。

## 数据列表

### InternVL2.5-SFT
TODO

### InternVL3-SFT
TODO

## 开源协议

本数据集采用CC BY 4.0开源协议发布。

## 引用说明

若本项目对您的研究有所助益，请引用以下文献：
BibTeX
@article{zhu2025internvl3,
title={InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models},
author={Zhu, Jinguo and Wang, Weiyun and Chen, Zhe and Liu, Zhaoyang and Ye, Shenglong and Gu, Lixin and Duan, Yuchen and Tian, Hao and Su, Weijie and Shao, Jie and others},
journal={arXiv preprint arXiv:2504.10479},
year={2025}
}
@article{chen2024expanding,
title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
journal={arXiv preprint arXiv:2412.05271},
year={2024}
}
@article{chen2024far,
title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
journal={arXiv preprint arXiv:2404.16821},
year={2024}
}
@inproceedings{chen2024internvl,
title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={24185--24198},
year={2024}
}