InternVL2_5-Pretrain-Models

[📂 GitHub] [📜 InternVL 1.0] [📜 InternVL 1.5] [📜 Mini-InternVL] [📜 InternVL 2.5]

[🆕 Blog] [🗨️ Chat Demo] [🤗 HF Demo] [🚀 Quick Start] [📖 Documents]

About This Repository

This repository hosts InternVL 2.5's pre-trained models from the Stage-1 or Stage-1.5 pre-training phase. For models that have undergone Stage-1.5—such as InternVL2.5-8B and InternVL2.5-26B—the released weights are from Stage-1.5, while all other models use Stage-1 weights.

Introduction

We are excited to introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality.

InternVL 2.5 Family

In the following table, we provide an overview of the InternVL 2.5 series.

Model Name	Vision Part	Language Part	HF Link
InternVL2_5-1B	InternViT-300M-448px-V2_5	Qwen2.5-0.5B-Instruct	🤗 link
InternVL2_5-2B	InternViT-300M-448px-V2_5	internlm2_5-1_8b-chat	🤗 link
InternVL2_5-4B	InternViT-300M-448px-V2_5	Qwen2.5-3B-Instruct	🤗 link
InternVL2_5-8B	InternViT-300M-448px-V2_5	internlm2_5-7b-chat	🤗 link
InternVL2_5-26B	InternViT-6B-448px-V2_5	internlm2_5-20b-chat	🤗 link
InternVL2_5-38B	InternViT-6B-448px-V2_5	Qwen2.5-32B-Instruct	🤗 link
InternVL2_5-72B	InternViT-300M-448px-V2_5	Qwen2.5-72B-Instruct	-
InternVL2_5-78B	InternViT-6B-448px-V2_5	Qwen2.5-72B-Instruct	🤗 link

License

This project is released under the MIT license.

Citation

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

@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{gao2024mini,
  title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
  author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
  journal={arXiv preprint arXiv:2410.16261},
  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}
}