Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
+language:
+  - multilingual
+tags:
+  - internvl
+  - vision
+  - ocr
+  - multi-image
+  - video
+  - custom_code
+---
+
+# InternVL2-Pretrain-Models
+
+[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL)  [\[🆕 Blog\]](https://internvl.github.io/blog/)  [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238)  [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821)
+
+[\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/)  [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#quick-start)  [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971)  [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/)
+
+<div align="center">
+  <img width="500" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/0g_jb7zCVHc77sDpK8L2i.png">
+  <br>
+</div>
+
+## About This Repository
+
+This repository hosts the pre-trained models of InternVL 2.0, specifically from the Stage-1 pre-training phase. During this phase, the models are trained to align multimodal inputs—including text, images, and videos—establishing the foundational capabilities necessary for the subsequent instruction-tuning stage.
+
+## Introduction
+
+We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of **instruction-tuned models**, ranging from 1 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-1B model.
+
+Compared to the state-of-the-art open-source multimodal large language models, InternVL 2.0 surpasses most open-source models. It demonstrates competitive performance on par with proprietary commercial models across various capabilities, including document and chart comprehension, infographics QA, scene text understanding and OCR tasks, scientific and mathematical problem solving, as well as cultural understanding and integrated multimodal capabilities.
+
+InternVL 2.0 is trained with an 8k context window and utilizes training data consisting of long texts, multiple images, and videos, significantly improving its ability to handle these types of inputs compared to InternVL 1.5. For more details, please refer to our [blog](https://internvl.github.io/blog/2024-07-02-InternVL-2.0/) and [GitHub](https://github.com/OpenGVLab/InternVL).
+
+|      Model Name      |                                     Vision Part                                     |                                        Language Part                                         |                             HF Link                              |                                MS Link                                 |
+| :------------------: | :---------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------: | :--------------------------------------------------------------------: |
+|     InternVL2-1B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |            [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)            |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-1B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-1B)     |
+|     InternVL2-2B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |          [internlm2-chat-1_8b](https://huggingface.co/internlm/internlm2-chat-1_8b)          |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-2B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-2B)     |
+|     InternVL2-4B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |    [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)     |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-4B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-4B)     |
+|     InternVL2-8B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |          [internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-7b-chat)          |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-8B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-8B)     |
+|    InternVL2-26B     | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) |           [internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b)           |    [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-26B)     |    [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-26B)     |
+|    InternVL2-40B     | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) |       [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)       |    [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-40B)     |    [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-40B)     |
+| InternVL2-Llama3-76B | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) | [Hermes-2-Theta-Llama-3-70B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B) | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B) | [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-Llama3-76B) |
+
+## License
+
+This project is released under the MIT license.
+
+## Citation
+
+If you find this project useful in your research, please consider citing:
+
+```BibTeX
+@article{chen2023internvl,
+  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 Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
+  journal={arXiv preprint arXiv:2312.14238},
+  year={2023}
+}
+@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}
+}
+```