Clean code

Author: czczup

README.md

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 # <img width="60" alt="image" src="https://github.com/OpenGVLab/InternVL/assets/47669167/7037290e-f474-4d11-b90f-1d8316087bf8"> InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-4o
 
-[\[🔥 Mini-InternVL\]](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat/shell/mini_internvl) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🤔 FAQs\]](https://internvl.readthedocs.io/en/latest/tutorials/faqs.html)  [\[🚀 InternVL2 Blog\]](https://internvl.github.io/blog/2024-07-02-InternVL-2.0/)  [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/)  [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[📖 Document\]](https://internvl.readthedocs.io/en/latest/)  [\[🌐 API\]](https://internvl.readthedocs.io/en/latest/get_started/internvl_chat_api.html)  [\[🚀 Quick Start\]](#quick-start-with-huggingface)
+<div align="center">
+  <img width="500" alt="image" src="https://github.com/user-attachments/assets/930e6814-8a9f-43e1-a284-118a5732daa4">
+  <br>
+</div>
+
+[\[🆕 Blog\]](https://internvl.github.io/blog/)  [\[🤔 FAQs\]](https://internvl.readthedocs.io/en/latest/tutorials/faqs.html)  [\[🚀 InternVL2 Blog\]](https://internvl.github.io/blog/2024-07-02-InternVL-2.0/)  [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/)  [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[📖 Document\]](https://internvl.readthedocs.io/en/latest/)  [\[🌐 API\]](https://internvl.readthedocs.io/en/latest/get_started/internvl_chat_api.html)  [\[🚀 Quick Start\]](#quick-start-with-huggingface)
 
-[\[🔥 Mini-InternVL Report\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238)   [\[📖 2.0 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[📖 1.5 中文解读\]](https://zhuanlan.zhihu.com/p/699439759)    [\[📖 1.0 中文解读\]](https://zhuanlan.zhihu.com/p/702946079)  
+[\[🔥 Mini-InternVL Report\]](https://arxiv.org/abs/2410.16261)  [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821)  [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238)
+
+[\[📖 2.0 中文解读\]](https://zhuanlan.zhihu.com/p/706547971)  [\[📖 1.5 中文解读\]](https://zhuanlan.zhihu.com/p/699439759)  [\[📖 1.0 中文解读\]](https://zhuanlan.zhihu.com/p/702946079)
 
 [Switch to the Chinese version (切换至中文版)](/README_zh.md)
 
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 </div>
 
 ## News 🚀🚀🚀
-- `2024/10/21`: We release the Mini-InternVL series, which includes three chat models: __Mini-InternVL-1B__,  __Mini-InternVL-2B__ and __Mini-InternVL-4B__. These models achieve impressive performance with minimal size: the 4B model achieves 90% of the performance with just 5% of the model size. For more details, please check our [Project page](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat/shell/mini_internvl) and [Document](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html).
+
+- `2024/10/21`: We release the Mini-InternVL series, which includes three chat models: __Mini-InternVL-1B__,  __Mini-InternVL-2B__ and __Mini-InternVL-4B__. These models achieve impressive performance with minimal size: the 4B model achieves 90% of the performance with just 5% of the model size. For more details, please check our [project page](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat/shell/mini_internvl) and [document](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html).
 - `2024/08/01`: The [Chartmimic](https://chartmimic.github.io/) team evaluated the InternVL2 series models on their benchmark. The InternVL2-26B and 76B models achieved the top two performances among open-source models, with the InternVL2 76B model surpassing GeminiProVision and exhibiting comparable results to Claude-3-opus.
 - `2024/08/01`: InternVL2-Pro achieved the SOTA performance among open-source models on the [CharXiv](https://charxiv.github.io/#leaderboard) dataset, surpassing some well-known closed-source models such as GPT-4V, Gemini 1.5 Flash, and Claude 3 Sonnet.
 - `2024/07/24`: The [MLVU](https://github.com/JUNJIE99/MLVU) team evaluated InternVL-1.5 on their benchmark. The average performance on the multiple-choice task was 50.4%, while the performance on the generative tasks was 4.02. The performance on the multiple-choice task ranked #1 among all open-source MLLMs.

README_zh.md

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 # <img width="60" alt="image" src="https://github.com/OpenGVLab/InternVL/assets/47669167/7037290e-f474-4d11-b90f-1d8316087bf8"> InternVL家族：通过开源组件缩小与商业多模态模型的差距 —— GPT-4o的开源替代方案
 
+<div align="center">
+  <img width="500" alt="image" src="https://github.com/user-attachments/assets/930e6814-8a9f-43e1-a284-118a5732daa4">
+  <br>
+</div>
+
 [\[🆕 博客\]](https://internvl.github.io/blog/) [\[🤔 常见问题\]](https://internvl.readthedocs.io/en/latest/tutorials/faqs.html) [\[🚀 InternVL2 博客\]](https://internvl.github.io/blog/2024-07-02-InternVL-2.0/)  [\[🗨️ 对话Demo\]](https://internvl.opengvlab.com/)  [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[📖 文档\]](https://internvl.readthedocs.io/en/latest/)  [\[🌐 API\]](https://internvl.readthedocs.io/en/latest/get_started/internvl_chat_api.html)  [\[🚀 快速开始\]](#使用-huggingface-快速开始)
 
-[\[📜 InternVL 1.0 论文\]](https://arxiv.org/abs/2312.14238)  [\[📜 InternVL 1.5 报告\]](https://arxiv.org/abs/2404.16821)  [\[📖 1.0 中文解读\]](https://zhuanlan.zhihu.com/p/702946079)  [\[📖 1.5 中文解读\]](https://zhuanlan.zhihu.com/p/699439759)  [\[📖 2.0 中文解读\]](https://zhuanlan.zhihu.com/p/706547971)
+[\[🔥 Mini-InternVL 报告\]](https://arxiv.org/abs/2410.16261)  [\[📜 InternVL 1.5 报告\]](https://arxiv.org/abs/2404.16821)  [\[📜 InternVL 1.0 论文\]](https://arxiv.org/abs/2312.14238)
+
+[\[📖 2.0 中文解读\]](https://zhuanlan.zhihu.com/p/706547971)  [\[📖 1.5 中文解读\]](https://zhuanlan.zhihu.com/p/699439759)  [\[📖 1.0 中文解读\]](https://zhuanlan.zhihu.com/p/702946079)
 
 [Switch to the English version (切换至英文版)](/README.md)
 
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 ## 最新消息 🚀🚀🚀
 
+- `2024/10/21`：我们发布了 Mini-InternVL 系列，其中包括三个对话模型：__Mini-InternVL-1B__、__Mini-InternVL-2B__ 和 __Mini-InternVL-4B__。这些模型在保持极小模型体积的同时实现了出色的性能：4B 模型仅用 5% 的模型大小便达到了 90% 的性能。有关更多详细信息，请查看我们的[项目页面](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat/shell/mini_internvl)和[文档](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html)。
 - `2024/08/01`: [Chartmimic](https://chartmimic.github.io/) 团队在他们的基准测试中评估了 InternVL2 系列模型。InternVL2-26B 和 76B 模型在开源模型中取得了前两名的成绩，其中 InternVL2-Llama3-76B 模型超过了 GeminiProVision，并表现出与 Claude-3-opus 相当的结果。
 - `2024/08/01`: InternVL2-Pro 在 [CharXiv](https://charxiv.github.io/#leaderboard) 数据集中实现了开源模型中的 SOTA 性能，也比部分知名闭源模型如 GPT-4V、Gemini 1.5 Flash、Claude 3 Sonnet 取得了更好成绩
 - `2024/07/24`: [MLVU](https://github.com/JUNJIE99/MLVU)团队在它们的基准测试中评估了InternVL-1.5。在多项选择任务上的平均表现为50.4%，而在生成任务上的表现为4.02。多项选择任务的表现在所有开源多模态大语言模型中排名第一。