Merge branch 'master' of https://ghproxy.com/https://github.com/QwenL…

…M/Qwen-VL

README.md

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 <p align="center">
         Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 <a> | <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 <a>| <a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ Qwen-VL-Chat-Int4 <a href="https://huggingface.co/Qwen/Qwen-VL-Chat-Int4">🤗</a>
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-<a href="assets/wechat.png">WeChat</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2308.12966">Paper</a>
+<a href="assets/wechat.png">WeChat</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2308.12966">Paper</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://github.com/camenduru/Qwen-VL-Chat-colab">Colab</a>
 </p>
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@@ -36,6 +36,7 @@ We release two models of the Qwen-VL series:
   <br>
 
 ## News and Updates
+* ```2023.9.8``` 👍👍👍 Thanks to [camenduru](https://github.com/camenduru) for contributing the wonderful [Colab](https://github.com/camenduru/Qwen-VL-Chat-colab). Everyone can use it as a local or online Qwen-VL-Chat-Int4 Demo tutorial on one 12G GPU.
 * ```2023.9.5``` 👏👏👏 Qwen-VL-Chat achieves SOTAs on [MME Benchmark](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation), a comprehensive evaluation benchmark for multimodal large language models. It measures both perception and cognition abilities on a total of 14 subtasks.
 * ```2023.9.4``` ⭐⭐⭐ Qwen-VL series achieve SOTAs on [Seed-Bench](https://huggingface.co/spaces/AILab-CVC/SEED-Bench_Leaderboard), a multimodal benchmark of 19K multiple-choice questions with accurate human annotations for evaluating Multimodal LLMs including both image and video understanding.
 * ```2023.9.1``` 🔥🔥🔥 We release the [TouchStone](https://github.com/OFA-Sys/TouchStone) Evaluation, which is a comprehensive assessment of multimodal language models, encompassing not only basic recognition and comprehension but also extending to literary creation. By using strong LLMs as judges and converting multimodal information into text.

README_CN.md

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 <p align="center">
         Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 <a> | <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 <a>| <a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ Qwen-VL-Chat-Int4 <a href="https://huggingface.co/Qwen/Qwen-VL-Chat-Int4">🤗</a>
 <br>
-<a href="assets/wechat.png">WeChat</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2308.12966">Paper</a>
+<a href="assets/wechat.png">WeChat</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-VL-Chat-Demo/summary">Demo</a>&nbsp ｜ &nbsp<a href="https://arxiv.org/abs/2308.12966">Paper</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://github.com/camenduru/Qwen-VL-Chat-colab">Colab</a>
 </p>
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@@ -37,6 +37,7 @@
 
 ## 新闻
 
+* 2023年9月8日 感谢[camenduru](https://github.com/camenduru)贡献了[Colab](https://github.com/camenduru/Qwen-VL-Chat-colab)示例，每个人都可以以此为教程，在12G的GPU上做本地或在线的Demo。
 * 2023年9月5日 在社区多模态通用模型榜单 [MME Benchmark](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation) 上取得了感知和认知双赛道的当前最好结果。
 * 2023年9月4日 在社区多模态通用模型榜单 [SEED-Bench](https://huggingface.co/spaces/AILab-CVC/SEED-Bench_Leaderboard) 上取得了图像理解和视频理解的当前最好结果。
 * 2023年9月1日 发布[TouchStone](https://github.com/OFA-Sys/TouchStone) 测评, 这是一个综合评估LVLM能力的测评,它不仅考察模型的视觉描述和推理能力，还包括根据视觉内容的文学创作能力。同时它是将多模态信息用文本表述并用LLMs进行评估的方法。