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README.md

@@ -3,11 +3,19 @@ license: mit
 pipeline_tag: image-text-to-text
 ---
 
+# InternVL2-2B-AWQ
+
+[\[📂 GitHub\]](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)  \[🌟 [魔搭社区](https://modelscope.cn/organization/OpenGVLab) | [教程](https://mp.weixin.qq.com/s/OUaVLkxlk1zhFb1cvMCFjg) \]
+
+## Introduction
+
 <div align="center">
   <img src="https://raw.githubusercontent.com/InternLM/lmdeploy/0be9e7ab6fe9a066cfb0a09d0e0c8d2e28435e58/resources/lmdeploy-logo.svg" width="450"/>
 </div>
 
-# INT4 Weight-only Quantization and Deployment (W4A16)
+### INT4 Weight-only Quantization and Deployment (W4A16)
 
 LMDeploy adopts [AWQ](https://arxiv.org/abs/2306.00978) algorithm for 4bit weight-only quantization. By developed the high-performance cuda kernel, the 4bit quantized model inference achieves up to 2.4x faster than FP16.
 
@@ -34,7 +42,7 @@ This article comprises the following sections:
 
 <!-- tocstop -->
 
-## Inference
+### Inference
 
 Trying the following codes, you can perform the batched offline inference with the quantized model:
 
@@ -56,7 +64,7 @@ print(response.text)
 
 For more information about the pipeline parameters, please refer to [here](https://github.com/InternLM/lmdeploy/blob/main/docs/en/inference/pipeline.md).
 
-## Service
+### Service
 
 To deploy InternVL2 as an API, please configure the chat template config first. Create the following JSON file `chat_template.json`.