Add OpenXLab demo

Author: csuhan

README.md

@@ -10,7 +10,7 @@ Official implementation of ['LLaMA-Adapter: Efficient Fine-tuning of Language Mo
 
 This repo proposes **LLaMA-Adapter (V2)**, a lightweight adaption method for fine-tuning **Instruction-following** and **Multi-modal** [LLaMA](https://github.com/facebookresearch/llama) models 🔥.
 
-Try out the web demo 🤗 of LLaMA-Adapter: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/csuhan/LLaMA-Adapter), [LLaMA-Adapter V2](http://llama-adapter.opengvlab.com/) and [ImageBind-LLM](http://imagebind-llm.opengvlab.com/).
+Try out the web demo 🤗 of LLaMA-Adapter: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/csuhan/LLaMA-Adapter)  [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/RenRuiZhang/LLaMA_Adapter), [LLaMA-Adapter V2](http://llama-adapter.opengvlab.com/) and [ImageBind-LLM](http://imagebind-llm.opengvlab.com/).
 
 ## News
 - **[2023.08.28]** We release quantized LLM with [OmniQuant](https://github.com/OpenGVLab/OmniQuant), which is an efficient, accurate, and omnibearing (even extremely low bit) quantization algorithm. Multimodal version is coming soon.🔥🔥🔥