Update README.md

README.md

@@ -5,6 +5,9 @@
 <a href="http://arxiv.org/abs/2411.05007"><b>Paper</b></a> | <a href="https://hanlab.mit.edu/projects/svdquant"><b>Website</b></a> | <a href="https://hanlab.mit.edu/blog/svdquant"><b>Blog</b></a> | <a href="https://svdquant.mit.edu"><b>Demo</b></a> | <a href="https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c"><b>HuggingFace</b></a> | <a href="https://modelscope.cn/collections/svdquant-468e8f780c2641"><b>ModelScope</b></a> | <a href="https://github.com/mit-han-lab/ComfyUI-nunchaku"><b>ComfyUI</b></a>
 </h3>
 
+<h3 align="center"> 
+<a href="https://github.com/mit-han-lab/nunchaku/blob/main/README.md"><b>English</b></a> | <a href="https://github.com/mit-han-lab/nunchaku/blob/main/README_ZH.md"><b>中文</b></a>
+</h3>
 
 **Nunchaku** is a high-performance inference engine optimized for 4-bit neural networks, as introduced in our paper [SVDQuant](http://arxiv.org/abs/2411.05007). For the underlying quantization library, check out [DeepCompressor](https://github.com/mit-han-lab/deepcompressor).
 
@@ -340,4 +343,4 @@ We thank MIT-IBM Watson AI Lab, MIT and Amazon Science Hub, MIT AI Hardware Prog
 
 We use [img2img-turbo](https://github.com/GaParmar/img2img-turbo) to train the sketch-to-image LoRA. Our text-to-image and image-to-image UI is built upon [playground-v.25](https://huggingface.co/spaces/playgroundai/playground-v2.5/blob/main/app.py) and [img2img-turbo](https://github.com/GaParmar/img2img-turbo/blob/main/gradio_sketch2image.py), respectively. Our safety checker is borrowed from [hart](https://github.com/mit-han-lab/hart).
 
-Nunchaku is also inspired by many open-source libraries, including (but not limited to) [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [vLLM](https://github.com/vllm-project/vllm), [QServe](https://github.com/mit-han-lab/qserve), [AWQ](https://github.com/mit-han-lab/llm-awq), [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), and [Atom](https://github.com/efeslab/Atom). 
\ No newline at end of file
+Nunchaku is also inspired by many open-source libraries, including (but not limited to) [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [vLLM](https://github.com/vllm-project/vllm), [QServe](https://github.com/mit-han-lab/qserve), [AWQ](https://github.com/mit-han-lab/llm-awq), [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), and [Atom](https://github.com/efeslab/Atom).