Merge pull request #96 from CharlesCNorton/patch-1

Update README.md

README.md

@@ -43,7 +43,7 @@ SVDQuant is a post-training quantization technique for 4-bit weights and activat
 
 *  Ensure your CUDA version is **≥ 12.2 on Linux** and **≥ 12.6 on Windows**.
 
-*  For Windows user, please refer to [this issue](https://github.com/mit-han-lab/nunchaku/issues/6) for the instruction. Please upgrade your MSVC compiler to the latest version.
+*  For Windows users, please refer to [this issue](https://github.com/mit-han-lab/nunchaku/issues/6) for the instruction. Please upgrade your MSVC compiler to the latest version.
 
 *  We currently support only NVIDIA GPUs with architectures sm_86 (Ampere: RTX 3090, A6000), sm_89 (Ada: RTX 4090), and sm_80 (A100). See [this issue](https://github.com/mit-han-lab/nunchaku/issues/1) for more details.
 
@@ -153,4 +153,4 @@ 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 sketch-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). 
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+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).