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‎README.md

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<img src='https://github.com/user-attachments/assets/9306862b-2a30-4a87-bb33-0fde9e9d7cea' width=250 >
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<a href="#cuda-kernel">📚200+ CUDA Kernels</a> | <a href="#my-blogs-part-1"> 📚100+ LLM/CUDA Blogs</a> | <a href="#hgemm-tensorcores-mma-bench"> ⚡️HGEMM MMA</a> | <a href="#fa-mma-bench"> ⚡️FA-2 MMA </a> <p>
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<h2>📚 LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners 🐑</h2>
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<a href="#cuda-kernel">📚200+ CUDA Kernels</a> | <a href="#my-blogs-part-1"> 📚100+ LLM/VLM/CUDA Blogs</a> | <a href="#hgemm-tensorcores-mma-bench"> ⚡️HGEMM MMA</a> | <a href="#fa-mma-bench"> ⚡️FA-2 MMA </a> <p>
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📚 **Modern CUDA Learn Notes with PyTorch** for Beginners: It includes **Tensor/CUDA Cores, TF32/F16/BF16/F8**, [📖200+ CUDA Kernels🔥🔥(Easy -> Hard++)](#cuda-kernel) with PyTorch bindings, [📖100+ LLM/VLM/CV/CUDA/CuTe🔥](#my-blogs-part-1) blogs, [📖toy-hgemm⚡️⚡️](./kernels/hgemm) which can achieve `98%~100%` performance of **cuBLAS**, and [📖flash-attention-mma⚡️⚡️](./kernels/flash-attn) using Tensor Cores with pure MMA PTX. Welcome to 🌟👆🏻star this repo to support me, many thanks ~ 🎉🎉
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<a href="#cuda-kernel">📚200+ CUDA Kernels</a> | <a href="#my-blogs-part-1"> 📚100+ LLM/CUDA Blogs</a> | <a href="#hgemm-tensorcores-mma-bench"> ⚡️HGEMM MMA</a> | <a href="#fa-mma-bench"> ⚡️FA-2 MMA </a> <p>
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## 📖 News 🔥🔥
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