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whyb/README.md

👋 Welcome to 張小凡's GitHub Profile!

Hey there! Thanks for stopping by my little corner of the internet. I'm 張小凡(whyb), and I'm thrilled to have you here. 🎉

About Me

I'm currently working into the fascinating world of visual image processing and AI deployment. My latest obsession? Learning the ins and outs of AI LLM quantization. It's a wild ride, but I'm loving every minute of it!

What I'm Up To

  • 🔭 Employed at a company developing industry-leading audio and video editing tools.
  • 🚀 Deploying AI models to make the world a smarter place.
  • 📚 Self-studying AI LLM quantization – because who doesn't love a good challenge?

Open Source Contributions

I'm proud to be a part of some amazing open-source projects, including:

  • Tencent's ncnn: ncnn is a high-performance neural network inference framework. Pure C++ implementation, cross-platform(Windows, MacOS, Linux, Android, iOS), and supports cpu(loongarch, arm, risc-v, mips, x86), supports GPU(Nvidia, AMD, Intel, Qualcomm, Apple, Hisilicon, Rockchip npu, Axera, etc..)
  • scarsty's mlcc: mlcc is a common tool library implemented in C++, including lexical interpreter, common command parsing, file, string processing, etc..
  • nihui's ruapu: ruapu it is using various extended instructions to harass and amuse the CPU to inferred whether the CPU supports a certain extended instruction set.

Tech Stack

  • 💻 Languages: Modern C++ – my trusty sidekicks.
  • 🌍 Location: Based in the vibrant city of Chengdu.

Familiar Technologies

C++ OpenCV OpenVINO Onnxruntime DirectML TensorRT ffmpeg OpenGL Vulkan Qt


whyb's GitHub stats whyb's Most used languages

Feel free to explore my repositories, and don't hesitate to reach out if you want to collaborate or just chat about tech. Happy coding! 😄

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  1. Tencent/ncnn Tencent/ncnn Public

    ncnn is a high-performance neural network inference framework optimized for the mobile platform

    C++ 20.5k 4.2k

  2. scarsty/mlcc scarsty/mlcc Public

    一些有用的功能

    C++ 45 15

  3. nihui/ruapu nihui/ruapu Public

    Detect CPU features with single-file

    C 297 37