ggml-org/ggml

**ggml-org/ggml**

Tensor library for machine learningggml Roadmap / Manifesto Tensor library for machine learning Note that this project is under active development. Some of the development is currently happening in the llama.cpp and whisper.cpp repos Features Low-level cross-platform implementation Integer quantization support Broad hardware support Automatic differentiation ADAM and L-BFGS optimizers No third-party dependencies Zero memory allocations during runtime Build git clone https://github.com/ggml-org/ggml cd ggml # install python dependencies in a virtual environment python3.10 -m venv .venv source .venv/bin/activate pip install -r requirements.txt # build the examples mkdir build && cd build cmake .. --config Release -j 8 GPT inference (example) # run the GPT-2 small 117M model ../examples/gpt-2/download-ggml-model.sh 117M ./bin/gpt-2-backend -m models/gpt-2-117M/ggml-model.bin -p "This is an example" For more information, checkout the corresponding programs in the examples folder. Using CUDA # fix the path to point to your CUDA compiler cmake -DGGML_CUDA=ON -DCMAKE_CUDA_COMPILER=/usr/local/cuda-12.1/bin/nvcc .. Using hipBLAS cmake -DCMAKE_C_COMPILER="$(hipconfig -l)/clang" -DCMAKE_CXX_COMPILER="$(hipconfig -l)/clang++" -DGGML_HIP=ON Using SYCL # linux source /opt/intel/oneapi/setvars.sh cmake -G "Ninja" -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL=ON ....

---

**[devsupporter 해설]**

이 기사는 GitHub Trending Daily All에서 제공하는 최신 개발 동향입니다. 관련 도구나 기술에 대해 더 알아보시려면 원본 링크를 참고하세요.