[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9623":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":31,"readmeContent":32,"aiSummary":33,"trendingCount":16,"starSnapshotCount":16,"syncStatus":34,"lastSyncTime":35,"discoverSource":36},9623,"ggml","ggml-org\u002Fggml","ggml-org","Tensor library for machine learning","",null,"C++",14792,1671,149,300,0,3,29,163,21,44.67,"MIT License",false,"master",true,[27,28,29,30],"automatic-differentiation","large-language-models","machine-learning","tensor-algebra","2026-06-12 02:02:10","# ggml\n\n[Manifesto](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\u002Fdiscussions\u002F205)\n\nTensor library for machine learning\n\n***Note that this project is under active development. \\\nSome of the development is currently happening in the [llama.cpp](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp) and [whisper.cpp](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fwhisper.cpp) repos***\n\n## Features\n\n- Low-level cross-platform implementation\n- Integer quantization support\n- Broad hardware support\n- Automatic differentiation\n- ADAM and L-BFGS optimizers\n- No third-party dependencies\n- Zero memory allocations during runtime\n\n## Build\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fggml\ncd ggml\n\n# install python dependencies in a virtual environment\npython3.10 -m venv .venv\nsource .venv\u002Fbin\u002Factivate\npip install -r requirements.txt\n\n# build the examples\nmkdir build && cd build\ncmake ..\ncmake --build . --config Release -j 8\n```\n\n## GPT inference (example)\n\n```bash\n# run the GPT-2 small 117M model\n..\u002Fexamples\u002Fgpt-2\u002Fdownload-ggml-model.sh 117M\n.\u002Fbin\u002Fgpt-2-backend -m models\u002Fgpt-2-117M\u002Fggml-model.bin -p \"This is an example\"\n```\n\nFor more information, checkout the corresponding programs in the [examples](examples) folder.\n\n## Resources\n\n- [Introduction to ggml](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fintroduction-to-ggml)\n- [The GGUF file format](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fggml\u002Fblob\u002Fmaster\u002Fdocs\u002Fgguf.md)\n","ggml 是一个用于机器学习的张量库，主要使用 C++ 开发。该项目提供低级别的跨平台实现、整数量化支持以及广泛的硬件兼容性，同时具备自动微分功能，并内置了 ADAM 和 L-BFGS 优化器。它不依赖任何第三方库，在运行时无需内存分配，这使得其非常适合在资源受限或对性能有高要求的应用场景中使用，例如在嵌入式设备上进行大规模语言模型的推理。",2,"2026-06-11 03:23:49","top_topic"]