ggml
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About
GGML is a tensor library designed for machine learning, enabling large model implementations and high performance on commodity hardware. It supports multiple features such as 16-bit float and integer quantization, automatic differentiation, and built-in optimizations like ADAM and L-BFGS. Targeting Apple Silicon, it also supports x86 architectures through AVX/AVX2. The library avoids third-party dependencies, ensures zero memory allocations during runtime, and provides guided language output support. GGML is open-sourced under the MIT license and welcomes contributions to broaden its applications, such as whisper.cpp and llama.cpp for speech recognition and large language model inference respectively.
Platform
Task
Features
• optimized for apple silicon
• 16-bit float support
• automatic differentiation
• zero memory allocations during runtime
• tensor library support for machine learning
• integer quantization support (4-bit, 5-bit, 8-bit)
• built-in optimization algorithms (adam, l-bfgs)
• x86 architecture support via avx/avx2
• webassembly and wasm simd support
• guided language output support
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