GGML

What is GGML

GGML is a tensor library designed for machine learning on commodity hardware, enabling high performance for large models. Written in C, it supports various integer quantization levels and optimizations for different architectures, including Apple Silicon and x86. The open-source library encourages contributions and experimentation, facilitating projects like Whisper and LLaMA for speech recognition and large language model inference, respectively.

Key Features of GGML

- Tensor Library for Machine Learning: Offers a powerful tensor library designed for enabling large models and achieving high performance on commodity hardware.

- Broad Compatibility: Compatible with leading projects like llama.cpp and whisper.cpp.

- 16-bit Float Support: Integral support for 16-bit floating-point calculations.

- Integer Quantization: Supports quantization to 4-bit, 5-bit, and 8-bit integers for optimized model performance.

- Automatic Differentiation: Built-in support for automatic differentiation to facilitate model training and optimization.

- Built-In Optimization Algorithms: Includes optimized algorithms like ADAM and L-BFGS for efficient model training.

- Optimized for Apple Silicon: Tailored for optimal performance on Apple Silicon hardware.

- Enhanced x86 Support: Utilizes AVX/AVX2 intrinsics for enhanced performance on x86 architectures.

- WebAssembly Support: Compatible with WebAssembly and WASM SIMD for web applications.

- No Third-Party Dependencies: Operates without relying on third-party dependencies, ensuring robustness and simplicity.

- Zero Memory Allocations During Runtime: Designed to avoid memory allocations during runtime for efficient execution.

- Guided Language Output Support: Provides structured support for guided language output.

- Open Source and Collaborative: Freely available under the MIT license with an open development process, welcoming contributions from the community.

- Minimalistic Design: Emphasizes simplicity and a minimal codebase for ease of use and maintenance.

- Spirit of Innovation: Encourages contributors to experiment with innovative ideas and build novel demonstrations.