GGML
What is GGML?
ggml is a machine learning tensor library written in C that provides high performance and large model support on commodity hardware. The library supports 16-bit floats, integer quantization, automatic differentiation, and built-in optimization algorithms like ADAM and L-BFGS. It is optimized for Apple Silicon, utilizes AVX/AVX2 intrinsics on x86 architectures, offers WebAssembly support, and performs zero memory allocations during runtime. Use cases include voice command detection on Raspberry Pi, running multiple instances on Apple devices, and deploying high-efficiency models on GPUs. ggml promotes simplicity, openness, and exploration while fostering community contributions and innovation.
GGML's Top Features
Key capabilities that make GGML stand out.
Written in C
16-bit float support
Integer quantization support (4-bit, 5-bit, 8-bit)
Automatic differentiation
Built-in optimization algorithms (ADAM, L-BFGS)
Optimized for Apple Silicon
Supports AVX/AVX2 intrinsics on x86 architectures
WebAssembly and WASM SIMD support
No third-party dependencies
Zero memory allocations during runtime
Guided language output support
Key Details
- Category
- Machine Learning
- Pricing Model
- Free
- Website
- Visit GGML
- Last Updated
- August 8, 2024
Tags
Category
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Use Cases
Who benefits most from this tool.
Voice recognition enthusiasts
Using ggml for short voice command detection on Raspberry Pi 4 with whisper.cpp.
Apple device users
Running multiple instances of large models like 13B LLaMA and Whisper Small on M1 Pro.
AI researchers
Deploying high-efficiency models like 7B LLaMA at 40 tok/s on M2 Max.
Machine learning developers
Creating machine learning solutions with built-in optimization algorithms and automatic differentiation.
Web developers
Deploying tensor operations on the web via WebAssembly and WASM SIMD.
Open-source contributors
Contributing to the development and innovation of ggml and related projects.
Tech companies
Exploring enterprise deployment and support for machine learning solutions using ggml.
Embedded system developers
Implementing machine learning models on embedded systems like Raspberry Pi and other commodity hardware.
Optimization experts
Utilizing integer quantization and zero runtime memory allocations for efficient model deployments.
Educational institutions
Teaching and experimenting with high-performance tensor libraries in academic settings.