GGML (Generic Graph Machine Learning) is a powerful tensor library that caters to the needs of machine learning practitioners. It provides a robust set of features and optimizations that enable the training of large-scale models and high-performance computing on commodity hardware.
- C-based Implementation:GGML is written in C, providing efficiency and compatibility across platforms.
- 16-bit Float Support:Supports 16-bit floating-point operations, reducing memory requirements and improving computation speed.
- Integer Quantization:Enables optimization of memory and computation by quantizing model weights and activations to lower bit precision.
- Large-scale Model Training:GGML is ideal for training machine learning models that require extensive computational resources.
- High-Performance Computing:GGML’s optimizations make it well-suited for high-performance computing tasks in machine learning.