GGML
GGML is a tensor library for machine learning to enable large models and high performance on commodity hardware.
December 29th, 2024
About GGML
GGML (Generic Graph Machine Learning) is a highly capable tensor library designed specifically for machine learning professionals. It offers a comprehensive range of features and optimizations that facilitate the development of large-scale models and high-performance computing on standard hardware.
Key Features
- GGML is a C-based implementation that ensures efficiency and compatibility across platforms.
- It supports 16-bit floating-point operations, which reduces memory requirements and improves computation speed.
- Integer quantization is enabled, allowing for optimization of memory and computation by quantizing model weights and activations to lower bit precision.
Use Cases
- GGML is perfect for large-scale model training that needs significant computational resources.
- GGML's optimizations make it ideal for high-performance computing tasks in machine learning.
- GGML is a robust tensor library that caters to the needs of machine learning practitioners.
Other Features
- Open Source
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