GGML is a tensor library for machine learning to enable large models and high performance on commodity hardware.

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GGML Features 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. Key Features: 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. Use Cases: 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. GGML is a powerful tensor library designed to meet the demands of machine learning practitioners.