gguf-quantization

What problem does it solve?

This Skill addresses the challenge of running large language models efficiently on diverse hardware, including consumer-grade CPUs, Apple Silicon, and GPUs, by leveraging the GGUF format and quantization techniques.

Core Features & Use Cases

• GGUF Conversion: Convert models from HuggingFace format to the GGUF format.
• Quantization: Apply various quantization methods (e.g., Q4_K_M, Q8_0) to reduce model size and memory footprint while minimizing quality loss.
• Inference: Run quantized models using llama.cpp for CPU and GPU inference.
• Use Case: Deploying a large language model on a laptop for local chat completion or running inference on an edge device with limited resources.

Quick Start

Use the gguf-quantization skill to convert the model located at './path/to/model' to GGUF format with Q4_K_M quantization.