GGUF is a binary file format designed for efficient storage and fast large language model (LLM) loading with GGML, a C-based tensor library for machine learning.
GGUF encapsulates all essential components for inference, including the tokenizer and code, inside a single file. It supports the conversion of varied language models, resembling Llama 3, Phi, and Qwen2. Moreover, it facilitates model quantization to lower precisions to enhance speed and memory efficiency on CPUs.
We regularly write “GGUF quantization” but GGUF itself is simply a file format, not a quantization method. There are several quantization algorithms implemented in llama.cpp to cut back the model size and serialize the resulting model within the GGUF format.
In this text, we’ll see find out how to accurately quantize an LLM and convert it to GGUF, using an importance matrix (imatrix) and the K-Quantization method. I provide the GGUF conversion code for Gemma 2 Instruct, using an imatrix. It really works the identical with other models supported by llama.cpp: Qwen2, Llama 3, Phi-3, etc. We can even see find out how to evaluate the accuracy of the quantization and inference throughput of the resulting models.
