Fast and accurate GGUF models for your CPU
Are you looking for fast and accurate GGUF models for your CPU? Look no further! GGUF is a binary file format designed for efficient storage and fast loading of large language models (LLMs) using GGML, a C-based tensor library for machine learning.
With GGUF, all essential components for inference, including the tokenizer and code, are neatly encapsulated within a single file. Not only does GGUF support the conversion of various language models like Llama 3, Phi, and Qwen2, but it also allows for model quantization to lower precisions, enhancing speed and memory efficiency on CPUs.
While we often refer to “GGUF quantization,” it’s important to note that GGUF is primarily a file format and not a quantization method in itself. In fact, llama.cpp implements several quantization algorithms to reduce model size and serialize the resulting model in the GGUF format.
In this article, we’ll delve into the process of accurately quantizing an LLM and converting it to GGUF using an importance matrix (imatrix) and the K-Quantization method. I’ll also share the GGUF conversion code for Gemma 2 Instruct, which can be applied to other supported models by llama.cpp, such as Qwen2, Llama 3, Phi-3, and more. Additionally, we’ll explore how to assess the accuracy of the quantization and the inference throughput of the resulting models.