Quantizing LLMs Step-by-Step: Converting FP16 Models to GGUF
Large language models like LLaMA, Mistral, and Qwen have billions of parameters that demand a lot of memory and compute power.
In this article, you will learn how quantization shrinks large language models and how to convert an FP16 checkpoint into an efficient GGUF file you can share and run locally.
Topics we will cover include:
- What precision types (FP32, FP16, 8-bit, 4-bit) mean for model size and speed
- How to use
huggingface_hubto fetch a model and authenticate - How to convert to GGUF with
llama.cppand upload the result to Hugging Face
And away we go.
