Optimize open LLMs using GPTQ and Hugging Face Optimum


  • Hugging Face Optimum team collaborated with AutoGPTQ library for a simple API to apply GPTQ quantization on language models.
  • GPTQ quantization allows open LLMs to 8, 4, 3, or 2 bits, enabling them to run on smaller hardware with minimal performance loss.
  • The blog covers:
  1. Setting up the development environment.
  2. Preparing the quantization dataset.
  3. Loading and quantizing the model.
  4. Testing performance and inference speed.
  5. Bonus: Running inference with text generation.
  • GPTQ’s purpose is explained before diving into the tutorial.