- 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:
- Setting up the development environment.
- Preparing the quantization dataset.
- Loading and quantizing the model.
- Testing performance and inference speed.
- Bonus: Running inference with text generation.
- GPTQ’s purpose is explained before diving into the tutorial.