https://www.philschmid.de/gptq-llama

• 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.