AutoAWQ

AutoAWQ is a user-friendly MLOps tool designed for 4-bit quantized models, aiming to enhance model efficiency by achieving a 3x acceleration in speed and a corresponding 3x reduction in memory requirements compared to FP16. This tool implements the Activation-aware Weight Quantization (AWQ) algorithm, originally developed at MIT.

AutoAWQ prioritizes ease of use and rapid inference speed, combining these features into a single package. Users can leverage AutoAWQ to easily quantize and perform inference on large language models (LLMs). The tool is available on Hugging Face's GitHub repository, with releases on PyPI for convenient installation and usage.