Optimum Quanto

Optimum Quanto is a PyTorch quantization backend for Hugging Face Optimum, enabling efficient training and inference of large language models (LLMs) and other neural networks with reduced precision (e.g., 8-bit integers or 8-bit floats). It focuses on model optimization for hardware acceleration by integrating with PyTorch's native quantization functionalities. The current version is 0.2.7. As a rapidly evolving library deeply integrated with the Hugging Face ecosystem and PyTorch's quantization efforts, its release cadence is generally frequent, often tied to major Optimum or PyTorch updates.