Model Quantization & Optimization

Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM. Supports GPTQ, AWQ, SmoothQuant, AutoRound, and FP8/INT8 quantization with seamless Hugging Face integration.

Unified library of SOTA model optimization techniques including quantization, pruning, distillation, and speculative decoding. Compresses deep learning models for deployment with TensorRT-LLM, TensorRT, and vLLM to optimize inference speed across NVIDIA hardware.