Accessible large language models via k-bit quantization for PyTorch.
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PyTorch native quantization for training and inference
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM
QLoRA: Efficient Finetuning of Quantized LLMs
A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime
