NVIDIA / Model-Optimizer

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.

活跃维护 Apache 2.0 Python Tracked

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项目摘要

NVIDIA/Model-Optimizer: 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.

为什么值得关注

Growing community with 5,416 stars, active contributor community (83 contributors), frequent releases.

优势

Large and active community
Continuous integration configured
Test suite present

局限性

Limited documentation

分析模型：fallback | 分析时间：3 天前