NVIDIA/Model-Optimizer
Model Optimizer
Model-Optimizer is a deep learning toolkit and framework dedicated to compressing, pruning, quantizing, and optimizing neural network architectures. It provides methodologies covering weight quantization, model distillation, and speculative decoding for efficient text generation, alongside automated neural architecture search for discovering optimal network structures.
The library implements post-training quantization pipelines that convert high-precision neural network weights into lower-bit formats using calibration data. Additional optimization techniques include teacher-student knowledge distillation, magnitude-based post-training sparsification, and pruning utilities that remove redundant weights and connections. It also supports speculative decoding acceleration using lightweight draft models or auxiliary heads.
For operational workflows, the toolkit includes sparse model checkpointing mechanisms to persist masks and metadata alongside weights, as well as export utilities that serialize compressed models into standard formats for downstream inference frameworks.
