Pruning Python Packages

nncf

Neural Network Compression Framework for enhanced OpenVINO™ inference

tensorflow-model-optimization

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

neural-compressor

SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime

aimet-torch

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

torch-pruning

[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.

aimet-onnx

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

sparsezoo

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

deepsparse

Sparsity-aware deep learning inference runtime for CPUs

cozempic

Context cleaning for Claude Code — prune bloated sessions, protect Agent Teams from context loss, auto-guard with tiered pruning

tf-model-optimization-nightly

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

fasterai

FasterAI: Prune and Distill your models with FastAI and PyTorch

neural-compressor-pt

SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime

thekaveh-nnx

Lightweight PyTorch toolkit for training, fine-tuning, and exporting modern neural nets. FFN, GNN, decoder-only LM, diffusion, JEPA, MoE, PEFT, quantization, pruning, surgery, ONNX and Hugging Face Hub interop, plus experimental GGUF/Ollama bundle export.

sparseml

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

torch-kirigami

Structured pruning and sparse training for PyTorch models.

certified-pruner

Stop bad runs early, with a conformal bound on how often you stop a good one

sparsify

ML model optimization product to accelerate inference.

mmrazor

OpenMMLab Model Compression Toolbox and Benchmark.

sconce

E2E AutoML Model Compression Package

neural-compressor-tf

SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime

retinal-thin-vessels

A Python package for computing the recall and precision scores specifically on thin vessels in retinal images and generating weight masks for BCE Loss to enhance models perfomance on segmenting these fine structures, as detailed in the paper "Vessel-Width-Based Metrics and Weight Masks for Retinal Blood Vessel Segmentation".

deepsparse-ent

Sparsity-aware deep learning inference runtime for CPUs

delve

PyTorch model training and layer saturation monitor

micronet

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape