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.

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 suite of tools that users, both novice and advanced can use to optimize machine learning models for deployment and execution.

deepsparse

Sparsity-aware deep learning inference runtime for CPUs

sparsezoo

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

sparseml

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

neural-compressor-pt

Repository of Intel® Neural Compressor

deepsparse-ent

Sparsity-aware deep learning inference runtime for CPUs

sconce

E2E AutoML Model Compression Package

delve

PyTorch model training and layer saturation monitor

neural-compressor-tf

Repository of Intel® Neural Compressor

fasterai

FasterAI: Prune and Distill your models with FastAI and PyTorch

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

sparsify

ML model optimization product to accelerate inference.

torch-optim

PyTorch models optimization by neural network pruning

mmrazor

OpenMMLab Model Compression Toolbox and Benchmark.

torch-simplify

Simplification of pruned models for accelerated inference | SoftwareX https://doi.org/10.1016/j.softx.2021.100907

kd-lib

A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.

only-train-once

Only Train Once (OTO): Automatic One-Shot General DNN Training and Compression Framework