Popular GitHub Repositories for Alphabeta Pruning

TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.

Sparsity-aware deep learning inference runtime for CPUs

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

A curated list of neural network pruning resources.

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

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

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

A curated list for Efficient Large Language Models

在 oxford hand 数据集上对 YOLOv3 做模型剪枝（network slimming）

Practical course about Large Language Models.

OpenMMLab Model Compression Toolbox and Benchmark.

PaddleSlim is an open-source library for deep model compression and architecture search.

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

Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Neural Network Compression Framework for enhanced OpenVINO? inference

[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

A simple and effective LLM pruning approach.