《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
-
Updated
Dec 1, 2024 - Jupyter Notebook
《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
Sparsity-aware deep learning inference runtime for CPUs
[CVPR 2023] DepGraph: Towards Any Structural Pruning
A curated list of neural network pruning resources.
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
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、reg…
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
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.
OpenMMLab Model Compression Toolbox and Benchmark.
Config driven, easy backup cli for restic.
Efficient computing methods developed by Huawei Noah's Ark Lab
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
mobilev2-yolov5s剪枝、蒸馏,支持ncnn,tensorRT部署。ultra-light but better performence!
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
Embedded and mobile deep learning research resources
Add a description, image, and links to the pruning topic page so that developers can more easily learn about it.
To associate your repository with the pruning topic, visit your repo's landing page and select "manage topics."