#Model-compression
Showing 36 of 36 repositories tagged #model-compression, ranked by stars
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Awesome Knowledge Distillation
[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free
Collection of recent methods on (deep) neural network compression and acceleration.
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
[ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization
Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.
Awesome machine learning model compression research papers, quantization, tools, and learning material.
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Infrastructures™ for Machine Learning Training/Inference in Production.
[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
[CVPR 2024 Highlight] Logit Standardization in Knowledge Distillation
a collection of computer vision projects&tools. 计算机视觉方向项目和工具集合。
On-device LLM Inference Powered by X-Bit Quantization
[ICLR 2025🔥] SVD-LLM & [NAACL 2025🔥] SVD-LLM V2
FasterAI: Prune and Distill your models with FastAI and PyTorch
Code and pretrained models for paper: Data-Free Adversarial Distillation
The framework to prune LLMs to any size and any config.
The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)".
Knowledge Distillation Toolkit
Gather research papers, corresponding codes (if having), reading notes and any other related materials about Hot🔥🔥🔥 fields in Computer Vision based on Deep Learning.
Compressing Representations for Self-Supervised Learning
Navigating Model Phase Transitions to Enable Extreme Lossless Compression: A Perspective
It is a blueprint to data science from the mathematics to algorithms. It is not completed.
Model Compression Based on Geoffery Hinton's Logit Regression Method in Keras applied to MNIST 16x compression over 0.95 percent accuracy.An Implementation of "Distilling the Knowledge in a Neural Network - Geoffery Hinton et. al"
Train neural networks with joint quantization and pruning on both weights and activations using any pytorch modules
QuantEase, a layer-wise quantization framework, frames the problem as discrete-structured non-convex optimization. Our work leverages Coordinate Descent techniques, offering high-quality solutions without the need for matrix inversion or decomposition.