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

[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.

Automated Identification of Redundant Layer Blocks for Pruning in Large Language Models

A research library for pytorch-based neural network pruning, compression, and more.

[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models

Model optimizer used in Adlik.

Caffe/Neon prototxt training file for our Neurocomputing2017 work: Fuzzy Quantitative Deep Compression Network

Official code for "EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices" (IJCAI2024)

This project provides tools to load and prune large language models using a structured pruning method.

KEN: Unleash the power of large language models with the easiest and universal non-parametric pruning algorithm

[EMNLP 2024] Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning

Hierarchical Ensemble Pruning

[PRL 2024] This is the code repo for our label-free pruning and retraining technique for autoregressive Text-VQA Transformers (TAP, TAP†).

[CVPR 2025] "Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training" by Lexington Whalen, Zhenbang Du, Haoran You, Chaojian Li, Sixu Li, and Yingyan (Celine) Lin.

This repository contains scripts to prune Wav2vec2 using a neuroevolution-based method. More details about this method can be found in the paper Compressing Wav2vec2 for Embedded Applications.

[JCST 2023] "Inductive Lottery Ticket Learning for Graph Neural Networks" by Yongduo Sui, Xiang Wang, Tianlong Chen, Meng Wang, Xiangnan He, Tat-Seng Chua.

This repository has the porpouse of give a solution to the travelling sales man problem

Code and trained models for our paper: N. Kaparinos, V. Mezaris, "B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning", Proc. IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW 2025).

Official implementation of the LIES Network for Symbolic Regression