Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
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Updated
Aug 1, 2024 - Python
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.
Model optimizer used in Adlik.
[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
Caffe/Neon prototxt training file for our Neurocomputing2017 work: Fuzzy Quantitative Deep Compression Network
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
Hierarchical Ensemble Pruning
Official code for "EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices" (IJCAI2024)
This repository has the porpouse of give a solution to the travelling sales man problem
[JCST 2023] "Inductive Lottery Ticket Learning for Graph Neural Networks" by Yongduo Sui, Xiang Wang, Tianlong Chen, Meng Wang, Xiangnan He, Tat-Seng Chua.
[EMNLP 2024] Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
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.
Play the Othello board game against the algorithm and try to win!
attention pruning
Predicting employee productivity using tree models (decision tree cassification, cross validation, minimal cost-complexity pruning, random forest)
[PRL 2024] This is the code repo for our label-free pruning and retraining technique for autoregressive Text-VQA Transformers (TAP, TAP†).
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