[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
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Jan 6, 2024 - Python
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[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
[NeurIPS 2023] Structural Pruning for Diffusion Models
Awesome Pruning. ✅ Curated Resources for Neural Network Pruning.
💍 Efficient tensor decomposition-based filter pruning
Knowledge distillation from Ensembles of Iterative pruning (BMVC 2020)
Structured pruning and bias visualization for Large Language Models. Tools for LLM optimization and fairness analysis.
Code for CHIP: CHannel Independence-based Pruning for Compact Neural Networks (NeruIPS 2021).
OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
This repository is the official implementation of the paper Pruning via Iterative Ranking of Sensitivity Statistics and implements novel pruning / compression algorithms for deep learning / neural networks. Amongst others it implements structured pruning before training, its actual parameter shrinking and unstructured before/during training.
We have implemented a framework that supports developers to structured prune neural networks of Tensorflow Models
Code Implementation for "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP 2023)
The framework to prune LLMs to any size and any config.
🌠 Enhanced Network Compression Through Tensor Decompositions and Pruning
Code repository for paper "Efficient Structured Pruning and Architecture Searching for Group Convolution" https://arxiv.org/abs/1811.09341
Towards Meta-Pruning via Optimal Transport, ICLR 2024 (Spotlight)
第四届“华为杯”无线通信算法大赛:LoMACS-SVDNet: PyTorch model for MIMO SVD (no QR/SVD/EVD), orthogonality via NOR, FFT gating, projected attention, structured pruning. Score 63 — 4th (Third Prize).
Research-ready and production-friendly neural network pruning for PyTorch—transparent methods, reproducible baselines, and deployment metrics to compress models for real-world use.
2SSP: A Two-Stage Framework for Structured Pruning of LLMs
Loss-aware automatic selection of structured pruning criteria for deep neural network acceleration. ✅ Published in Image and Vision Computing, 2023.
Official implementation of our ICC 2025 paper on structured nonuniform pruning of deep learning models for TinyML wireless applications.
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