Structural Pruning for LLaMA
-
Updated
May 20, 2023 - Python
#
structural-pruning
Here are 6 public repositories matching this topic...
Towards Meta-Pruning via Optimal Transport, ICLR 2024 (Spotlight)
pruning optimal-transport model-compression network-pruning structured-pruning structural-pruning iclr2024 intra-fusion
-
Updated
Dec 5, 2024 - Python
Code for paper "Accelerating Federated Learning for IoT in Big Data Analytics with Pruning, Quantization and Selective Updating"
deep-neural-networks internet-of-things mnist vgg alexnet pruning quantization cifar10 alexnet-model edge-computing model-training federated-learning vgg16-model quantization-algorithms structural-pruning selective-updates
-
Updated
Jul 22, 2026 - Python
Official ICML 2026 Spotlight implementation for structural MoE compression, including attribution-guided channel scoring, coverage-maximized pruning, compact checkpoint construction, and fine-tuning support.
-
Updated
May 23, 2026 - Python
Multimodal-MoE-Slimming is a post-training compression framework for multimodal Mixture-of-Experts models. It analyzes how experts respond differently to visual and textual information, then uses this modality-aware behavior to reduce redundant expert capacity and build compact MoE checkpoints for more efficient deployment.
-
Updated
Jun 15, 2026 - Python
An independent reproduction of DepGraph (CVPR 2023) for ResNet-18 structural pruning. (Compression: 73.26% MACs, Accuracy: 91.69%)
-
Updated
Mar 8, 2026 - Python
Improve this page
Add a description, image, and links to the structural-pruning topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the structural-pruning topic, visit your repo's landing page and select "manage topics."