Structural Pruning for LLaMA
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Updated
May 20, 2023 - Python
Structural Pruning for LLaMA
Towards Meta-Pruning via Optimal Transport, ICLR 2024 (Spotlight)
Code for paper "Accelerating Federated Learning for IoT in Big Data Analytics with Pruning, Quantization and Selective Updating"
Official ICML 2026 Spotlight implementation for structural MoE compression, including attribution-guided channel scoring, coverage-maximized pruning, compact checkpoint construction, and fine-tuning support.
A PyTorch implementation for structural pruning applied to neural networks during training
An independent reproduction of DepGraph (CVPR 2023) for ResNet-18 structural pruning. (Compression: 73.26% MACs, Accuracy: 91.69%)
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