model-pruning

SKILL.md

Key Techniques: Wanda (weights × activations), SparseGPT (second-order), structured pruning, N:M sparsity

Papers: Wanda ICLR 2024 (arXiv 2306.11695), SparseGPT (arXiv 2301.00774)

| Method | Accuracy Loss | Speed | Memory | Retraining Needed |

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods. Source: ovachiever/droid-tings.

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