Individual skill
emerging-techniques-model-pruning
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.
cli-tool/components/skills/ai-research/emerging-techniques-model-pruning/SKILL.md
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- emerging-techniques Declared skill tag
- model-pruning Declared skill tag
- wanda Declared skill tag
- sparsegpt Declared skill tag
- sparsity Declared skill tag
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- n:m-sparsity Declared skill tag
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# Model Pruning: Compressing LLMs
## When to Use This Skill
Use Model Pruning when you need to:
- **Reduce model size** by 40-60% with <1% accuracy loss
- **Accelerate inference** using hardware-friendly sparsity (2-4× speedup)
- **Deploy on constrained hardware** (mobile, edge devices)
- **Compress without retraining** using one-shot methods
- **Enable efficient serving** with reduced memory footprint
**Key Techniques**: Wanda (weights × activations), SparseGPT (second-order), structured pruning, N:M sparsity
**Papers**: Wanda ICLR 2024 (arXiv 2306.11695), SparseGPT (arXiv 2301.00774)