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.
Install
npx skills add davila7/claude-code-templates@model-pruningRuns in your terminal. Adds the skill globally for Claude Code, Cursor, Codex and others; add -g -y to skip prompts.
What it does
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) Installation Quick Start Wanda Pruning…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
View source on GitHubBefore you install
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
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