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.
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Install
armory install model-pruning --cli claudewrites the skill into.claude/skills/model-pruning/SKILL.mdListed as compatible
# fetches the source and writes it to:
.claude/skills/model-pruning/SKILL.mdNeeds the armory CLI · not on npm yet, build it from cli/ in the repository
What it is
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.
When to use it
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.
How to install / invoke
# Copy the skill into your .claude/skills/ directory
curl -sL https://raw.githubusercontent.com/davila7/claude-code-templates/main/cli-tool/components/skills/ai-research/emerging-techniques-model-pruning/SKILL.md -o .claude/skills/emerging-techniques-model-pruning/SKILL.md
Notes
Extracted from davila7/claude-code-templates, emerging-techniques-model-pruning category.