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.
安装
npx skills add davila7/claude-code-templates --skill model-pruningnpx skills add davila7/claude-code-templates --skill model-pruning -a claude-code -gnpx skills add davila7/claude-code-templates --skill model-pruning -a codex -ggemini skills install https://github.com/davila7/claude-code-templates.git --path cli-tool/components/skills/ai-research/emerging-techniques-model-pruninggh skill install davila7/claude-code-templates model-pruninggit clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/davila7__claude-code-templates
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/davila7__claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-model-pruning ~/.claude/skills/model-pruning # Claude Code
cp -R /tmp/davila7__claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-model-pruning ~/.agents/skills/model-pruning # Codex, Cursor, Gemini CLI, Copilot, OpenCode