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.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install orchestra-research-ai-research-skills-model-pruning
Repository
Skill path: 19-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.
Open repositoryBest for
Primary workflow: Ship Full Stack.
Technical facets: Full Stack.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: Orchestra-Research.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install model-pruning into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/Orchestra-Research/AI-Research-SKILLs before adding model-pruning to shared team environments
- Use model-pruning for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.