Model Pruning
orchestra-research/ai-research-skills/19-emerging-techniques/model-pruningby orchestra-research773a52944ba4MIT13K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 months ago
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.
Only the file list is public. File contents are available once the skill is installed in a workspace.
| Path | Size | Type |
|---|---|---|
| references/wanda.md | 9.3 KB | text/markdown |
| SKILL.md | 13.4 KB | text/markdown |
Source and attribution
Source:orchestra-research/ai-research-skillsin19-emerging-techniques/model-pruningat commit773a529
License: MIT
Content belongs to its original authors. SourceWeft indexes it from a public repository.
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