Model Pruning

This gives you practical implementations of Wanda and SparseGPT for compressing LLMs without retraining. You get one-shot pruning that can hit 50% sparsity with under 1% accuracy loss, plus N:M structured patterns that actually map to NVIDIA's sparse tensor cores for real 2x speedups on A100s. The Wanda code is straightforward: multiply weight magnitudes by activation statistics, threshold, mask, done. What's useful here is seeing the tradeoff between unstructured pruning (better quality, no speedup) and semi-structured 2:4 patterns (hardware friendly, minimal accuracy hit). If you're deploying large models on constrained hardware or trying to cut serving costs, this walks through the techniques that actually work in production without the usual fine-tuning overhead.

skills.sh installs: 1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill model-pruning --agent claude-code

Installs into .claude/skills of the current project.

Files

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

# Wanda implementation
git clone https://github.com/locuslab/wanda
cd wanda
pip install -r requirements.txt

# Optional: SparseGPT
git clone https://github.com/IST-DASLab/sparsegpt
cd sparsegpt
pip install -e .

# Dependencies
pip install torch transformers accelerate

Quick Start

Wanda Pruning (One-Shot, No Retraining)

Source: ICLR 2024 (arXiv 2306.11695)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

SKILL.md on GitHub

Details

Repository: github.com/orchestra-research/ai-research-skills
Categories: AI & Agent Building, Data Science & ML
First seen: Jun 3, 2026