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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-pruning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash # 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) ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer # Load model model = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16, device_map="cu
- When to Use This Skill
- Installation
- Quick Start
- Wanda Pruning (One-Shot, No Retraining)
- SparseGPT (Second-Order Pruning)
- N:M Structured Pruning (Hardware Accelerator)
- Core Concepts
- 1. Pruning Criteria
- 2. Structured vs Unstructured
- 3. Sparsity Patterns
- Pruning Strategies
- Strategy 1: Gradual Magnitude Pruning
- Strategy 2: Layer-wise Pruning
- Strategy 3: Iterative Pruning + Fine-tuning
Wanda implementation git clone https://github.com/locuslab/wanda cd wanda pip install -r requirements.txt git clone https://github.com/IST-DASLab/sparsegpt cd sparsegpt pip install -e . Dependencies pip install torch transformers accelerate
What does the model-pruning skill do?
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 do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-pruning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.