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model-pruning SKILL.md skillA

model-pruning from orchestra-research/ai-research-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned

mdr add orchestra-research/ai-research-skills/model-pruning@v1.0.0

Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.

Audit grade: A, 17 of 17 checks passed
Kind: SKILL.md skill
Repository: orchestra-research/ai-research-skills
Path: 19-emerging-techniques/model-pruning/SKILL.md
Versions recorded: 5
Latest version: v1.0.0, committed Nov 20, 2025
First version recorded: Nov 19, 2025
Size: 13,744 bytes
Repository stars: 13,718
License: MIT

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.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# 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",
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint

mdr add orchestra-research/ai-research-skills/model-pruning@sha256:aca913d834bc1e3d

The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

[![mdr](https://markdownregistry.com/badge/art_kycslkdnrudzmppk.svg)](https://markdownregistry.com/a/art_kycslkdnrudzmppk)

1 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 5 versions of this file, between Nov 19, 2025 and Nov 20, 2025, more than one a day.

version	committed	commit	size	audit
v1.0.0 latest	2025-11-20	0a9ded4	13,744 B	A	view · diff
v1.0.0	2025-11-20	51fc3a3	13,723 B	A	view · diff
v1.0.0	2025-11-20	81c38fe	13,722 B	A	view · diff
v1.0.0	2025-11-19	bd4461f	13,721 B	A	view · diff
git:20251119.66e31d2	2025-11-19	66e31d2	13,471 B	A	view

Audit of the latest version

A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.

pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (13744 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag

Source

GitHub

orchestra-research/ai-research-skills · 13,718 stars · license MIT · pushed 2026-10-11 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_kycslkdnrudzmppk
GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/model-pruning
GET https://markdownregistry.com/api/v1/blob/aca913d834bc1e3d39bbfb75d0658d5a1b9acabf6511744719c5bab91bf306a8

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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