> ## Documentation Index
> Fetch the complete documentation index at: https://kalarislabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# model-pruning — AI agent skill for multimodal and emerging

> Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT.

# `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.

**Category:** [multimodal-and-emerging](/skills#multimodal-and-emerging) · **License:** MIT · **Version:** 1.0.0

## Install

```bash theme={null}
npx research-agent-skills install model-pruning
npx skills add KalarisLabs/research-agent-skills --skill model-pruning
```

## When to use it

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.

## Full playbook

Read [SKILL.md](https://github.com/KalarisLabs/research-agent-skills/blob/main/skills/model-pruning/SKILL.md) for the complete workflow, references and any scripts. The agent installer copies the full skill folder.