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Model Pruning

Checked
davila7·vlatest·ai-ml·MIT

Reduces large language model size and computational cost while preserving accuracy through strategic parameter removal.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill model-pruning -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

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Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/emerging-techniques-model-pruning/SKILL.md
Scanned
2 files, 792 lines
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About this skill

What it does

This skill enables AI agents to reduce the size of large language models (LLMs) and accelerate inference speed without retraining. It achieves this through a technique called model pruning, which strategically removes less important parameters from the LLM. The goal is to compress models by 40-60% while maintaining accuracy and enabling deployment on resource-constrained devices. Key techniques supported include Wanda, SparseGPT, and structured pruning.

When to use it

  • To reduce the overall size of an LLM for easier storage and distribution.
  • When faster inference speeds are needed, potentially achieving 2-4x speedup.
  • For deploying LLMs on devices with limited resources like mobile phones or edge computing platforms.
  • To enable efficient serving of LLMs by reducing their memory footprint.
  • When a one-shot compression method is desired, avoiding the need for retraining the model.

Key capabilities

  • Model Size Reduction: Compresses models by 40-60% with minimal accuracy loss (<1%).
  • Inference Acceleration: Potentially speeds up inference by 2-4x through hardware-friendly sparsity.
  • One-Shot Pruning: Performs compression without requiring model retraining.
  • Supports Multiple Techniques: Implements Wanda, SparseGPT, and structured pruning methods.
  • Hardware Deployment: Enables deployment on constrained hardware (mobile, edge devices).

Example prompts

  • "Prune the Llama 2-7b model using Wanda with a sparsity of 50%."
  • "Reduce the size of this language model for mobile deployment."
  • "Apply SparseGPT pruning to improve inference speed."

Tips & gotchas

  • Requires installation of dependencies including torch, transformers, and accelerate.
  • The Wanda implementation requires cloning and installing a separate GitHub repository.
  • A small calibration dataset is needed for the Wanda pruning function to collect activation statistics.