Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install peftOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install peftβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/peft/π‘Extract and place the folder at the path above, then restart your agent.
Category
π»Developer & DevOpsPlatforms
What Peft Fine Tuning can do for your AI workflow
Parameter-efficient directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 2,056+ developers worldwide
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Peft Fine Tuning
Help me get started with Peft Fine Tuning
Explains what Peft Fine Tuning does, walks through the setup, and runs a quick demo based on your current project
Use Peft Fine Tuning to parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+...
Invokes Peft Fine Tuning with the right parameters and returns the result directly in the conversation
What can I do with Peft Fine Tuning in my developer & devops workflow?
Lists the top use cases for Peft Fine Tuning, with example commands for each scenario
Guides & tutorials for AI skills
Peft Fine Tuning extends your AI assistant with the ability to parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Peft Fine Tuning as its underlying capability.
Peft Fine Tuning works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Peft Fine Tuning once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
Getting started with Peft Fine Tuning takes about two minutes. Place the skill at `~/.claude/skills/peft/` (personal, all projects) or `.claude/skills/peft/` (project-specific), then restart your AI client. From that point, typing `/peft` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.
Peft Fine Tuning has been installed 2,056 times, making it one of the more actively used skills in the Developer & DevOps category. The install rate suggests it solves a real, recurring need rather than a niche edge case. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/peft/ for personal use (all projects), or .claude/skills/peft/ for project-specific use. Restart your AI client, then invoke with /peft or let the AI discover it automatically.
Peft Fine Tuning supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Peft Fine Tuning is free to install. Check the repository for licensing information.
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
Automate my developer & devops tasks using Peft Fine Tuning
Identifies repetitive steps in your workflow and sets up Peft Fine Tuning to handle them automatically
MCP vs traditional plugins: what's the difference?
Peft Fine Tuning is categorized under Developer & DevOps. These skills help AI agents perform specialized tasks in this domain.