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peft

Fine-tune large language models by training a small subset of parameters.

Updated Apr 19, 2026
Search Tags:#llm#adapter#lora#memory-efficient#peft#finetuning#qlora
One-click install
npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill peft-chenyiru3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft
Source: https://github.com/CHENyiru3/AI-Skills-Collections/tree/main/skills-market/ai-ml/llm/peft
Command: npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill peft-chenyiru3

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

PEFT enables efficient fine-tuning of large language models by training only a small subset of parameters, reducing memory and compute requirements.

Core Features & Use Cases

  • LoRA, QLoRA, and adapters for memory-efficient fine-tuning on GPU-constrained setups.
  • Supports multiple parameter-efficient methods (prefix tuning, prompt tuning) for quick domain adaptation.
  • Use Case: fine-tune a GPT-2 or LLaMA model on a specialized corpus with limited hardware.

Quick Start

Install and run a small LoRA experiment to adapt a model to your domain with minimal resources.

Frequently Asked Questions about peft

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a LLaMA model with limited GPU memory?▼

You can fine-tune large language models like LLaMA in memory-constrained environments by training only a small subset of parameters using PEFT methods such as LoRA and QLoRA.

What is the difference between LoRA and QLoRA for memory-efficient finetuning?▼

Both LoRA and QLoRA enable memory-efficient finetuning by training small parameter subsets, but QLoRA specifically supports k-bit training options to further reduce memory requirements on GPU-constrained setups.

Can I use adapter and prefix-prompt tuning for quick domain adaptation?▼

Yes, PEFT supports multiple parameter-efficient methods including adapters, prefix tuning, and prompt tuning to achieve quick domain adaptation for large language models.

Do I need the transformers library to run LoRA experiments on GPT-2?▼

Yes, running LoRA experiments on models like GPT-2 requires libraries such as transformers and peft, integrating with common training pipelines to adapt the model to your domain.

When should I use parameter-efficient finetuning instead of full model training?▼

Use parameter-efficient finetuning when you need to adapt large models on specialized corpora with limited hardware, as it trains only a small subset of parameters to reduce memory and compute requirements.

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