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peft-fine-tuning

Fine-tune large language models with LoRA and QLoRA on limited GPU memory.

1|1|Updated May 25, 2026
Search Tags:#fine-tuning#LLM#LoRA#PEFT#QLoRA#parameter-efficient
One-click install
npx skills add https://github.com/aayushsoam/clawbot-agent --skill peft-fine-tuning-aayushsoam
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/aayushsoam/clawbot-agent/tree/main/optional-skills/mlops/peft
Command: npx skills add https://github.com/aayushsoam/clawbot-agent --skill peft-fine-tuning-aayushsoam

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires peft>=0.13.0, transformers>=4.45.0, torch>=2.0.0, bitsandbytes>=0.43.0, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of fine-tuning large language models (LLMs) with limited GPU memory, enabling training of less than 1% of parameters with minimal accuracy loss.

Core Features & Use Cases

  • Parameter-Efficient Fine-Tuning (PEFT): Apply LoRA, QLoRA, and 25+ adapter methods to fine-tune LLMs.
  • Memory Optimization: Fine-tune 7B-70B models on consumer GPUs or single 24GB GPU.
  • Use Case: Ideal for scenarios where training with limited parameters or memory is required, such as multi-adapter serving or complex tasks with large LLMs.

Quick Start

Install the PEFT library and use it to fine-tune a Llama model with LoRA on your dataset.

Frequently Asked Questions about peft-fine-tuning

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

FAQPage Schema
How do I fine-tune a 7B large language model on a single 24GB GPU?▼

You can fine-tune large language models on a single 24GB GPU using parameter-efficient fine-tuning techniques like QLoRA. This approach trains less than 1% of parameters, enabling 7B-70B model training with minimal accuracy loss.

What is parameter-efficient fine-tuning and how does it reduce memory usage?▼

Parameter-efficient fine-tuning applies adapter methods like LoRA to train less than 1% of a model's parameters. This dramatically reduces GPU memory requirements while maintaining minimal accuracy loss compared to full fine-tuning.

Can I use PyTorch and Transformers with LoRA for multi-adapter serving?▼

Yes, this parameter-efficient fine-tuning method supports multi-adapter serving using PyTorch and Transformers. It applies 25+ adapter methods to large language models, making it ideal for complex tasks with limited memory.

What's the best way to fine-tune Llama models with limited GPU memory?▼

The best way to fine-tune Llama models with limited GPU memory is using QLoRA. This parameter-efficient technique quantizes the base model and applies LoRA adapters to train large models efficiently on consumer hardware.

Do I need specific library versions to run PEFT on large language models?▼

Yes, parameter-efficient fine-tuning requires PEFT, Transformers, and PyTorch libraries. You need specific versions including peft>=0.13.0, transformers>=4.45.0, torch>=2.0.0, and bitsandbytes>=0.43.0 for QLoRA support.

When should I not use LoRA for fine-tuning large language models?▼

You should avoid parameter-efficient fine-tuning when you have abundant GPU memory and need maximum accuracy. LoRA trains less than 1% of parameters, which introduces minimal accuracy loss unsuitable for tasks requiring full parameter updates.

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