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peft-fine-tuning

Fine-tune large language models with LoRA and QLoRA adapters.

2|Updated Feb 4, 2026
Search Tags:#llm#fine-tuning#adapter#lora#peft#qlora
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill peft-fine-tuning-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/peft
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill peft-fine-tuning-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires peft, transformers, torch, bitsandbytes, datasets, accelerate, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of fine-tuning large language models (LLMs) on consumer hardware by enabling parameter-efficient fine-tuning (PEFT) techniques like LoRA and QLoRA.

Core Features & Use Cases

  • Memory Optimization: Fine-tune 7B-70B models on GPUs with limited VRAM (e.g., 24GB).
  • Parameter Efficiency: Train less than 1% of model parameters, drastically reducing training time and storage.
  • Multi-Adapter Serving: Deploy multiple fine-tuned variants from a single base model efficiently.
  • Use Case: Adapt a large pre-trained LLM to a specific domain (e.g., legal documents, medical texts) or a particular task (e.g., summarization, code generation) without needing massive computational resources.

Quick Start

Install the necessary libraries and then use the provided Python code to apply LoRA to a base model.

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 LLM with limited GPU memory?▼

Fine-tune a 7B LLM with limited GPU memory by applying parameter-efficient methods like LoRA or QLoRA to train less than 1% of parameters, fitting models on 24GB VRAM.

What is the difference between LoRA and QLoRA for fine-tuning?▼

LoRA and QLoRA are both parameter-efficient fine-tuning techniques. QLoRA further optimizes memory by quantizing the base model, enabling fine-tuning of 70B parameter models on constrained GPUs.

Can I use HuggingFace transformers with PEFT for adapter training?▼

Yes, you can use HuggingFace transformers with PEFT. The skill integrates directly with the ecosystem, allowing you to apply adapter techniques and deploy multiple fine-tuned variants from a single base model.

What's the best way to adapt a pre-trained model for specific domain tasks?▼

Adapt a pre-trained model for specific domain tasks like summarization or code generation by fine-tuning it with PEFT, which drastically reduces training time and storage while preserving accuracy.

Does multi-adapter serving work with large language models?▼

Multi-adapter serving works with large language models by deploying multiple fine-tuned variants efficiently from a single base model, maximizing resource utilization without duplicating the entire model.

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