peft

Fine-tune large language models with LoRA, QLoRA, Unsloth adapters in PyTorch workflows.

Updated Aug 23, 2026

Search Tags:#adapters #lora #transformers #peft #finetuning #qlora #unsloth

Ready to run

npx skills add https://github.com/atrawog/overthink-plugins --skill peft

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: peft
Source: https://github.com/atrawog/overthink-plugins/tree/main/overthink-jupyter/skills/peft
Command: npx skills add https://github.com/atrawog/overthink-plugins --skill peft

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Parameter-efficient fine-tuning using LoRA, QLoRA, and Unsloth reduces memory and compute by training only small adapter parameters while preserving model performance.

Core Features & Use Cases

LoRA basics: train low-rank adapters on selected layers to adapt models with minimal parameter updates.
QLoRA and Unsloth: leverage quantized training and speedups for faster experimentation.
Practical workflows: domain adaptation, task-specific fine-tuning, rapid prototyping, and reversible adapter deployment.

Quick Start

Train a small LoRA adapter on your base model with your dataset and save the adapters for later loading during inference.

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 large language model with limited GPU memory?▼

You can use parameter-efficient fine-tuning (PEFT) with LoRA to train small, low-rank adapter parameters on selected layers instead of the full model, significantly reducing memory and compute requirements while preserving model performance.

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

LoRA trains low-rank adapters on selected layers to adapt models with minimal parameter updates, while QLoRA leverages 4-bit quantization during training to further reduce memory usage and enable faster experimentation on large models.

How do I configure LoraConfig for task-specific fine-tuning?▼

LoraConfig requires specifying parameters like r, lora_alpha, target_modules, lora_dropout, bias, and task_type to define how the low-rank adapters are trained during the fine-tuning process within your PyTorch and transformers workflow.

Can I use Unsloth for faster fine-tuning on 7B+ models?▼

Yes, Unsloth provides speedups for quantized training and faster experimentation when fine-tuning 7B+ models, working alongside LoRA and QLoRA to enable rapid prototyping and domain adaptation with limited memory.

How do I save and load LoRA adapters for inference?▼

After training your LoRA adapter on a base model with your dataset, you can save the adapters separately and load them later during inference, enabling reversible adapter deployment without modifying the original base model weights.

When should I use parameter-efficient fine-tuning instead of full fine-tuning?▼

Use parameter-efficient fine-tuning for domain adaptation, specialized task fine-tuning, and rapid prototyping on 7B+ models when you have limited memory and compute, as it trains only small adapter parameters while preserving model performance.