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peft-fine-tuning

Fine-tune large language models up to 70B parameters using PEFT techniques.

6|3|Updated Jan 29, 2026
Search Tags:#fine-tuning#LLM#PEFT#large language models#parameter efficiency
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill peft-fine-tuning-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/peft
Command: npx skills add https://github.com/jonnabio/ace-framework --skill peft-fine-tuning-jonnabio

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 with limited GPU memory and parameter constraints, offering a suite of PEFT methods for efficient model adaptation.

Core Features & Use Cases

  • Parameter-Efficient Fine-Tuning (PEFT): Offers a range of PEFT techniques like LoRA, QLoRA, and more, enabling training of large models with minimal accuracy loss.
  • Use Cases: Ideal for scenarios where you need to fine-tune models on consumer GPUs, with limited memory, or for deploying multiple fine-tuned variants from a single base model.

Quick Start

Install PEFT and run the following command to fine-tune a Llama model with LoRA:

pip install peft transformers accelerate bitsandbytes datasets
python fine_tuning_example.py

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 large language models on consumer GPUs with limited memory?▼

This Skill applies parameter-efficient fine-tuning methods like LoRA and QLoRA to fine-tune large language models up to 70B parameters on consumer GPUs. It achieves this with minimal accuracy loss and a significantly reduced memory footprint.

What is parameter-efficient fine-tuning and when do I need it for large language models?▼

Parameter-efficient fine-tuning adapts large language models by training a minimal subset of parameters. It is needed when facing GPU memory constraints or when deploying multiple adapted variants from a single base model.

Do I need bitsandbytes and transformers to run PEFT fine-tuning?▼

Yes, PEFT fine-tuning requires the bitsandbytes and transformers libraries, alongside PEFT (>=0.13.0) and PyTorch (>=2.0.0). These dependencies provide the necessary quantization and model loading capabilities for resource-efficient training.

What is the best way to fine-tune a Llama model with LoRA?▼

Fine-tuning a Llama model with LoRA is achieved by running the provided Python scripts. After installing the required libraries like PEFT, transformers, and bitsandbytes, you can execute the script directly to adapt the model efficiently.

Are there limitations to using parameter-efficient fine-tuning for large language models?▼

While parameter-efficient fine-tuning enables training up to 70B parameters on consumer GPUs, it may incur minimal accuracy loss compared to full fine-tuning. Users must also ensure their environment strictly meets the specified library version dependencies for successful execution.

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