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peft-fine-tuning

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

4|Updated May 14, 2026
Search Tags:#fine-tuning#LoRA#large language models#QLoRA#parameter-efficient#GPU memory optimization
One-click install
npx skills add https://github.com/liumeixin/hermes-skills --skill peft-fine-tuning-liumeixin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/liumeixin/hermes-skills/tree/main/mlops/training/peft
Command: npx skills add https://github.com/liumeixin/hermes-skills --skill peft-fine-tuning-liumeixin

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) components.

What problem does it solve?

This Skill solves the challenge of fine-tuning large LLMs with limited GPU memory, minimizing accuracy loss, and supporting multi-adapter serving.

Core Features & Use Cases

  • Parameter-Efficient Fine-Tuning: Fine-tune LLMs using LoRA, QLoRA, and 25+ methods, training <1% of parameters.
  • Memory Optimization: Designed for limited GPU memory, allowing fine-tuning of large models on consumer GPUs.
  • Multi-Adapter Serving: Deploy multiple fine-tuned variants from one base model, enhancing versatility.
  • Use Case: For data scientists and AI researchers who need to efficiently fine-tune large LLMs for specific tasks while maintaining high performance.

Quick Start

Fine-tune the Llama-3.1-8B model for a specific task using LoRA with the following command:

python train.py --model_name_or_path meta-llama/Llama-3.1-8B --train_dataset your_dataset --lora_r 16 --lora_alpha 32

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 with limited GPU memory?▼

Fine-tune large language models with limited GPU memory using parameter-efficient methods like LoRA and QLoRA, which train less than 1% of parameters to minimize memory usage while maintaining accuracy.

What's the best way to run multiple fine-tuned variants from one base model?▼

Multi-adapter serving allows you to deploy multiple fine-tuned variants from one base model, enhancing versatility and reducing deployment overhead compared to maintaining separate full models.

Do I need bitsandbytes to use QLoRA for large language model fine-tuning?▼

Yes, parameter-efficient fine-tuning with QLoRA requires the bitsandbytes library alongside peft, transformers, and torch to enable memory optimization and quantization on consumer GPUs.

Can I fine-tune the Llama-3.1-8B model on a consumer GPU using LoRA?▼

Yes, you can fine-tune the Llama-3.1-8B model on a consumer GPU using LoRA by configuring parameters like lora_r and lora_alpha to optimize memory usage and target specific tasks.

Does parameter-efficient fine-tuning cause accuracy loss compared to full fine-tuning?▼

Parameter-efficient fine-tuning minimizes accuracy loss while training less than 1% of parameters, delivering optimized performance and improved memory efficiency for specific tasks.

What are the limitations of using QLoRA for fine-tuning large language models?▼

While QLoRA optimizes GPU memory for large language models, it requires specific dependencies including peft, bitsandbytes, and torch, and is primarily suited for targeting specific tasks rather than general model adaptation.

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