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peft-fine-tuning

Fine-tune large language models with LoRA and QLoRA using less than 1% of parameters.

1|1|Updated May 9, 2026
Search Tags:#fine-tuning#LLM#LoRA#PEFT#QLoRA#parameter-efficient
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill peft-fine-tuning-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/optional-skills/mlops/peft
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill peft-fine-tuning-ldzhhxx

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 addresses the challenge of fine-tuning large language models (LLMs) with limited GPU memory, where minimal accuracy loss is crucial while training less than 1% of the model's parameters.

Core Features & Use Cases

  • Parameter-Efficient Fine-Tuning: Fine-tune LLMs using LoRA, QLoRA, and 25+ adapter methods with minimal accuracy loss.
  • Use Cases: Ideal for fine-tuning large models (7B-70B) on consumer GPUs, multi-adapter serving, and when training less than 1% of parameters is required.
  • Applications: Suitable for scenarios where memory is a constraint, such as training on a single 24GB GPU, or when deploying multiple fine-tuned variants from one base model.

Quick Start

Install the Skill with pip install peft, then use it to fine-tune a model with peft.fine_tune(model, lora_config, 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 large language model with limited GPU memory?▼

Fine-tune large language models with limited GPU memory using parameter-efficient fine-tuning methods like LoRA and QLoRA, which train less than 1% of parameters to achieve minimal accuracy loss on consumer GPUs.

What is parameter-efficient fine-tuning and when should I use it?▼

Parameter-efficient fine-tuning adapts large language models by training under 1% of their parameters using adapter methods. Use it when GPU memory is constrained, such as training 7B-70B models on a single 24GB GPU.

Can I fine-tune a 7B model on a single 24GB GPU using LoRA?▼

Yes, you can fine-tune 7B-70B large language models on a single 24GB GPU using QLoRA and LoRA adapter methods, which require training less than 1% of model parameters with minimal accuracy loss.

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

Use parameter-efficient fine-tuning to create multiple LoRA or QLoRA adapters from a single base model. This approach allows multi-adapter serving while training less than 1% of parameters for each variant.

Do I need bitsandbytes and torch to apply QLoRA to LLMs?▼

Yes, applying QLoRA to large language models requires the bitsandbytes, torch, and transformers libraries to facilitate parameter-efficient training with minimal accuracy loss on limited hardware.

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