model-fine-tuning

Fine-tune open-source LLMs with LoRA/QLoRA adapters and prepared datasets.

3|Updated May 28, 2026

Search Tags:#llm #lora #datasets #model-evaluation #finetuning #qlora

Ready to run

npx skills add https://github.com/mahg-es/araya --skill model-fine-tuning-mahg-es

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: model-fine-tuning
Source: https://github.com/mahg-es/araya/tree/main/skills/model-fine-tuning
Command: npx skills add https://github.com/mahg-es/araya --skill model-fine-tuning-mahg-es

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fine-tune open-source LLMs to create domain-specific models using LoRA/QLoRA and dataset preparation.

Core Features & Use Cases

Fine-tuning with LoRA/QLoRA adapters to optimize parameters without full model retraining.
Dataset preparation, augmentation, and evaluation to measure improvements against baselines.
Deployment guidance for integrating tuned adapters into local or edge deployments.

Quick Start

Configure a LoRA-based fine-tuning workflow on a given dataset to adapt an open-source LLM.

Frequently Asked Questions about model-fine-tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I fine-tune an open-source LLM for a domain-specific task?▼

To fine-tune an open-source LLM, you can configure a LoRA or QLoRA-based workflow to adapt the model on a specific dataset, optimizing parameters without full model retraining.

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

LoRA and QLoRA are adapter-based fine-tuning techniques that optimize parameters without full model retraining. QLoRA further reduces memory usage by quantizing the base model during training.

How do I prepare datasets for LLM fine-tuning?▼

Dataset preparation for LLM fine-tuning involves formatting, augmenting, and evaluating your data to measure improvements against baselines before applying LoRA or QLoRA adapters.

Can I deploy fine-tuned LoRA adapters locally?▼

Yes, you can deploy fine-tuned LoRA adapters locally or to edge deployments. The workflow provides deployment guidance for integrating tuned adapters into your target environment.

Do I need to retrain the entire model when fine-tuning with LoRA?▼

No, fine-tuning with LoRA or QLoRA adapters allows you to optimize parameters efficiently without the need for full model retraining, making scalable customization of base models possible.