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fine-tuning-llms

Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.

skills/fine-tuning-llms/SKILL.md

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# Fine-Tuning LLMs

Fine-tuning adapts a pretrained model to your task, domain, or style. Done well, it's the 
highest-leverage way to get reliable specialized behavior; done poorly, it's an expensive way to 
degrade a good model. The data matters more than the hyperparameters.

## Overview

The pipeline: curate high-quality demonstrations of desired behavior, choose the tuning method 
(full fine-tuning for maximum adaptation, LoRA/QLoRA for efficiency), train with careful 
hyperparameters, and evaluate against the base model on task-specific benchmarks. Most fine-tuning 
projects fail on data quality, not on training mechanics — a thousand excellent examples beat a 
million mediocre ones.