Fine-Tuning Assistant

The Fine-Tuning Assistant skill guides you through the process of adapting pre-trained models to your specific use case. Fine-tuning can dramatically improve model performance on specialized tasks, teach models your preferred style, and add capabilities that prompting alone cannot achieve.

This skill covers when to fine-tune versus prompt engineer, preparing training data, selecting base models, configuring training parameters, evaluating results, and deploying fine-tuned models. It applies modern techniques including LoRA, QLoRA, and instruction tuning to make fine-tuning practical and cost-effective.

Whether you are fine-tuning GPT models via API, running local training with open-source models, or using platforms like Hugging Face, this skill ensures you approach fine-tuning strategically and effectively.

Core Workflows

Workflow 1: Decide Whether to Fine-Tune

1. Assess the problem:
   • Can prompting achieve the goal?
   • Is the task format or style consistent?
   • Do you have quality training data?
   • Is this worth the investment?
2. Compare approaches:

   Approach	When to Use	Investment