Module 11 Wrap-up: The Model Builder

You have completed the transition from a "Model User" to a "Model Builder." You understand that Fine-Tuning isn't magic—it is a technical process of teaching style, format, and logic through structured data.

The Workflow Summary

Define: Is this a Style problem (Fine-tune) or a Fact problem (RAG)?
Dataset: Create 100-500 high-quality JSONL examples.
Train: Use Unsloth or MLX to generate a 50MB LoRA Adapter.
Integrated: Attach the Adapter to any base model in an Ollama Modelfile.
Run: Chat with your specialized AI expert.

When to Stop Fine-Tuning

It is easy to get obsessed with training. But remember the "Engineering Paradox":

Prompting takes 5 minutes to test.
RAG takes 1 hour to build.
Fine-tuning takes 1 day to prepare and 1 hour to train.

Always try to solve your problem with Prompting first, then RAG, and only use Fine-Tuning as a last resort.

Module 11 Summary

LoRA is the most efficient way to customize model behavior locally.
Data quality is the most important factor in a successful training run.
Unsloth and MLX are the tools of choice for the local AI community.
The ADAPTER command in Ollama makes using your custom weights easy.

Coming Up Next...

In Module 12, we leave the "Engineering" side and enter the "Enterprise" side. We will look at Security and Compliance—how to ensure your local AI setup is actually safe and how to handle sensitive data in a corporate environment.

Module 11 Checklist

I can explain why RAG is usually better for "facts" than fine-tuning.
I understand that a LoRA adapter is small and doesn't change the base model.
I have seen a JSONL file and understand the instruction/output format.
I know which training tool to use for my specific computer (NVIDIA vs Mac).
I can write the ADAPTER line in a Modelfile.

Module 11 Wrap-up: The Fine-Tuning Journey