Fine-Tuning & Model Adaptation
Learn when and how to adapt language models through fine-tuning, and when simpler methods win.
intermediate · 45 minutes
Fine-tuning adapts a pretrained model to your task, format, or style using your own examples. This course teaches you the decision framework for choosing between fine-tuning, prompt engineering, retrieval, and long context, and how to do parameter-efficient fine-tuning well. You will learn what fine-tuning changes (and, crucially, what it does not), how to prepare data, and how to evaluate results without fooling yourself.
What you'll learn
- What Fine-Tuning Actually Is
- The Decision: Fine-Tune, Prompt, RAG, or Long Context?
- Full Fine-Tuning vs Parameter-Efficient Fine-Tuning
- Instruction Tuning and Preference Tuning
- Data Quality, Quantity, and the Failure Modes
- Evaluation, Cost, and Serving in Production