AI Engineering/Phase 10/Lesson 06

Instruction Tuning (SFT)

A base model predicts the next token. That's it. It doesn't follow instructions, answer questions, or refuse harmful requests. SFT is the bridge between a token predictor and a useful assistant. Every model you've ever talked to -- Claude, GPT, Llama Chat -- went through this step.

Build 1.5 hr Python (with numpy)

Prerequisites: Phase 10, Lesson 04 (Pre-Training a Mini GPT)

#Learning Objectives

Implement supervised fine-tuning (SFT) that converts a base language model into an instruction-following assistant
Format training data using chat templates with system, user, and assistant roles, and mask loss on non-assistant tokens
Explain why SFT is necessary: base models continue text rather than answer questions
Evaluate SFT quality by comparing base model vs fine-tuned model responses on a held-out instruction set

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.