Module 7Transformer Deep Dive

Decoder Models and GPT Thinking

Build causal next-token predictors that generate text autoregressively.

Why this module matters

Modern LLM systems are largely decoder-first, so this mental model matters enormously.

Prerequisites

▸ Attention and masking

Learning objectives

▸ Understand causal masks
▸ Train tiny autoregressive models
▸ Interpret sampling behavior

Core concepts

Next-token prediction

Autoregressive generation

KV caching basics

Hands-on practice

▸ Train a tiny character-level decoder and sample outputs

Expected output

A small GPT-style model that generates toy text.

Study checklist

✅ Understand causal masks
✅ Train tiny autoregressive models
✅ Interpret sampling behavior

Common mistakes

⚠️ Masking errors that silently leak future tokens
⚠️ Judging model quality from one sample only

Module rhythm

1. Read the summary and why-it-matters section first.
2. Work through concepts before rushing into practice.
3. Use the checklist to verify real understanding, not just completion.

How to continue

Next comes the training recipe that turns toy models into stable runs.

Back to course overview →

How to use this page well

Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.