Phase 2: Pre-training & Fine-tuning · ~120 minutes · Python

Build a Transformer from Scratch — The Capstone

Thirteen lessons. One model. No shortcuts.

What you will learn

Implement build a transformer from scratch — the capstone from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build
Languages: Python
Prerequisites: Earlier lessons in this course
Time: ~120 minutes

The Problem

You've read every paper. You've implemented attention, multi-head splits, positional encodings, encoder and decoder blocks, BERT and GPT losses, MoE, KV cache. Now make them work together on a real task.

The capstone: train a small decoder-only transformer end-to-end on a character-level language modeling task. It reads Shakespeare. It generates new Shakespeare. It is small enough to train on a laptop in under 10 minutes. It is correct enough that swapping in a bigger dataset and longer training gets you a real LM.

This is the "nanoGPT" of the course. It is not original — Karpathy's 2023 nanoGPT tutorial is the reference implementation every student writes at least once. We lift the shape and retool it around what we've covered.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.