The Illustrated GPT-2 (Visualizing Transformer LMs)

by Jay Alammar

IntermediateGuideFree~40 min read

See exactly how a decoder-only GPT generates text, one token at a time.

Start LearningReviewed July 3, 2026

Overview

A natural follow-up to The Illustrated Transformer, this guide focuses on decoder-only models like GPT-2 (the same family as today's chat models). It visualizes masked self-attention, how the model processes a prompt, and how it generates text autoregressively token by token. If the general transformer explainer left you wondering how generation actually works, this fills the gap.

At a Glance

Topic
Models
Level
Intermediate
Format
Guide
Cost
Free
Duration
~40 min read
Provider
Jay Alammar
Hands-on
No
Certificate
None

What You’ll Learn

  • Decoder-only (GPT-style) architecture
  • Masked self-attention
  • Autoregressive text generation
  • How prompts flow through the model

Highlights

  • Focuses on the GPT/decoder-only family
  • Same clear illustrated style

Who It’s For

Best For

  • Learners who want to understand text generation

Prerequisites

  • The Illustrated Transformer or equivalent

FAQ

What is The Illustrated GPT-2 (Visualizing Transformer LMs)?

Jay Alammar's illustrated guide to GPT-2 and decoder-only language models — masked self-attention and autoregressive generation.

Is The Illustrated GPT-2 (Visualizing Transformer LMs) free?

The Illustrated GPT-2 (Visualizing Transformer LMs) is free to access.

What level is The Illustrated GPT-2 (Visualizing Transformer LMs) for?

The Illustrated GPT-2 (Visualizing Transformer LMs) is aimed at a intermediate audience. Recommended background: The Illustrated Transformer or equivalent.

How long does The Illustrated GPT-2 (Visualizing Transformer LMs) take?

Expect roughly ~40 min read. Most learners work through it at their own pace.

What will I learn from The Illustrated GPT-2 (Visualizing Transformer LMs)?

You'll learn: Decoder-only (GPT-style) architecture; Masked self-attention; Autoregressive text generation; How prompts flow through the model.

Topics

GPT-2decoder-onlyself-attentiongeneration