How this book works

Each step includes automated tests that verify your implementation before moving forward. This immediate feedback helps you catch issues early and build confidence.

You’ll first need to clone the GitHub repository and navigate to the repository:

git clone https://github.com/modular/max-llm-book
cd max-llm-book

Then download and install pixi:

curl -fsSL https://pixi.sh/install.sh | sh

To validate a step, use the corresponding test command. For example, to test Step 01:

pixi run s01

Initially, tests will fail because the implementation isn’t complete:

✨ Pixi task (s01): python tests/test.step_01.py
Running tests for Step 01: Create Model Configuration...

Results:
❌ dataclass is not imported from dataclasses
❌ GPT2Config does not have the @dataclass decorator
❌ vocab_size is incorrect: expected match with Hugging Face model configuration, got None
# ...

Each failure tells you exactly what to implement.

When your implementation is correct, you’ll see:

✨ Pixi task (s01): python tests/test.step_01.py                                                                         
Running tests for Step 01: Create Model Configuration...

Results:
✅ dataclass is correctly imported from dataclasses
✅ GPT2Config has the @dataclass decorator
✅ vocab_size is correct
# ...

The test output tells you exactly what needs to be fixed, making it easy to iterate until your implementation is correct. Once all checks pass, you’re ready to move on to the next step.

Prerequisites

This tutorial assumes:

Basic Python knowledge: Classes, functions, type hints
Familiarity with neural networks: What embeddings and layers do (we’ll explain the specifics)
Interest in understanding: Curiosity matters more than prior transformer experience

Whether you’re exploring MAX for the first time or deepening your understanding of model architecture, this tutorial provides hands-on experience you can apply to current projects and learning priorities.

Ready to build? Let’s get started with Step 01: Model configuration.