LLM From Scratch: Building GPT From First Principles

A complete journey from text to tokens to transformers to chat assistants

Welcome to LLM From Scratch, a hands-on series that demystifies Large Language Models by building a GPT-style transformer from the ground up.

This isn’t just theory—by the end of this series, you’ll understand every component that powers systems like ChatGPT, Claude, and Llama. We’ll write the code, visualize the math, and connect the dots between research papers and real implementations.

What You’ll Build¶

Over 10 lessons, we’ll construct a complete GPT architecture:

A Byte Pair Encoding tokenizer that handles any text
Embedding layers that give words geometric meaning
The self-attention mechanism that lets words talk to each other
Multi-head attention for parallel relationship processing
The causal mask that prevents cheating during training
A complete decoder-only Transformer (the GPT architecture)
A training pipeline with modern optimizers and learning rate schedules
Inference techniques (temperature, top-p, beam search)
Fine-tuning methods (SFT and RLHF) to create chat assistants

The Series¶

Foundation: Text to Tensors¶

Lesson	Title	What You’ll Learn
L01	Tokenization From Scratch	Teaching computers to read — Build a BPE tokenizer that converts text into token IDs
L02	Embeddings & Positional Encoding	Giving numbers meaning — Transform tokens into vectors and add positional information

Core Architecture: The Attention Mechanism¶

Lesson	Title	What You’ll Learn
L03	Self-Attention: The Search Engine of Language	How words talk to each other — Implement Query-Key-Value attention and understand parallel processing
L04	Multi-Head Attention	Why eight brains are better than one — Split attention into multiple heads for richer representations
L05	Normalization & Residuals	The plumbing of deep networks — Stabilize training with LayerNorm and residual connections
L06	The Causal Mask	How to stop cheating — Prevent the model from seeing future tokens during training

Assembly and Training¶

Lesson	Title	What You’ll Learn
L07	Assembling the GPT	The grand finale — Stack all components into a complete decoder-only Transformer
L08	Training the LLM	Learning to speak — Implement the training loop with AdamW, learning rate schedules, and gradient accumulation

Inference and Fine-tuning¶

Lesson	Title	What You’ll Learn
L09	Inference & Sampling	Controlling the creativity — Generate text with temperature, top-p sampling, and beam search
L10	Fine-tuning: From Completion to Conversation	Transforming into a chat assistant — Apply SFT and RLHF to create helpful, harmless AI assistants

Prerequisites¶

This series assumes:

Python fundamentals (functions, classes, basic numpy)
High school math (algebra, basic calculus concepts)
Neural network basics (what a layer is, forward/backward pass)
- New to neural networks? Start with our Neural Networks From Scratch series!

We’ll explain everything else from scratch, including:

Matrix operations and why they matter
Backpropagation and gradient flow
PyTorch fundamentals as we go

Philosophy¶

Code First, Math Second: Every concept is implemented in PyTorch before diving into equations.

Visual Intuition: We use diagrams, plots, and animations to build intuition before formalism.

No Magic: We demystify research papers by showing the gap between “attention is all you need” and “here’s how it actually works.”

Production-Aware: We explain not just what works, but why certain choices (Pre-Norm vs Post-Norm, AdamW vs SGD) became industry standard.

How to Use This Series¶

Sequential Reading: Lessons build on each other—start from L01
Run the Code: Each lesson is an executable Jupyter notebook
Pause and Experiment: Modify parameters, break things, rebuild understanding
Skip What You Know: Familiar with embeddings? Jump ahead (but skim for our specific approach)

Ready to Begin?¶

Let’s start with the first step in any LLM pipeline: teaching computers to read.

Next: L01 - Tokenization From Scratch →

Additional Resources¶

Research Papers: Each lesson links to relevant papers (Attention is All You Need, GPT-2, etc.)
PyTorch Docs: We reference official documentation for implementation details
Further Reading: Suggested deep dives for those who want more mathematical rigor

This series is designed for engineers and researchers who want to understand LLMs deeply enough to modify, debug, and innovate on them.