← BREAF Academy

Beginner track · 24 lessons

Build an LLM From Scratch

Start at y = mx + c. Finish with your own model running an agent.

Twenty-four lessons, zero to hero: start at a single line of best fit and end with a small language model you trained yourself, wired into a working AI agent. Every idea is built by hand before it's named — no assumed math, no black boxes.

What you'll walk away with

  • ·Train a tiny language model on real text, from raw gradients to a working GPT
  • ·Understand attention, transformers, and fine-tuning from first principles — no black boxes
  • ·Fine-tune, align (DPO), and run inference on your own model
  • ·Build a working AI agent — tools, memory, and RAG — on top of a model you trained yourself

BREAF Premium

This lesson is for Premium

The full lesson — notes, practice, and progress — is waiting on the other side. Every course reaches Premium members first.

Already a member? Sign in

Part 1 — The Neuron

  1. A line is a modelFree previewYou already learned the core equation of machine learning in school. Nobody told you that's what it was — and it's the same equation, at any scale, all the way up to GPT.50 min
  2. Learning is just rolling downhillComing
  3. Why straight lines aren't enoughComing
  4. Stacking neurons into a networkComing
  5. Backpropagation: assigning blameComing
  6. Build a neural net in raw PythonComing

Part 2 — Teaching a Machine Language

  1. Text → numbers: tokenisationComing
  2. Embeddings: meaning as directionComing
  3. Your first language modelComing
  4. The memory problemComing

Part 3 — The Transformer

  1. Attention, from first principlesComing
  2. Queries, keys, values — and maskingComing
  3. Multi-head attention and positionComing
  4. The full transformer blockComing
  5. Build GPT from scratchComing

Part 4 — Training Your Own SLM

  1. Data is the modelComing
  2. The pretraining runComing
  3. Fine-tuning and instruction tuningComing
  4. Alignment: RLHF and DPOComing
  5. Inference: how generation really worksComing

Part 5 — The Agent

  1. What an agent actually isComing
  2. Tools: giving the model handsComing
  3. Memory, context and planningComing
  4. Your agent, on your modelComing