Yash DabhadeAI Engineer

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Build a Large Language Model (From Scratch)

Sebastian Raschka · AI

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Takeaways

The book that made transformers stop feeling like magic. You write every piece of a GPT-style model in PyTorch, then pre-train and fine-tune it yourself.

What You Build, Chapter by Chapter

  1. raw text
  2. tokenizer (BPE) + data loader
  3. self-attention → multi-head causal attention
  4. GPT model
  5. pre-train
  6. fine-tune: classifierfine-tune: instructions
Everything is small enough to run on a laptop, and you load real GPT-2 weights at the end.

Notes

  • Writing the sliding-window data loader by hand made next-token prediction concrete — input and target are the same text, shifted by one.
  • The causal mask is one line of code and the whole difference between "encoder" and "decoder".
  • Weight tying, layer norm placement, GELU — small details you normally never see, all explained.
  • Pairs perfectly with reading "Attention Is All You Need" and the GPT-3 paper. The book gives the code, the papers give the why.

If I had to teach someone LLMs from zero, this is the book I would hand them.