AI Engineering/Phase 19/Lesson 35

GPT Model Assembly

Twelve blocks stacked, a token embedding, a learned position embedding, a final LayerNorm, and a tied language model head. That is the entire 124 million parameter GPT model. This lesson assembles those pieces into a working class, counts the parameters to confirm the model matches the reference 124M shape, and generates text with multinomial sampling, temperature, and top-k.

Build 1.5 hr Python

Prerequisites: Phase 19 lessons 30 to 34

#Learning Objectives

Assemble the transformer block from lesson 34 into a full GPT model: token embedding, position embedding, N blocks, final LayerNorm, language model head.
Reproduce the 124 million parameter configuration: vocab 50257, context 1024, embedding 768, twelve heads, twelve layers.
Tie the language model head weights to the token embedding and explain why that saves ~38 million parameters at this scale.
Generate text from a prompt with multinomial sampling, temperature scaling, and top-k truncation, holding context length with a sliding window.
Measure parameter count and forward pass cost against the 124M target.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.