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. 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. A transformer block does nothing on its own. You need to turn token ids into vectors, mix in positional information, run them through the stack, and project back to vocabulary logits. Forget any one of those four steps and the model either fails to forward, drifts in position information, or cannot speak. The shape of the model also matters. The reference GPT-2 small is 124 million parameters at exactly the configuration…
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…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
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