Build a Transformer from Scratch
From raw text to generated output: build a working transformer language model step by step in pure Python.
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Step 1: Setup & Data
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Step 2: Tokenizer
Build a simple word-level tokenizer with encode/decode methods.
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Step 3: Embeddings
Create an embedding layer that converts token IDs to dense vectors.
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Step 4: Single-Head Attention
Implement the core QKV attention mechanism.
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Step 5: Multi-Head Attention
Run multiple attention heads in parallel and concatenate.
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Step 6: Transformer Block
Combine attention, feedforward, residual connections, and layer normalization.
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Step 7: Text Generation
Build autoregressive generation - predict one token at a time.
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Step 8: Temperature & Top-p Sampling
Add temperature and nucleus sampling for controlled generation.
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What You Built:
- A tokenizer that converts text to integers and back
- An embedding layer that gives tokens vector meaning
- Single-head and multi-head attention mechanisms
- A full transformer block with residual connections and normalization
- Autoregressive text generation
- Temperature and top-p sampling for controlled output
In production, these same components are scaled to billions of parameters and trained on trillions of tokens.