You now understand transformer architecture at a practical level.
Key takeaways:
- Attention computes relevance between all positions using Q, K, V
- Multi-head attention captures different relationship types
- FFN layers store knowledge, attention routes it
- Residual connections enable deep networks
- Decoder-only with causal masking dominates modern LLMs
Next, I'll show you the infrastructure needed to train these models.