Transformer Architecture Explained: Self-Attention, Encoders & Decoders
How the Transformer works: self-attention, positional encodings (incl. RoPE), feed-forward layers, and normalization patterns explained with diagrams and code.
By AI Mastermind Lab · Published 1 Mar 2026
Transformers replace recurrence with attention, letting every token attend to every other. This section‑by‑section reference clarifies the moving parts.
Self‑attention: compute Q, K, V projections; use scaled dot‑product attention with softmax; combine multiple heads to capture diverse relations.
Positions: compare sinusoidal, learned, and rotary embeddings. Note how RoPE improves extrapolation and is widely used in 2025–2026 LLMs.
MLP blocks: GELU‑based two‑layer networks carry much of the model capacity; discuss gating and SwiGLU variants.
Norm/residual: pre‑norm vs post‑norm trade‑offs; stability considerations; why pre‑norm dominates modern stacks.
Training notes: scaling, token mixing, and optimizer choices (AdamW, LAMB). Include tips on gradient clipping and activation checkpointing.