Lesson 16 of 22Go deeperadvanced7 min

Transformer Architecture

Inside the engine: how modern AI actually processes language

What is a Transformer?

The Transformer is the architecture behind modern chat, coding, and multimodal models. Introduced in the 2017 paper “Attention Is All You Need,” it centers on self-attention: each position can mix information from other visible positions rather than only an adjacent recurrent state. Visibility still depends on the mask—for example, a causal decoder cannot attend to future tokens.

Simulated internals

Attention heatmaps, FFN bars, and top-token lists here are teaching simulations, not live model weights. Patterns are computed from simple rules so you can explore architecture without downloading a network.

The 2017 blueprint versus modern LLMs

This diagram teaches the original encoder-decoder building blocks. Many generative LLMs instead use decoder-only blocks with causal masks, and model families may choose rotary position encodings, gated feed-forward layers, or grouped-query attention. Exact IO-aware kernels such as FlashAttention reduce memory traffic; they do not remove the quadratic pairwise arithmetic of dense attention.

Input tokens →

→ Output token probabilities

Multi-Head Self-Attention

Each token looks at all other tokens to gather context

Self-Attention: Tokens gather context

For each token, the model creates three vectors: Query (what am I looking for?), Key (what do I contain?), and Value (what information should I pass along?). Scores use Q(A) · K(B) / √dₖ, then masking and softmax turn the visible scores into normalized weights. Those weights mix the Value vectors. In a text generator, a causal mask prevents a position from using future tokens.

Attention heatmap · Head 0

Future positions are masked before softmax, as in a text-generating decoder. Focuses on nearby words

Darker = stronger attention

Where “cat” is attending

The

3.4%

cat

96.6%

sat

masked

the

masked

mat

masked

Try Your Own Input

Approx. params (dense, no embed)

Attention Heads

Model Dimension

256

FFN Dimension

512

Layers

Vocabulary

10,000

Max Context

128 tokens

Key Insight

The Transformer's power comes from self-attention. Full dense attention forms an n × n score matrix, so its pairwise work grows quadratically with sequence length. Exact efficient kernels can reduce memory traffic and materialization without changing the attention function; sparse or sliding-window patterns change which pairs are computed. Residual paths and normalization make deep stacks such as this 2-layer teaching configuration practical to optimize.

Check your understanding

1/5

When can a Transformer process positions in parallel?

Sources, scope & review statusVerified Jul 29, 2026

Claims this lesson makes

Stable conceptScaled dot-product attention mixes value vectors using softmax-normalized query-key scores; causal decoders mask future positions before softmax.
Stable conceptDense attention forms a sequence-length-by-sequence-length score matrix; IO-aware exact kernels can reduce memory traffic without changing that attention function.
Stable conceptThe original encoder-decoder Transformer is a blueprint, while deployed language models may use decoder-only stacks and different positional, normalization, attention, and feed-forward choices.
Scope: The selectable configurations and heatmaps are deterministic teaching simulations, not extracted activations or complete specifications of the named models.

Primary reading

Next content review: 2027-07-29. Source links establish the lesson's claims; interactive numbers remain labeled simulations unless explicitly identified as measured data.