The Complete Transformer Architecture

Begin with the central question

How do embeddings, attention, FFNs, residuals, and normalization become one complete model?

Essential words

A model architecture specifies how components connect. An encoder stack builds representations of available input. A decoder stack generates while obeying causal constraints and may cross-attend to encoder output.

What You Will Understand

The complete Transformer assembled end to end: text through tokenization, embeddings, positional information, and a full stack of Transformer blocks, verified with a real 4-layer computation. Then an overview of the three major architecture families — encoder-only, decoder-only, encoder-decoder — and what each is suited for.

tokens -> embeddings/positions -> repeated blocks -> task output

The problem this module solves

Modules 2-11 built every piece in isolation: input processing, one attention computation, one full block. This module exists purely to assemble everything into the complete picture, and to introduce the architecture-level choice (encoder vs. decoder vs. both) that Module 13 then examines in depth.

Build the intuition

you’ve built one room of a building (the Transformer block, Module 9) in complete detail. This module builds the whole building — the same room design, repeated floor after floor, with text entering at the ground level and a final, richly contextual representation emerging at the top.

Analogy: The Stacked Floor Skyscraper Assembly Think of assembling a complete Transformer architecture like building a 96-story skyscraper:

The Ground Floor (Input Processing): Human visitors enter the lobby, get registered (Token ID lookup), and get pinned with a floor access badge stamp (Positional Encoding).

The Repeating Floors (The Stack of Blocks): Floors 1 through 96 use the exact same room blueprints (Transformer blocks). Visitors walk out of the elevator, discuss their plans in the conference room (Attention communication), return to their desk to write up updates (FFN transformation), adjust their posture (Normalization), and take the elevator up to the next floor.

The Observation Deck (The Head Output): By the top floor, the raw passenger has been contextualized by 96 levels of revisions. The final team uses this rich consensus state to output decisions (the classification logits or next-token predictions).

📊 Visual Flowchart: End-to-End Complete Transformer Stack

Here is the global information pipeline from raw input characters to the final logits head:

graph TD
    classDef block fill:#3498db,stroke:#333,stroke-width:1px,color:#fff;
    classDef head fill:#f1c40f,stroke:#333,stroke-width:1px,color:#fff;




    Text["Raw Text String"] --> InputPrep["1. Input Embeddings + Position Coding"]




    subgraph BlockStack ["N-Layer Repeating Transformer Blocks"]
        InputPrep --> Block0["Transformer Block 1"]:::block
        Block0 --> Block1["Transformer Block 2"]:::block
        Block1 --> BlockN["Transformer Block N"]:::block
    end




    BlockN --> LMHead["2. Output Layer: LM Head (Linear Layer)"]:::head
    LMHead --> Logits["3. Logits (Vocabulary Size scores)"]
    Logits --> Softmax["4. Softmax Selector"]
    Softmax --> NextToken["5. Next Token Choice"]

4. Core Concept

Text
  ↓
Tokenizer                (Module 2)
  ↓
Token IDs                 (Module 2)
  ↓
Embedding                  (Module 2)
  ↓
Positional Information      (Module 8)
  ↓
Transformer Block            (Module 9)
  ↓
Transformer Block
  ↓
Transformer Block
  ↓
...  (repeated N times — a real model might use dozens)
  ↓
Final Representation

The three major architecture families

Encoder-only (e.g., BERT-style):
  - FULL (non-causal) self-attention -- every position sees
    every other position, including LATER ones
  - Best suited for UNDERSTANDING a complete, already-available
    input (classification, extracting information)




Decoder-only (e.g., GPT-style, modern LLMs):
  - CAUSAL self-attention -- each position sees only itself
    and EARLIER positions (Module 6)
  - Best suited for GENERATING text, one token at a time




Encoder-decoder (e.g., T5-style):
  - An ENCODER processes the full input with non-causal attention
  - A DECODER generates output with causal self-attention PLUS
    CROSS-attention to the encoder's output
  - Best suited for SEQUENCE-TO-SEQUENCE tasks with a clear
    distinct input and output (e.g., translation, summarization)

5. How It Works — Step by Step

1. Raw text is tokenized (Module 2)
2. Token IDs are looked up in the embedding matrix (Module 2)
3. Positional information is added (Module 8)
4. The result enters the FIRST Transformer block (Module 9)
5. That block's output becomes the SECOND block's input
6. This repeats for however many blocks the model has (N layers)
7. The FINAL block's output is the model's "final representation"
   -- a richly contextual vector per token position, ready for
   whatever comes next: a classification head (encoder-only), a
   next-token prediction head (decoder-only, Module 14), or
   further processing by a decoder (encoder-decoder)

6. Mathematical Intuition

Read the mathematics as a story

Stacking works because every block accepts and returns the same outer shape. The next block receives refined token states, not a different data type.

token states (n,d) -> block 1 (n,d) -> block 2 (n,d) -> ... -> output

Nothing new mathematically — this module is pure assembly. The one thing worth being precise about: every block’s output shape matches its input shape (Module 9), which is exactly what makes stacking N blocks a simple, uniform repetition rather than requiring different logic at each layer.

7. Small Worked Example

Walk through the example

Start with three token vectors. 2. Run one complete block. 3. Feed its output into the next block. 4. Verify shape stays constant while values evolve.

Stacking 4 Transformer blocks on a 3-token, d_model=4 input: each block receives the previous block’s full output and produces a new (3, 4) representation.

By the 4th block, the representation has been shaped by 4 rounds of attention (gathering context from other tokens) and FFN (independent per-token transformation) — a genuinely deeper, more contextual representation than what any single block alone could produce.

8. Python / NumPy Example

What the code will demonstrate

This small NumPy example makes The Complete Transformer Architecture visible with inspectable numbers and shapes. Read it in three passes: identify each input, follow the transformation line by line, and connect the printed output to the diagram above. The arrays are intentionally tiny teaching values; unless the text explicitly says otherwise, they are not weights or measurements from GPT, Gemini, or another trained model.

# The arrays are intentionally small so each transformation can be inspected.
# Printed values illustrate the mechanism; they are not trained-model measurements.
import numpy as np




def softmax(x, axis=-1):
    exp_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
    return exp_x / np.sum(exp_x, axis=axis, keepdims=True)




def layer_norm(x, eps=1e-8):
    mean = x.mean(axis=-1, keepdims=True)
    std = x.std(axis=-1, keepdims=True)
    return (x - mean) / (std + eps)




def relu(x): return np.maximum(0, x)




np.random.seed(11)
seq_len, d_model, d_ff = 3, 4, 8




def transformer_block(x, seed):
    rng = np.random.RandomState(seed)
    Wq = rng.randn(d_model, d_model) * 0.3
    Wk = rng.randn(d_model, d_model) * 0.3
    Wv = rng.randn(d_model, d_model) * 0.3
    Wo = rng.randn(d_model, d_model) * 0.3
    Q, K, V = x @ Wq, x @ Wk, x @ Wv
    scores = Q @ K.T / np.sqrt(d_model)
    attn_out = (softmax(scores, axis=-1) @ V) @ Wo
    x = layer_norm(x + attn_out)




    W1 = rng.randn(d_model, d_ff) * 0.3
    W2 = rng.randn(d_ff, d_model) * 0.3
    ffn_out = relu(x @ W1) @ W2
    x = layer_norm(x + ffn_out)
    return x




X = np.round(np.random.randn(seq_len, d_model) * 0.5, 3)
print("Input to the full stack:\n", X, "\n")




num_layers = 4
x = X
for layer_idx in range(num_layers):
    x = transformer_block(x, seed=layer_idx)
    print(f"After Transformer block {layer_idx + 1}: shape={x.shape}, row 0 = {np.round(x[0], 4)}")




print("\nFinal representation:\n", np.round(x, 4))
print("\nTotal weight matrices used: 4 blocks x 6 matrices each =", num_layers * 6)

Expected Output:

Input to the full stack:
 [[ 0.875 -0.143 -0.242 -1.327]
 [-0.004 -0.16  -0.268  0.158]
 [ 0.211 -0.533 -0.443 -0.238]]




After Transformer block 1: shape=(3, 4), row 0 = [ 1.3351  0.3104 -0.2035 -1.4421]
After Transformer block 2: shape=(3, 4), row 0 = [ 1.504  -0.0214 -0.1764 -1.3063]
After Transformer block 3: shape=(3, 4), row 0 = [ 1.4173  0.2106 -0.2563 -1.3716]
After Transformer block 4: shape=(3, 4), row 0 = [ 1.7005 -0.2631 -0.7731 -0.6643]




Final representation:
 [[ 1.7005 -0.2631 -0.7731 -0.6643]
 [ 0.8505 -1.1593 -0.8165  1.1252]
 [ 1.7062 -0.8404 -0.496  -0.3697]]




Total weight matrices used: 4 blocks x 6 matrices each = 24

9. How It Works

The shape stays exactly (3, 4) after every single block, all 4 layers deep — direct confirmation of Module 9’s “output shape matches input shape” property, now demonstrated across a genuine multi-layer stack, not just one block.
Each block’s output is visibly different from the last (row 0’s values genuinely change block to block) — the representation is actively being refined by each additional layer of attention and FFN processing, not converging to a fixed point or degrading (Module 11 showed what degradation without residuals would look like).
24 total weight matrices (Wq, Wk, Wv, Wo, W1, W2 × 4 blocks) is a small, concrete instance of the parameter-counting exercise from DL Module 2 — real models simply have vastly more blocks, each with vastly larger matrices.

10. How Is This Used in Modern AI?

Where this concept lives

Follow the concept at three levels: inside the model, where the computation happens; inside the AI product, where that computation supports a visible feature; and inside production, where engineers measure speed, memory, quality, and failure cases. The details below connect those levels.

🤖 How Is This Used in Modern AI?

A real LLM’s architecture description (e.g., “32 layers, d_model = 4096”) is describing precisely this: how many times this exact block is stacked, and how wide each layer’s representations are. Nothing architecturally new happens beyond what you’ve now built and verified — only scale changes.

Family	Real-world examples	Best suited for
Encoder-only	BERT and BERT-family models	Classification, understanding tasks, extracting structured information from text
Decoder-only	GPT-family, Claude, and most modern general-purpose LLMs	Open-ended text generation
Encoder-decoder	T5, and translation-focused models	Sequence-to-sequence tasks with a clear source and target

11. How Is This Used in LLMs?

Trace one model call

User text → tokens → Transformer computation → output-token probabilities
          this topic affects one part of that computation

An LLM does not apply this idea as a separate magic step. It uses it as part of the repeated numerical pipeline that transforms token vectors and produces the next-token probabilities.

Virtually every LLM you’ll build applications with is decoder-only (Module 13 covers exactly why this became the dominant choice for general-purpose LLMs). The full stack you just built and verified — with causal masking (Module 6) instead of full attention — is architecturally what these models are, just at a scale of dozens of layers and thousands-dimensional representations.

Real systems you can recognize

The original Transformer was an encoder-decoder translation model. BERT popularized encoder-only use, while GPT-style LLMs use decoder-only stacks. Hugging Face supports all three families through task-specific model classes.

12. How Is This Used in Agentic AI?

Trace one agent step

Goal + history + tool results
              ↓
     LLM processes the context
              ↓
Suggested answer or tool call
              ↓
Agent runtime validates and executes it

This distinction matters: the Transformer helps produce the proposal, while the surrounding agent software controls tools, permissions, retries, memory, and execution.

Direct relevance to Agentic AI: High. Every agent’s reasoning capability comes from exactly this stacked architecture, running as a decoder-only model.

Understanding that “the model” is fundamentally this repeated block structure — not a mysterious black box — is what lets you reason sensibly about an agent’s capabilities and limitations (e.g., why longer context requires processing through every layer for every token, a cost consideration Module 17 covers directly).

When this knowledge is useful

Use The Complete Transformer Architecture when you need to explain, implement, debug, evaluate, or optimize the corresponding part of a Transformer pipeline. It is also useful when a model API behaves unexpectedly and you need to trace the behavior back to tokens, tensor shapes, attention visibility, training, or inference mechanics.

When it is not enough

Understanding this mechanism does not by itself prove that a complete model or application is accurate, safe, fast, or cost-effective. Production decisions still require representative evaluation data, latency and memory measurements, model-specific documentation, and tests of the surrounding retrieval or agent code.

13. Common Beginner Mistakes

⚠️ Mistake

Incorrect idea: assuming later blocks do something fundamentally different from earlier blocks.

Why it is incorrect: Every block has the identical structure (Module 9) — what differs is each block’s independently learned parameters, and the fact that later blocks operate on increasingly refined representations from earlier blocks, not that they’re built differently.

⚠️ Mistake

Incorrect idea: assuming decoder-only is “the” Transformer architecture and encoder-only/encoder-decoder are obscure variants.

Why it is incorrect: All three are genuine, real architecture families with real use cases — decoder-only simply became dominant specifically for general-purpose LLMs (Module 13 explains precisely why).

⚠️ Mistake

Incorrect idea: thinking model “depth” (number of layers) is the only thing that matters for capability.

Why it is incorrect: Width (d_model), number of attention heads (Module 7), and FFN inner dimension (Module 10) all contribute to total capacity alongside depth.

14. Important Distinctions

Encoder-Only	Decoder-Only
Full, non-causal self-attention	Causal self-attention only (Module 6)
Sees the entire input at once	Can only see itself and earlier positions
Good for understanding/classification	Good for generation

Decoder-Only	Encoder-Decoder
No separate encoder, no cross-attention	Separate encoder + decoder, connected via cross-attention
One unified sequence, causally processed	Distinct source and target sequences

15. Production / Engineering Considerations

Model depth and width are both real cost drivers — more layers and larger d_model both increase parameter count, memory footprint, and compute cost for both training and inference.
Architecture family choice affects what a model is naturally suited for — using an encoder-only model for open-ended generation, or a pure decoder-only model for a task needing genuinely bidirectional understanding of a fixed input, works against the architecture’s natural strengths.

16. Interview Questions

Beginner

Q: What’s the complete path from raw text to a Transformer’s final representation?

Ans: Text is tokenized into tokens, converted to token IDs, looked up in an embedding matrix to get token embeddings, combined with positional information, then passed through a stack of Transformer blocks — each identical in structure but with its own learned parameters — producing a final, contextual representation per token position.

Intermediate

Q: What are the three major Transformer architecture families, and what is each best suited for?

Ans: Encoder-only (like BERT), which uses full, non-causal attention and is well-suited for understanding a complete input, such as classification tasks. Decoder-only (like GPT-style models and most modern LLMs), which uses causal attention and is well-suited for generating text one token at a time.

Encoder-decoder (like T5), which uses a separate encoder for the input and a decoder with cross-attention for generating output, well-suited for sequence-to-sequence tasks with a clear distinct source and target, like translation.

Advanced

Q: Why does stacking N identical Transformer blocks work as a simple, uniform repetition, rather than requiring custom logic at each layer?

Ans: Because every Transformer block’s output shape exactly matches its input shape (verified directly in Module 9 and again across this module’s 4-layer stack) — this shape consistency means block 2 can take block 1’s output directly as input with zero adaptation needed, and this holds for however many blocks are stacked.

Each block has independently learned parameters, but the architectural “shape” of the computation is identical at every layer.

Scenario

Q: A team wants to build a model for extracting structured information (like named entities) from complete documents, where the full document is always available upfront. Which architecture family would be a natural fit, and why?

Ans: Encoder-only would be a natural fit — since the full document is always available upfront (not being generated token by token), the task benefits from full, non-causal self-attention, where every position can consider the entire document, including text that comes later. There’s no need for causal masking’s “can’t see the future” restriction, since generation isn’t the goal — understanding a fixed, complete input is.

Architecture

Q: Why might an encoder-decoder architecture be preferred over a decoder-only architecture for machine translation specifically?

Ans: Translation has a genuinely distinct source sequence (the original language) and target sequence (the translated language) — an encoder-decoder architecture explicitly models this structure: the encoder builds a full, bidirectional understanding of the complete source sentence, and the decoder generates the target sentence causally while using cross-attention to reference the encoder’s complete understanding of the source at every generation step.

A pure decoder-only model can also be trained to perform translation (by concatenating source and target and using causal attention throughout), but the encoder-decoder structure explicitly separates and specializes for each half of the task.

Engineering

Q: If you’re evaluating two candidate foundation models for building an application, and one has “24 layers, d_model=2048” while the other has “48 layers, d_model=2048,” what does this difference tell you, based on this module?

Ans: The second model has twice the depth (twice as many stacked Transformer blocks) at the same width — meaning it has significantly more total parameters and will require more compute for both training and inference, but also has more capacity for building increasingly refined, contextual representations through additional rounds of attention and FFN processing.

This isn’t a guarantee of better performance on any specific task, but it’s a genuine, real difference in model capacity and cost worth weighing against your application’s actual requirements.

17. What You Should Remember

The complete pipeline: text → tokens → embeddings + positional info → stack of N identical Transformer blocks → final representation — verified directly across a real 4-layer stack, with shapes and values confirmed at every layer.
The three architecture families — encoder-only, decoder-only, encoder-decoder — differ in their attention pattern (full vs. causal) and structure (single sequence vs. source+target), each suited to different task types.
Stacking works because every block’s output shape matches its input shape — a uniform, repeatable unit.

18. How This Helps Me Build AI Systems

You’ve now built and verified the complete architectural shape of a real Transformer model, end to end. Module 13 goes deeper into exactly why decoder-only specifically became the dominant choice for the LLMs you’ll actually build applications with — the natural next question after seeing all three families side by side.

Next: Module 13 — Encoder vs Decoder vs Decoder-Only LLMs — a deeper look at the tradeoffs, and precisely why modern LLMs overwhelmingly chose decoder-only.

Author: TechByteByByte Editorial Team
Reviewed by: TechByteByByte Admin
Published: August 31, 2026
Last reviewed: August 31, 2026