Transformer Architecture Explained (with Visual References)

Transformers are the foundation of modern AI systems such as OpenAI GPT models, Google Gemini models, and many large language models (LLMs).

High-Level Transformer Architecture

A Transformer processes text in parallel rather than word-by-word.

Main Components

1. Input Embedding
2. Positional Encoding
3. Multi-Head Self-Attention
4. Feed Forward Network
5. Residual Connections
6. Layer Normalization
7. Output Layer

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Step 1: Input Embeddings

Computers cannot understand words directly.

Example sentence:

I love artificial intelligence

Each word is converted into a vector (list of numbers).

I          → [0.12, 0.45, ...]
love       → [0.88, 0.23, ...]
artificial → [0.44, 0.91, ...]
intelligence → [0.67, 0.31, ...]

These vectors are called embeddings.

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Step 2: Positional Encoding

Transformers process all words simultaneously.

Because of this, they must know the order of words.

Example:

Dog bites man

and

Man bites dog

contain the same words but have different meanings.

Positional encoding adds position information.

Visualization

The model combines:

Final Input = Word Embedding + Position Encoding

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Step 3: Self-Attention (The Heart of Transformers)

Self-attention allows every word to look at every other word.

Sentence:

The animal didn't cross the street because it was tired.

The word:

it

needs to understand that it refers to:

animal

not:

street

Self-attention learns these relationships automatically.

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Query, Key, and Value

Each token produces three vectors:

Query (Q)
Key   (K)
Value (V)

Think of it like:

Component	Purpose
Query	What am I looking for?
Key	What information do I have?
Value	Actual information

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Attention Visualization

Attention Score Formula

The transformer computes:

\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V

This determines how strongly one word should pay attention to another.

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Step 4: Multi-Head Attention

Instead of one attention calculation, transformers use multiple attention heads.

Each head learns different relationships.

Example:

Sentence:

John gave Mary a book because she asked.

Different heads may learn:

• Grammar relationships
• Subject-object relationships
• Pronoun references
• Semantic meaning

Multi-Head Attention

Typical models use:

8 heads
12 heads
16 heads
32 heads

depending on size.

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Step 5: Add & Normalize

After attention:

Output = Input + Attention Output

This is called a Residual Connection.

Benefits:

• Prevents vanishing gradients
• Enables very deep networks
• Improves training stability

Then Layer Normalization is applied.

Input
 ↓
Attention
 ↓
Add
 ↓
Normalize

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Step 6: Feed Forward Network

Every token passes through a small neural network.

Example:

Input Vector
    ↓
Linear Layer
    ↓
Activation Function
    ↓
Linear Layer
    ↓
Output Vector

This allows the model to learn more complex patterns.

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Encoder and Decoder

Original Transformer (2017 paper) contains:

Encoder

Reads input.

Example:

English sentence

Decoder

Generates output.

Example:

French translation

Encoder-Decoder Architecture

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GPT vs Original Transformer

Original Transformer

Encoder + Decoder

Used for:

• Translation
• Summarization
• Sequence-to-sequence tasks

GPT

Decoder Only

Used for:

• Chatbots
• Text generation
• Coding assistants

BERT

Encoder Only

Used for:

• Classification
• Search
• Understanding tasks

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Why Transformers Beat RNNs and LSTMs

Feature	RNN	LSTM	Transformer
Parallel Processing	❌	❌	✅
Long Context	Poor	Better	Excellent
Training Speed	Slow	Slow	Fast
Scalability	Limited	Limited	Excellent

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Complete Data Flow

Input Text
    ↓
Tokenization
    ↓
Embeddings
    ↓
Positional Encoding
    ↓
Multi-Head Attention
    ↓
Add & Normalize
    ↓
Feed Forward Network
    ↓
Add & Normalize
    ↓
Repeat Many Layers
    ↓
Output Probabilities
    ↓
Predicted Next Token

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Why Transformers Revolutionized AI

Transformers enabled:

• OpenAI GPT models
• ChatGPT
• Google Gemini
• Large-scale translation systems
• Modern recommendation engines
• Image transformers (ViT)
• Multimodal AI

The key innovation is self-attention, which allows the model to understand relationships between all tokens simultaneously, making it highly scalable and effective for large datasets.

In practice, a modern LLM is essentially a very large stack of transformer blocks trained on enormous amounts of text, with billions or even trillions of parameters.