110: The Transformer Architecture

Topic Overview

The Transformer, introduced in the 2017 paper "Attention Is All You Need," is the neural network architecture that underpins nearly all modern Foundation Models. Its invention marked a pivotal moment in AI, solving key problems that held back previous sequence-to-sequence models like RNNs.

This page serves as a "Map of Content" (MOC), providing a structured path to understanding the Transformer's core components.

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The Core Innovation: Solving the Bottleneck

Before the Transformer, models like RNNs processed text sequentially (word by word). This created two major problems: 1. Information Bottleneck: The entire meaning of a long input sequence had to be compressed into a single, fixed-size vector. 2. Slow Processing: The sequential nature meant that computation could not be parallelized, making it slow for long inputs.

The Transformer solves this with its core innovation: the Attention Mechanism.

flowchart TD
    subgraph RNN ["🔴 Before Transformers: RNNs"]
        direction LR
        A[Word 1] --> B[Word 2] --> C[Word 3] --> D[Context Vector<br/>📦 Bottleneck]
    end

    subgraph TRANS ["🟢 With Transformers"]
        direction TB
        E[Word 1] 
        F[Word 2] 
        G[Word 3]

        E -.-> F
        E -.-> G
        F -.-> E
        F -.-> G
        G -.-> E
        G -.-> F

        H[✨ All words attend<br/>to each other in parallel]
        E --> H
        F --> H
        G --> H
    end

    style D fill:#ffcdd2,stroke:#b71c1c,stroke-width:2px
    style H fill:#c8e6c9,stroke:#1b5e20,stroke-width:2px
    style RNN fill:#fff3e0,stroke:#f57c00
    style TRANS fill:#e8f5e8,stroke:#2e7d32

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Architecture Components

The Transformer consists of several key components working together:

Core Components Map

graph LR
    subgraph INPUT ["Input Processing"]
        A[Token Embeddings] --> B[Positional Encoding]
        B --> C[Input Representations]
    end

    subgraph ATTENTION ["Attention Mechanism"]
        D[Multi-Head Attention] --> E[Self-Attention]
        E --> F[Cross-Attention]
    end

    subgraph LAYERS ["Transformer Layers"]
        G[Encoder Layers] --> H[Decoder Layers]
        H --> I[Feed-Forward Networks]
    end

    subgraph OUTPUT ["Output Generation"]
        J[Linear Projection] --> K[Softmax] --> L[Predictions]
    end

    C --> ATTENTION
    ATTENTION --> LAYERS
    LAYERS --> OUTPUT

    style INPUT fill:#e3f2fd,stroke:#1976d2
    style ATTENTION fill:#fff3e0,stroke:#f57c00
    style LAYERS fill:#e8f5e8,stroke:#388e3c
    style OUTPUT fill:#fce4ec,stroke:#c2185b

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Learning Path

Follow this structured path to master the Transformer architecture:

1. Foundation Concepts

• Self-Attention Mechanism - The core innovation that makes Transformers work
• Positional Encoding - How Transformers understand sequence order
• Multi-Head Attention - Parallel attention for richer representations

2. Architecture Deep Dive

• Encoder Architecture - Understanding the encoding stack
• Decoder Architecture - How generation works
• Feed-Forward Networks - The other half of each layer

3. Training & Optimization

• Training Strategies - How to train these massive models
• Attention Patterns - What the model learns to attend to
• Scaling Laws - Why bigger models work better

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Key Insights

Why Transformers Won

The Transformer's success comes from three key innovations:

1. Parallel Processing: All positions can be processed simultaneously
2. Long-Range Dependencies: Direct connections between any two positions
3. Scalability: Architecture scales efficiently with more data and compute

Modern Variations

While the original Transformer had both encoder and decoder, modern foundation models often use:

• Encoder-only: BERT, RoBERTa (understanding tasks)
• Decoder-only: GPT, LLaMA (generation tasks)
• Encoder-Decoder: T5, BART (translation, summarization)

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Next Steps

Ready to dive deeper? Start with understanding the core mechanism:

🔍 Self-Attention Mechanism →

Or explore the broader context:

← Foundation Models Multi-Head Attention →