117: Training Transformers

Chapter Overview

Training a [[110-transformer-architecture/index|Transformer]]-based [[101-Foundation-Models|Foundation Model]] is a monumental undertaking that requires massive datasets, vast computational resources, and sophisticated optimization techniques. This note provides a high-level overview of the standard training process.

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The Core Training Loop

At its heart, training a Transformer is a supervised learning process, even if the "labels" are self-generated. The goal is to iteratively adjust the model's weights to minimize a loss function. A single training step follows a standard deep learning pattern, repeated millions of times.

flowchart TD
    subgraph DataInput ["📊 Training Data Input"]
        A["🗂️ Batch of Training Data<br/>(e.g., text sequences)"]
    end

    subgraph TrainingLoop ["🔄 The Training Loop"]
        direction TB
        B["1️⃣ Forward Pass<br/>Model makes predictions"]
        C["2️⃣ Calculate Loss<br/>Compare predictions to targets"]
        D["3️⃣ Backward Pass<br/>Compute gradients"]
        E["4️⃣ Optimizer Step<br/>Update model weights"]

        B -->|Predictions| C
        C -->|Loss value| D
        D -->|Gradients| E
        E -.->|Updated weights| B
    end

    subgraph Result ["🎯 Final Outcome"]
        F["✅ Trained Model<br/>Ready for inference"]
    end

    A --> B
    TrainingLoop -->|After many epochs| F

    %% Styling
    classDef inputStyle fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    classDef forwardStyle fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    classDef lossStyle fill:#fce4ec,stroke:#c2185b,stroke-width:2px
    classDef backwardStyle fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    classDef optimizerStyle fill:#e8f5e8,stroke:#388e3c,stroke-width:2px
    classDef resultStyle fill:#c8e6c9,stroke:#1B5E20,stroke-width:3px
    classDef subgraphStyle fill:#f9f9f9,stroke:#666,stroke-width:2px

    class A inputStyle
    class B forwardStyle
    class C lossStyle
    class D backwardStyle
    class E optimizerStyle
    class F resultStyle
    class DataInput,TrainingLoop,Result subgraphStyle