Explaining How GPT/Transformers (LLMs) works in Layman Terms - with Visual Representation

The Inner Workings of GPT & Transformers: A Visual Guide 🤖

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Table of Contents 📚

1. Introduction
2. The Big Picture
3. Step-by-Step Breakdown
4. Putting It All Together
    

What is GPT? 🤖

GPT = Generative Pretrained Transformer

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   Generative    │     │   Pretrained    │     │   Transformer   │
│  Creates new    │     │  Learned from   │     │  Special AI     │
│    content      │     │  massive data   │     │  architecture   │
└─────────────────┘     └─────────────────┘     └─────────────────┘
    

1. Introduction: What Are We Looking At? 🔍

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Transformer Model
     ┌─────────────────────┐
     │    Input Text       │
     │ "Hello, how are you"│
     └──────────┬──────────┘
                ▼
     ┌─────────────────────┐
     │     Processing      │
     └──────────┬──────────┘
                ▼
     ┌─────────────────────┐
     │     Output Text     │
     │  "I am doing well"  │
     └─────────────────────┘
    

Transformers are a type of neural network architecture that has revolutionized natural language processing. They excel at understanding context and generating human-like text. This diagram shows the basic input-output flow of a transformer model.

2. The Big Picture: Main Components 🎯

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Input
  │
  ▼
┌─────────────────┐
│   Tokenizer     │    Breaks text into pieces
└───────┬─────────┘
        │
        ▼
┌─────────────────┐
│   Embeddings    │    Converts to numbers
└───────┬─────────┘
        │
        ▼
┌─────────────────┐
│ Encoder Blocks  │    Processes information
└───────┬─────────┘    (Multiple layers)
        │
        ▼
┌─────────────────┐
│   Prediction    │    Generates output
└─────────────────┘
    

This diagram outlines the main components of a transformer model:

1. Tokenizer: Breaks input text into smaller units (tokens).
2. Embeddings: Converts tokens into numerical vectors.
3. Encoder Blocks: Process the information through multiple layers.
4. Prediction: Generates the final output based on processed information.

3. Step-by-Step Breakdown 📝

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A. Tokenization Process

Original: "Hello, how are you?"
          ↓    ↓   ↓   ↓   ↓
Tokens: [Hello][,][how][are][you][?]

Vocabulary Example:
┌────────────┬─────────┐
│   Token    │   ID    │
├────────────┼─────────┤
│   Hello    │   456   │
│   how      │   789   │
│   are      │   234   │
│   you      │   567   │
└────────────┴─────────┘
    

Tokenization breaks down the input text into individual tokens. Each token is then assigned a unique ID from a predefined vocabulary. This process allows the model to work with discrete units of text.

B. Embedding Layer

Token ID → Vector Conversion
    456 →  [0.2, 0.5, -0.1]
    789 →  [0.3, 0.2, -0.4]
    234 →  [-0.1, 0.7, 0.2]
    

The embedding layer converts token IDs into dense vector representations(Multi Dimension). For explanation, I have considered only 3 dimension for each words.

3D Space Example:
      z     • Hello
      │    ╱
      │   ╱
      │  • you
      │ ╱
      │╱
y─────┼──── x
    

Real embeddings typically use hundreds or thousands of dimensions. Each additional dimension allows for capturing more nuanced relationships and properties.

Higher dimensions allow for:

• More precise relationships
• Better separation of concepts
• More complex patterns

C. Understanding Context (Attention Mechanism) 🔍

Example 1:      The bank is by the river
                     │
                     ▼
                Natural formation
                
Example 2:      I went to the bank to deposit money
                              │
                              ▼
                    Financial institution

Word: "bank"
                    Context Check
                         │
           ┌────────────┼────────────┐
           │            │            │
         Query    →    Key    →   Value
           │            │            │
           ▼            ▼            ▼
     [What am I?]  [What are    [What info
                    others?]     to pass?]
    

Attention layer/mechanism allows the model to weigh the importance of different words in the input when processing each word. It creates query, key, and value vectors for each word and computes attention scores to determine how much focus to place on other words in the context.

4. Putting It All Together 🏗️

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Processing Text Example 🔄

Input: "The cat sat on the mat"
       │    │   │   │   │   │
       ▼    ▼   ▼   ▼   ▼   ▼
Token: [The][cat][sat][on][the][mat]
       │    │   │   │   │   │
       ▼    ▼   ▼   ▼   ▼   ▼
Vector: [   Numbers for each token   ]
       │                            │
       ▼                            ▼
Attention: Understanding relationships
       │                            │
       ▼                            ▼
Output: Prediction for next word
    

Generating Text Example 📝

Step 1: Input     → "Once upon a"
        │
Step 2: Process   →Convert to Tokens → Vectorise the input →Analyze context by Attention layers.
        │
Step 3: Predict   → "time" (87% probability)
        │          "day"  (10% probability)
        │          other  (3% probability)
        │
Step 4: Output    → "Once upon a time"
        └── Repeat for next word ──┘