July 7, 2025

Inside the Transformer: Architecture and Attention Demystified - A Complete Guide

Introduction: What Are Transformers and Why Should You Care? (Article 4 alternative)

Imagine trying to understand a conversation where you can only remember the last few words someone said. That’s how AI used to work before transformers came along. Transformers revolutionized AI by giving models the ability to understand entire contexts at once. It’s like having perfect memory of an entire conversation.

Transformers are like super-smart reading machines that power ChatGPT, Google Translate, and countless other AI applications. The article uses a great analogy. Transformers work like an orchestra where every musician listens to everyone else and adjusts in real-time.

This guide will show you exactly how they work under the hood, with code you can run yourself. Think of it as learning how a car engine works instead of just knowing how to drive!

The Big Picture: Transformers Are Made of Simple Parts

Just like a car is made of wheels, engine, and steering wheel, transformers have basic parts:

Tokenizer: Breaks sentences into pieces (like cutting a pizza into slices)
Embeddings: Turns words into numbers the computer understands
Attention: The secret sauce - lets the model focus on what’s important
Layers: Stack these parts to make the model smarter

Let’s explore each part with real code examples.

Environment Setup: Preparing Your Kitchen

Before cooking, you prepare your kitchen. Same with transformers:


# Clone the repository and navigate to it
cd art_hug_04


# Run the setup task (installs Python 3.12.9 and all dependencies)
task setup


# Run all examples
task run


# Run specific examples
task run-attention-mechanism  # Self-attention demos
task run-modern-models       # Architecture comparisons

The key ingredients we’ll use:

transformers: The Hugging Face library (your AI toolkit)
torch: PyTorch for the mathematical operations
matplotlib/seaborn: For creating visualizations

Part 1: From Text to Numbers - The Foundation

Computers don’t understand words - they only understand numbers. Let’s see this transformation step by step.

Basic Example: Breaking Down Text

In transformers, we don’t just split on spaces. We break words into subwords:

“Transformers” might become [‘Transform’, ’ers’]
This helps handle words the model hasn’t seen before!

Real Tokenization and Embedding Example

Now let’s see how real transformers do it:

from transformers import AutoTokenizer, AutoModel
import torch

def basic_tokenization_and_embedding():
    """Let's convert text to numbers step by step."""

    # Step 1: Load a pre-trained model (like buying a trained dog vs training one yourself)
    model_name = "roberta-base"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModel.from_pretrained(model_name)

    # Step 2: Take a sentence
    sentence = "Transformers are amazing!"

    # Step 3: Break it into pieces (tokenize)
    inputs = tokenizer(sentence, return_tensors="pt")
    print("Token IDs:", inputs["input_ids"])
    # Output: tensor([[0, 44929, 32, 2770, 328, 2]])

    # What do these numbers mean? Let's see:
    tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
    print(f"Tokens: {tokens}")
    # Output: ['<s>', 'Transform', 'ers', 'Ġare', 'Ġamazing', '!', '</s>']

    # Step 4: Convert to embeddings (meaningful numbers)
    with torch.no_grad():  # This means "just use, don't train"
        outputs = model(**inputs)
        embeddings = outputs.last_hidden_state
        print("Embeddings shape:", embeddings.shape)
        # Output: torch.Size([1, 6, 768])

What’s Really Happening:

Tokenization: “Transformers are amazing!” becomes pieces:
- <s> = start of sentence (like a capital letter)
- Transform + ers = the word split into known pieces
- Ġare = “are” with a space marker (Ġ)
- </s> = end of sentence (like a period)
Token IDs: Each piece gets a number (like a jersey number in sports)
Embeddings: Each token becomes 768 numbers that capture its meaning
- Shape [1, 6, 768] means: 1 sentence, 6 tokens, 768 features per token
- Think of 768 features like describing a person with 768 characteristics

Visualizing Embeddings

Let’s make this more concrete:

import matplotlib.pyplot as plt
import numpy as np

def visualize_embeddings():
    """Show what embeddings look like."""

    # Get embeddings for a few words
    tokenizer = AutoTokenizer.from_pretrained("roberta-base")
    model = AutoModel.from_pretrained("roberta-base")

    words = ["happy", "sad", "dog", "cat"]

    for word in words:
        inputs = tokenizer(word, return_tensors="pt")
        with torch.no_grad():
            outputs = model(**inputs)
            # Average the embeddings (excluding special tokens)
            embedding = outputs.last_hidden_state[0, 1:-1].mean(dim=0)

        # Show first 10 dimensions as a bar chart
        plt.figure(figsize=(10, 3))
        plt.bar(range(10), embedding[:10].numpy())
        plt.title(f"First 10 embedding dimensions for '{word}'")
        plt.xlabel("Dimension")
        plt.ylabel("Value")
        plt.show()

This shows how different words have different “fingerprints” in the embedding space!

Part 2: The Building Blocks Explained

Now let’s understand each component that makes transformers work.

1. Positional Encoding: Teaching Order

Words need to know their position in a sentence. Consider these two sentences:

“The cat chased the dog”
“The dog chased the cat”

Same words, different meaning! Position matters.

Without position information, both sentences would look identical to a computer (just word counts). With position, we can see the difference in word order.

Modern transformers use sophisticated positional encodings:

import math

def visualize_positional_encoding():
    """Show how positional encoding works."""

    seq_length = 20
    d_model = 64

    # Create positional encoding
    position = torch.arange(seq_length).unsqueeze(1)
    div_term = torch.exp(torch.arange(0, d_model, 2) *
                         -(math.log(10000.0) / d_model))

    pe = torch.zeros(seq_length, d_model)
    pe[:, 0::2] = torch.sin(position * div_term)  # Even dimensions
    pe[:, 1::2] = torch.cos(position * div_term)  # Odd dimensions

    # Visualize
    plt.figure(figsize=(10, 6))
    plt.imshow(pe, cmap='RdBu', aspect='auto')
    plt.colorbar()
    plt.xlabel('Embedding Dimension')
    plt.ylabel('Position in Sequence')
    plt.title('Positional Encoding Pattern')
    plt.show()

The wavy patterns help the model understand “this word comes before that word”!

2. Layer Normalization and Residual Connections

Deep networks can be unstable - like a tall stack of blocks. Transformers use two tricks:

import torch.nn as nn

class SimpleTransformerBlock(nn.Module):
    """A basic building block of transformers."""

    def __init__(self, d_model):
        super().__init__()
        self.linear = nn.Linear(d_model, d_model)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, x):
        # Residual connection: x + transformation(x)
        # Like having a safety net - if transformation fails, original x is preserved
        output = x + self.linear(x)

        # Normalization: keeps values in reasonable range
        # Like adjusting volume so it's not too loud or quiet
        return self.norm(output)


# Example usage
block = SimpleTransformerBlock(768)
input_tensor = torch.randn(1, 10, 768)  # [batch, sequence, features]
output = block(input_tensor)
print(f"Input shape: {input_tensor.shape}")
print(f"Output shape: {output.shape}")  # Same shape!

Why This Matters:

Residual Connection (x + self.linear(x)): Information can flow around problematic transformations
Layer Normalization: Keeps numbers stable, preventing “explosion” or “vanishing”

3. Feed-Forward Networks: Individual Processing

After attention, each token gets its own mini neural network:

class FeedForward(nn.Module):
    """Each token gets processed individually."""

    def __init__(self, d_model=768, d_ff=3072):
        super().__init__()
        # Two linear layers with ReLU in between
        self.net = nn.Sequential(
            nn.Linear(d_model, d_ff),      # Expand (768 → 3072)
            nn.ReLU(),                     # Add non-linearity
            nn.Dropout(0.1),               # Prevent overfitting
            nn.Linear(d_ff, d_model)       # Contract (3072 → 768)
        )

    def forward(self, x):
        return self.net(x)


# Demonstrate
ff = FeedForward()
tokens = torch.randn(1, 5, 768)  # 5 tokens, 768 dimensions each
output = ff(tokens)
print(f"Each token processed independently!")
print(f"Input: {tokens.shape} → Output: {output.shape}")

Think of this as each word getting its own personal analyst that adds specialized processing!

Part 3: Self-Attention - The Magic Ingredient

This is where transformers really shine. Every word can look at every other word to understand context.

Understanding the Intuition

Self-Attention is like being in a library:

Query: “I need books about cooking”
Keys: Book titles on the shelves
Values: The actual books

The librarian (attention mechanism) finds books (values) whose titles (keys) match your request (query)!

In a real sentence like “The cat sat on the mat”, when processing ‘sat’:

Query from ‘sat’: “Who is doing the sitting?”
Keys from other words: [‘The’, ‘cat’, ‘on’, ’the’, ‘mat’]
Attention focuses on ‘cat’ (high score)
Value from ‘cat’ enriches understanding of ‘sat’

The Mathematics of Attention

Now let’s see the actual calculation:

def demonstrate_self_attention():
    """Show self-attention step by step."""

    # Simple example dimensions
    d_model = 64   # Feature size
    seq_len = 5    # Number of words

    # Create Query, Key, Value matrices
    # In reality, these come from learned projections
    Q = torch.randn(seq_len, d_model)  # Queries
    K = torch.randn(seq_len, d_model)  # Keys
    V = torch.randn(seq_len, d_model)  # Values

    # Step 1: Calculate attention scores
    # How well does each query match each key?
    scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_model)
    print(f"Attention scores shape: {scores.shape}")  # [5, 5]

    # Step 2: Convert to probabilities
    attention_weights = torch.softmax(scores, dim=-1)
    print(f"Each row sums to: {attention_weights.sum(dim=-1)}")  # All 1.0!

    # Step 3: Weighted sum of values
    output = torch.matmul(attention_weights, V)
    print(f"Output shape: {output.shape}")  # [5, 64]

    # Visualize attention pattern
    plt.figure(figsize=(6, 5))
    plt.imshow(attention_weights.numpy(), cmap='Blues', vmin=0, vmax=1)
    plt.colorbar(label='Attention Weight')
    for i in range(seq_len):
        for j in range(seq_len):
            plt.text(j, i, f'{attention_weights[i,j]:.2f}',
                    ha='center', va='center')
    plt.xlabel('Key Position')
    plt.ylabel('Query Position')
    plt.title('Self-Attention Weights')
    plt.show()

What Each Step Does:

Scores: Dot product measures similarity (like asking “how related are these words?”)
Softmax: Ensures each word distributes exactly 100% of its attention
Weighted Sum: Combines information based on attention weights

A Complete Attention Example

Let’s see attention in action with real words:

def word_attention_example():
    """Show attention with actual words."""

    words = ["The", "cat", "sat", "on", "mat"]
    seq_len = len(words)

    # Simulate attention weights (in real transformers, these are learned)
    # Let's make "sat" pay attention to "cat"
    attention_weights = torch.zeros(seq_len, seq_len)
    attention_weights[2, 1] = 0.8  # "sat" → "cat"
    attention_weights[2, 2] = 0.2  # "sat" → "sat"

    # Make each row sum to 1
    for i in range(seq_len):
        if attention_weights[i].sum() > 0:
            attention_weights[i] = attention_weights[i] / attention_weights[i].sum()
        else:
            attention_weights[i] = torch.ones(seq_len) / seq_len

    # Visualize
    plt.figure(figsize=(8, 6))
    plt.imshow(attention_weights.numpy(), cmap='Blues', vmin=0, vmax=1)
    plt.colorbar(label='Attention Weight')

    # Add labels
    plt.xticks(range(seq_len), words)
    plt.yticks(range(seq_len), words)
    plt.xlabel('Attending To')
    plt.ylabel('Word')
    plt.title('Word-to-Word Attention')

    # Add values
    for i in range(seq_len):
        for j in range(seq_len):
            if attention_weights[i,j] > 0.1:
                plt.text(j, i, f'{attention_weights[i,j]:.1f}',
                        ha='center', va='center', color='white')
    plt.show()

Multi-Head Attention: Multiple Perspectives

Single attention = one perspective. Multi-head = multiple perspectives combined:

def multi_head_attention_demo():
    """Show how multiple attention heads work together."""

    num_heads = 4
    d_model = 64
    d_k = d_model // num_heads  # 16 dimensions per head

    # Input
    seq_len = 5
    x = torch.randn(seq_len, d_model)

    # Each head processes a portion of the features
    all_heads_output = []

    for head in range(num_heads):
        # Each head looks at different features
        start_idx = head * d_k
        end_idx = (head + 1) * d_k

        # Extract this head's portion
        head_input = x[:, start_idx:end_idx]

        # Simple attention for this head (simplified)
        Q = head_input
        K = head_input
        V = head_input

        scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_k)
        weights = torch.softmax(scores, dim=-1)
        head_output = torch.matmul(weights, V)

        all_heads_output.append(head_output)

        print(f"Head {head}: Focusing on dimensions {start_idx}-{end_idx}")

    # Concatenate all heads
    concat_output = torch.cat(all_heads_output, dim=-1)
    print(f"\nFinal shape after concatenating {num_heads} heads: {concat_output.shape}")

Why Multiple Heads?

Head 1 might focus on grammar (“who did what”)
Head 2 might track entities (“which cat, which mat”)
Head 3 might identify relationships (“sitting on”)
Head 4 might capture style or tone

Combined, they create rich, multi-faceted understanding!

Part 4: Different Types of Transformers

Not all transformers are the same. There are three main types, each designed for different jobs:

Comparing the Three Architectures

from transformers import pipeline

def compare_architectures():
    """Show the three transformer types in action."""

    print("=== Three Types of Transformers ===\n")

    # 1. ENCODER-ONLY: The Reader (understands text)
    print("1. Encoder-Only (BERT) - The Careful Reader:")
    classifier = pipeline('sentiment-analysis',
                         model='distilbert-base-uncased-finetuned-sst-2-english')

    text = "I love learning about transformers!"
    result = classifier(text)
    print(f"   Input: '{text}'")
    print(f"   Analysis: {result[0]['label']} (confidence: {result[0]['score']:.3f})")
    print("   Use for: Classification, understanding, search\n")

    # 2. DECODER-ONLY: The Writer (generates text)
    print("2. Decoder-Only (GPT) - The Creative Writer:")
    generator = pipeline('text-generation', model='gpt2', max_new_tokens=15)

    prompt = "The future of AI is"
    result = generator(prompt, max_length=25, num_return_sequences=1)
    print(f"   Prompt: '{prompt}'")
    print(f"   Generated: '{result[0]['generated_text']}'")
    print("   Use for: Chatbots, story writing, code completion\n")

    # 3. ENCODER-DECODER: The Translator (transforms text)
    print("3. Encoder-Decoder (T5) - The Translator:")
    summarizer = pipeline('summarization', model='t5-small')

    long_text = ("Transformers have revolutionized natural language processing "
                 "by using self-attention mechanisms. They process entire sequences "
                 "at once, understanding context better than previous models.")

    summary = summarizer(long_text, max_length=30, min_length=10)
    print(f"   Input: '{long_text[:50]}...'")
    print(f"   Summary: '{summary[0]['summary_text']}'")
    print("   Use for: Translation, summarization, Q&A")

Understanding Attention Masks

Different architectures use different attention patterns:

def visualize_attention_masks():
    """Show how different architectures see text."""

    seq_len = 6
    fig, axes = plt.subplots(1, 3, figsize=(15, 4))

    # 1. BERT: Can see everything (bidirectional)
    bert_mask = torch.ones(seq_len, seq_len)
    axes[0].imshow(bert_mask, cmap='Blues', vmin=0, vmax=1)
    axes[0].set_title('BERT (Encoder)\nSees Everything')
    axes[0].set_xlabel('Can see →')
    axes[0].set_ylabel('Token ↓')

    # 2. GPT: Can only see backwards (causal)
    gpt_mask = torch.tril(torch.ones(seq_len, seq_len))
    axes[1].imshow(gpt_mask, cmap='Blues', vmin=0, vmax=1)
    axes[1].set_title('GPT (Decoder)\nSees Only Past')

    # 3. Training: Random masking
    train_mask = (torch.rand(seq_len, seq_len) > 0.15).float()
    axes[2].imshow(train_mask, cmap='Blues', vmin=0, vmax=1)
    axes[2].set_title('Training\nRandom Masking')

    plt.tight_layout()
    plt.show()

Explanation:

BERT: Every position sees all positions (understanding)
GPT: Each position only sees previous positions (generation)
Training: Random masks make models robust

Choosing the Right Architecture

Here’s a decision tree for picking the right transformer:

Task	Recommended Architecture
Classify customer feedback	Encoder (BERT/RoBERTa)
Generate product descriptions	Decoder (GPT/Llama)
Translate user manuals	Encoder-Decoder (T5/BART)
Answer questions from documents	Encoder + Retrieval (DPR + BERT)
Chat with customers	Decoder (GPT/Llama) + Fine-tuning
Summarize long reports	Encoder-Decoder (T5/BART)

Part 5: Advanced Example - RAG (Retrieval-Augmented Generation)

RAG combines transformers with external knowledge, like giving AI a reference library. This addresses a key limitation of transformers. They tend to hallucinate or make things up.

Simple RAG Implementation

def simple_rag_example():
    """Show how RAG works with a simple example."""

    # Step 1: Our knowledge base (imagine this is Wikipedia)
    knowledge_base = [
        "The Eiffel Tower is 330 meters tall and located in Paris.",
        "The Great Wall of China is over 21,000 kilometers long.",
        "The Pyramid of Giza was built around 2560 BCE.",
        "Transformers were introduced in the 2017 'Attention is All You Need' paper.",
        "BERT stands for Bidirectional Encoder Representations from Transformers."
    ]

    # Step 2: User asks a question
    question = "How tall is the Eiffel Tower?"
    print(f"Question: {question}\n")

    # Step 3: Find relevant information (simple keyword matching)
    print("Step 1: Searching knowledge base...")
    relevant_docs = []
    for doc in knowledge_base:
        if "Eiffel Tower" in doc or "tall" in doc:
            relevant_docs.append(doc)
            print(f"Found: {doc}")

    # Step 4: Create a prompt with context
    context = " ".join(relevant_docs)
    prompt = f"""Based on the following information:
{context}

Question: {question}
Answer:"""

    print(f"\nStep 2: Creating prompt with context...")
    print(prompt)

    # Step 5: Generate answer (using GPT-2)
    print("\nStep 3: Generating answer...")
    generator = pipeline('text-generation', model='gpt2', max_new_tokens=20)
    answer = generator(prompt, max_length=150, pad_token_id=50256)

    final_answer = answer[0]['generated_text'].split('Answer:')[-1].strip()
    print(f"\nFinal Answer: {final_answer}")

Why RAG Matters

Without RAG:

Model might hallucinate (make up facts)
Knowledge is frozen at training time
Can’t access private documents

With RAG:

Answers are grounded in real documents
Knowledge can be updated without retraining
Can work with your company’s private data
Provides sources for fact-checking

Example Comparison:

Question: “What’s our company’s return policy?”
Without RAG: makes up a plausible but wrong policy
With RAG: retrieves actual policy document and quotes it accurately

Part 6: Practical Implementation Tips

Memory and Performance Optimization

1. Disable gradient calculation for inference:

with torch.no_grad():
    output = model(input)

# → Saves memory and speeds up inference

2. Process multiple examples at once:

Instead of processing texts separately, batch them together for 3-5x speedup

3. Choose the right model size:

distilbert-base: 67M parameters - Fast, good for simple tasks
bert-base: 110M parameters - Balanced performance
bert-large: 340M parameters - Best accuracy, slower
gpt2: 124M parameters - Good for generation
gpt2-xl: 1.5B parameters - Better quality, needs more resources

Common Pitfalls and Solutions

Common Pitfalls and Solutions:

Out of memory: Use smaller batch sizes or distilled models
Slow inference: Enable ONNX export or use quantization
Poor results: Check if you’re using the right architecture
Tokenization issues: Ensure using matching tokenizer for model
Training instability: Lower learning rate, use warmup

Summary: The Complete Transformer Pipeline

Let’s trace the complete journey of text through a transformer:

1.Input Text: “Transformers are revolutionary!” 2.Tokenization: - ‘Transformers are revolutionary!’ → [‘Transform’, ’ers’, ‘are’, ‘revolutionary’, ‘!’] 3.Convert to IDs: - [‘Transform’, ’ers’, …] → [1547, 433, 526, 9823, 256] 4.Create Embeddings: - [1547, 433, …] → [[0.23, -0.45, …], [0.67, 0.12, …], …] - Each token → 768-dimensional vector 5.Add Positional Information: - So model knows word order 6.Apply Self-Attention: - Each word looks at all other words - ‘revolutionary’ might focus on ‘Transformers’ 7. Feed-Forward Processing: - Each token individually processed 8. Final Output: - Classification: ‘POSITIVE sentiment’ - Generation: ‘…and changing the world!’ - Translation: ‘Les transformers sont révolutionnaires!’

Key Takeaways

Transformers = Tokenizer + Embeddings + Attention + Feed-Forward
Attention lets every word see every other word (the breakthrough!)
Three types: Encoder (understand), Decoder (generate), Both (transform)
Multi-head attention = multiple perspectives for richer understanding
Position matters - transformers need to know word order
RAG = Transformers + External Knowledge for better accuracy
Choose architecture based on task (classification vs generation vs transformation)

Running the Examples

To run all the code examples from this article:


# Setup environment
git clone [repository]
cd art_hug_04
task setup


# Run examples
task run-attention-mechanism  # See attention in action
task run-modern-models       # Compare architectures
task run                     # Run everything


# Or run individual Python files
python src/attention_mechanism.py
python src/modern_models.py
python src/rag_example.py

Next Steps

Now that you understand transformers inside and out:

1.Try the Code: Run the examples and modify them 2.Pick a Project: Choose a task (classification, generation, or transformation) 3.Select a Model: Use the decision tree to pick the right architecture 4.Fine-tune: Adapt a pre-trained model to your specific needs 5.Deploy: Use optimization techniques for production

Remember: Transformers are powerful because they’re simple components arranged cleverly. You now understand these components - go build something amazing!

Final Thought

Transformers seemed like magic when they first appeared. Now you know the magic is just clever engineering: breaking text into tokens, converting to embeddings, letting words attend to each other, and stacking these operations. With this knowledge, you’re ready to not just use transformers, but to understand, debug, and improve them.

About the Author

Rick Hightower brings extensive enterprise experience as a former executive and distinguished engineer at a Fortune 100 company. He specialized in Machine Learning and AI solutions to deliver intelligent customer experiences. His expertise spans both theoretical foundations and practical applications of AI technologies.

As a TensorFlow-certified professional and graduate of Stanford University’s comprehensive Machine Learning Specialization, Rick combines academic rigor with real-world implementation experience. His training includes mastery of supervised learning techniques, neural networks, and advanced AI concepts, which he has successfully applied to enterprise-scale solutions.

With a deep understanding of both business and technical aspects of AI implementation, Rick bridges the gap between theoretical machine learning concepts and practical business applications, helping organizations leverage AI to create tangible value.

Follow Rick on LinkedIn or Medium for more enterprise AI and AI insights.

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Inside the Transformer: Architecture and Attention Demystified - A Complete Guide

Introduction: What Are Transformers and Why Should You Care? (Article 4 alternative)

The Big Picture: Transformers Are Made of Simple Parts

Environment Setup: Preparing Your Kitchen

Part 1: From Text to Numbers - The Foundation

Basic Example: Breaking Down Text

Real Tokenization and Embedding Example

Visualizing Embeddings

Part 2: The Building Blocks Explained

1. Positional Encoding: Teaching Order

2. Layer Normalization and Residual Connections

3. Feed-Forward Networks: Individual Processing

Part 3: Self-Attention - The Magic Ingredient

Understanding the Intuition

The Mathematics of Attention

A Complete Attention Example

Multi-Head Attention: Multiple Perspectives

Part 4: Different Types of Transformers

Comparing the Three Architectures

Understanding Attention Masks

Choosing the Right Architecture

Part 5: Advanced Example - RAG (Retrieval-Augmented Generation)

Simple RAG Implementation

Why RAG Matters

Part 6: Practical Implementation Tips

Memory and Performance Optimization

Common Pitfalls and Solutions

Summary: The Complete Transformer Pipeline

Key Takeaways

Running the Examples

Next Steps

Final Thought

About the Author

Search

Share

Follow

Categories

Tags

Article 4 Inside the Transformer Architecture and

Inside the Transformer: Architecture and Attention Demystified - A Complete Guide

Introduction: What Are Transformers and Why Should You Care? (Article 4 alternative)

The Big Picture: Transformers Are Made of Simple Parts

Environment Setup: Preparing Your Kitchen

Part 1: From Text to Numbers - The Foundation

Basic Example: Breaking Down Text

Real Tokenization and Embedding Example

Visualizing Embeddings

Part 2: The Building Blocks Explained

1. Positional Encoding: Teaching Order

2. Layer Normalization and Residual Connections

3. Feed-Forward Networks: Individual Processing

Part 3: Self-Attention - The Magic Ingredient

Understanding the Intuition

The Mathematics of Attention

A Complete Attention Example

Multi-Head Attention: Multiple Perspectives

Part 4: Different Types of Transformers

Comparing the Three Architectures

Understanding Attention Masks

Choosing the Right Architecture

Part 5: Advanced Example - RAG (Retrieval-Augmented Generation)

Simple RAG Implementation

Why RAG Matters

Part 6: Practical Implementation Tips

Memory and Performance Optimization

Common Pitfalls and Solutions

Summary: The Complete Transformer Pipeline

Key Takeaways

Running the Examples

Next Steps

Final Thought

About the Author

Search

Share

Follow

Categories

Tags