Skip to main content

​
Transformers in Plain English

Transformers are the technology behind ChatGPT, BERT, and almost every modern AI model. Let’s understand what they are without the math.

​
The Big Idea

Imagine you’re reading a sentence. To understand each word, you need to consider all the other words around it. The word “bank” means something different in “river bank” vs “savings bank.” Transformers do exactly this - they look at all words simultaneously to understand context. This is their superpower.

​
Before Transformers

​
The Old Way (RNNs)

Previous AI models read text like humans do - one word at a time, left to right: 
The → cat → sat → on → the → mat
Problems:
  • Slow (can’t read words in parallel)
  • Forgetful (loses context over long texts)
  • Hard to train (information gets lost)

​
The Transformer Revolution (2017)

Transformers changed everything by reading all words at once: 
[The, cat, sat, on, the, mat] → All processed together
Benefits:
  • Fast (parallel processing)
  • Better context understanding
  • Handles long texts well
  • Easier to train

​
How Transformers Work

Think of transformers as having three main components:

​
1. Attention Mechanism

The “attention” part is like highlighting important words when reading. Example sentence: “The animal didn’t cross the street because it was too tired” The transformer figures out:
  • “it” refers to “animal” (not “street”)
  • “tired” relates to “animal”
  • This determines the meaning
Attention creates connections between related words, no matter how far apart they are.

​
2. Positional Encoding

Since transformers see all words at once, they need to know word order. Without position information:
  • “Dog bites man” = “Man bites dog” (very different!)
Transformers add position information to each word:
  • Word 1: “Dog” + [position 1]
  • Word 2: “bites” + [position 2]
  • Word 3: “man” + [position 3]

​
3. Feed-Forward Networks

After understanding relationships (attention), the model processes this information through neural networks to:
  • Extract meaning
  • Make predictions
  • Generate responses

​
Encoder vs Decoder

Transformers come in three flavors:

​
Encoder-Only (BERT)

What it does: Understands text deeply Like: A careful reader who analyzes every word Good for:
  • Classification
  • Understanding context
  • Extracting information
  • Sentiment analysis
How it works: Reads all words to build understanding

​
Decoder-Only (GPT)

What it does: Generates text Like: A writer creating content word by word Good for:
  • Text generation
  • Chatbots
  • Code completion
  • Creative writing
How it works: Predicts the next word based on previous words

​
Encoder-Decoder (T5)

What it does: Transforms text Like: A translator reading one language and writing another Good for:
  • Translation
  • Summarization
  • Question answering
  • Text transformation
How it works: Encoder reads input, decoder generates output

​
Self-Attention Explained

The key innovation of transformers is “self-attention” - the ability to relate every word to every other word.

​
Simple Example

Sentence: “The cat sat on the mat” Self-attention creates a grid showing how much each word relates to others: 
        The  cat  sat  on  the  mat
The      •    •    ○    ○    ○    ○
cat      •    •    •    ○    ○    ○
sat      ○    •    •    •    ○    •
on       ○    ○    •    •    •    •
the      •    ○    ○    •    •    •
mat      ○    ○    •    •    •    •

• = Strong relationship
○ = Weak relationship
The model learns these relationships during training.

​
Multi-Head Attention

Transformers use multiple attention “heads” - like having multiple experts each looking for different patterns:
  • Head 1: Looks for grammatical relationships
  • Head 2: Looks for semantic meaning
  • Head 3: Looks for entity relationships
  • Head 4: Looks for temporal connections
  • (and many more…)
All these perspectives combine for rich understanding.

​
Layers and Depth

Transformers stack multiple layers, each adding more understanding: Layer 1: Basic patterns (grammar, simple relationships) Layer 2: Phrases and simple concepts Layer 3: Sentences and context Layer 4: Paragraphs and themes … Layer N: Deep, abstract understanding More layers = deeper understanding (but also more compute needed)

​
Why Transformers Dominate

​
Parallelization

Old models: Process words sequentially (slow) Transformers: Process all words simultaneously (fast) This makes training much faster on modern GPUs.

​
Long-Range Dependencies

Can connect information across long distances:
  • Beginning and end of a document
  • Question and answer separated by paragraphs
  • Context from much earlier

​
Transfer Learning

Transformers trained on general text can be fine-tuned for specific tasks:
  1. Pre-train on Wikipedia (general knowledge)
  2. Fine-tune on medical texts (specialized)

​
Scalability

Transformers get better with:
  • More data
  • More parameters
  • More compute
This predictable scaling enables huge models like GPT-4.

​
Common Transformer Models

​
BERT Family

  • BERT: Bidirectional understanding
  • RoBERTa: Robustly optimized BERT
  • DistilBERT: Smaller, faster BERT
  • ALBERT: Lighter BERT

​
GPT Family

  • GPT-2: Early text generation
  • GPT-3: Large-scale generation
  • GPT-4: Multimodal capabilities

​
T5/BART Family

  • T5: Text-to-text unified framework
  • BART: Denoising autoencoder
  • mT5: Multilingual T5

​
Specialized

  • CLIP: Vision and language
  • Whisper: Speech recognition
  • LayoutLM: Document understanding

​
Transformer Sizes

SizeParametersLayersUse Case
TinyUnder 100M4-6Mobile, edge devices
Small100-500M6-12Standard applications
Base500M-1B12-24Production systems
Large1B-10B24-48High-performance
XL10B+48+State-of-the-art

​
Computational Requirements

​
Training

  • Small models: Hours on single GPU
  • Medium models: Days on multiple GPUs
  • Large models: Weeks on GPU clusters

​
Inference

  • Small models: CPU capable
  • Medium models: Single GPU
  • Large models: Multiple GPUs

​
Memory Formula (Rough)

  • Parameters × 4 bytes = Model size
  • Add 2-3x for training (gradients, optimizer)
  • Example: 1B parameters ≈ 4GB model, 12GB for training

​
Optimizations and Variants

​
Flash Attention

Makes attention calculation much faster by reorganizing memory access.

​
Sparse Attention

Only attend to important tokens instead of all tokens.

​
Efficient Transformers

  • Linformer: Linear complexity attention
  • Performer: Uses random features
  • Reformer: Reversible layers

​
Mixture of Experts (MoE)

Use different “expert” networks for different inputs, activating only what’s needed.

​
Limitations

​
Quadratic Complexity

Attention cost grows quadratically with sequence length:
  • 100 tokens: 10,000 comparisons
  • 1,000 tokens: 1,000,000 comparisons

​
Context Windows

Limited input length:
  • BERT: 512 tokens
  • GPT-3: 4,096 tokens
  • GPT-4: 32,000 tokens
  • Claude: 100,000+ tokens

​
Computational Cost

Large models are expensive to train and run.

​
Lack of True Understanding

Despite impressive abilities, transformers don’t truly “understand” - they find patterns.

​
Future Directions

​
Efficiency Improvements

  • Better attention mechanisms
  • Sparse models
  • Quantization
  • Distillation

​
Longer Context

  • Extending context windows
  • Efficient long-range attention
  • Hierarchical processing

​
Multimodal

  • Combining text, image, audio, video
  • Unified architectures
  • Cross-modal understanding

​
Practical Implications

​
For Training

  • Start with pre-trained transformers
  • Fine-tune on your specific task
  • Use appropriate model size for your data

​
For Deployment

  • Consider distilled versions for production
  • Use quantization to reduce size
  • Implement caching for efficiency

​
For Selection

  • Encoder for understanding tasks
  • Decoder for generation tasks
  • Encoder-decoder for transformation tasks

​
Next Steps

Model Types

Explore different architectures

Choosing Your Approach

Select the right training method