Get Started Now
Ready to build your own transformer? Clone the repo and start training in minutes:
git clone https://github.com/zhubert/transformer.gitcd transformermake installpython main.py # Interactive CLI
What is a Transformer?
Learn how transformers work by building one in PyTorch, from attention mechanisms to complete text generation
Introduction
Section titled “Introduction”What is a transformer? A transformer is a type of neural network architecture introduced in the landmark paper “Attention is All You Need” (Vaswani et al., 2017). It revolutionized artificial intelligence and is now the foundation of virtually all modern large language models, including GPT, BERT, Claude, and many others.
What makes transformers special? Previous approaches to language modeling used recurrent neural networks (RNNs), which process text one word at a time in sequence—like reading a sentence from left to right. Transformers instead use a mechanism called attention that allows them to process all words simultaneously while still understanding their relationships. This parallel processing makes them much faster to train and more effective at capturing long-range dependencies in text.
Try It Yourself
Section titled “Try It Yourself”Learning Path
Section titled “Learning Path”Step 1: Token Embeddings
Convert text to vectors and add position information
Step 2: Attention
Learn how tokens attend to each other using Query, Key, Value
Step 3: Multi-Head Attention
Run parallel attention heads to capture different relationships
Step 4: Feed-Forward Networks
Process attended information through position-wise MLPs
Step 5: Transformer Block
Combine attention, FFN, layer norm, and residual connections
Step 6: Complete Model
Stack blocks and add embedding/output layers
Step 7: Training at Scale
Use gradient accumulation and validation splits for stable training
Step 8: KV-Cache
Optimize inference speed by caching key-value pairs
Step 9: Interpretability
Analyze attention patterns and understand what the model learns