transformer architecture visual guide
transformer architecture visual guide
# The Illustrated Transformer: A Visual Guide to Understanding Transformer Architecture (2025/03/14)
Summary
This page is a detailed blog post by Jay Alammar that explains the Transformer architecture through visual illustrations and step-by-step breakdowns. The content focuses on helping readers understand the inner workings of the Transformer model, which was introduced in the paper "Attention is All You Need" and has become a foundational architecture for modern NLP models including large language models.
The post methodically explains:
High-level architecture of the Transformer, showing its encoder-decoder structure
Self-attention mechanism - the core innovation that allows the model to focus on different parts of the input sequence when processing each word
Multi-head attention - how multiple attention "heads" allow the model to focus on different aspects of the input simultaneously
Positional encoding - the method used to give the model information about word order
The complete forward pass through the model, from input embedding to final output generation
Training process including the loss function and how the model learns to translate sentences
The post uses clear illustrations, animations, and simplified examples to make these complex concepts accessible. It also notes that the content has been expanded into a book and includes references to additional resources for deeper understanding. The visual explanations make this an excellent resource for those looking to understand how modern language models function at a fundamental level.