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Transformer Architecture

Understanding the building blocks of modern language models

The Big Picture

A transformer is like a highly sophisticated text processing machine. It reads text, understands relationships between words, and can generate new text or answer questions. Let's break down how it works:

Output Layer

Feed Forward

Multi-Head Attention

Position Encoding

Input Embedding

Multi-Head Attention

Multi-head attention allows the model to focus on different aspects of the input simultaneously. Click through the heads below to see how each one might focus on different patterns:

The
cat
sat
on
the
mat

Each attention head focuses on different patterns in the text

Key Components

Self-Attention

Allows the model to weigh the importance of different words when processing each word in the input sequence.

Feed-Forward Networks

Process the attention output further, allowing the model to learn complex patterns and relationships.

Layer Normalization

Helps stabilize the learning process by normalizing the data as it flows through the network.

Real World Application

Translation Example

When translating "The cat sat on the mat" to French:

  • • Self-attention helps understand the relationship between words
  • • Multiple attention heads capture different aspects of meaning
  • • Feed-forward networks process this information
  • • The model generates: "Le chat s'est assis sur le tapis"
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