Visual Explanation of Transformer Neural Network Architecture

What is a Transformer?

The Transformer is a neural network architecture that has significantly altered the approach to Artificial Intelligence since its introduction in the 2017 paper "Attention is All You Need." It has become the standard architecture for deep learning models, driving text-generative models such as OpenAI's GPT, Meta's Llama, and Google's Gemini. Beyond text, Transformers are also applied in audio generation, image recognition, protein structure prediction, and game playing.

Core Functionality

Text-generative Transformer models operate on the principle of next-token prediction, determining the most probable next token (word or part of a word) given a text prompt. The primary innovation of Transformers is their use of a self-attention mechanism, which allows them to process entire sequences and capture long-range dependencies more effectively than prior architectures. The GPT-2 (small) model, with 124 million parameters, is an example of a text-generative Transformer and shares architectural principles with current state-of-the-art models.

Transformer Architecture Components

Every text-generative Transformer consists of three key components. First, "Embedding" converts text input into tokens, which are then transformed into numerical vectors that capture semantic meaning. Second, the "Transformer Block" processes and transforms input data, containing an "Attention Mechanism" for contextual information and relationships between words, and an "MLP (Multilayer Perceptron) Layer" to refine each token's representation. Finally, "Output Probabilities" are generated by linear and softmax layers, enabling the model to predict the next token in a sequence.