Transformers Explained Visually

A visual explanation of Transformer neural networks that uses GPT-2 (small) as a working example. It frames text generation as next-token prediction and highlights the self-attention mechanism that captures long-range dependencies more effectively than previous architectures. The exposition breaks a model down into three core parts: embedding (tokenization, token vectors, and positional encoding), stacked Transformer blocks (multi-head self-attention plus an MLP), and final output layers that convert representations into token probabilities. GPT-2 (small) is presented as a concrete, accessible instantiation: it has 124 million parameters and mirrors the architectural components found in larger state-of-the-art models.

Concrete implementation details are emphasized throughout: GPT-2’s vocabulary contains 50,257 tokens and uses 768-dimensional token embeddings combined with learned positional encodings; the model stacks 12 Transformer blocks, each with 12 attention heads. Each head computes Query, Key, and Value vectors, uses scaled dot-product attention with an upper-triangle mask to prevent peeking at future tokens, applies softmax and dropout, then concatenates head outputs and projects them linearly. The MLP expands representations 4× (768 → 3072) with GELU nonlinearity and projects back to 768. The final linear layer produces 50,257 logits, converted to probabilities via softmax, and sampling is controlled by temperature plus optional top-k/top-p filtering.