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Transformers Explained Visually

3 hours ago
  • Transformer is a neural network architecture introduced in 2017, powering models like GPT, Llama, and Gemini.
  • Core innovation is the self-attention mechanism, enabling processing of entire sequences and capturing long-range dependencies.
  • Text-generative Transformers operate on next-token prediction.
  • Key components: Embedding, Transformer Block (Multi-Head Self-Attention + MLP), and Output Probabilities.
  • Embedding involves tokenization, token embeddings (768-dim for GPT-2 small), positional encoding, and summation.
  • Multi-Head Self-Attention uses Query, Key, Value matrices, splits into heads, applies masked attention, and outputs concatenated projections.
  • MLP layer expands dimensionality then contracts, with GELU activation, processing tokens independently.
  • Output probabilities use linear layer, softmax, and sampling with temperature, top-k, and top-p parameters.
  • Auxiliary features include Layer Normalization, Dropout, and Residual Connections to stabilize training and prevent overfitting.
  • Transformer Explainer is an interactive browser-based tool using a live GPT-2 small model with Svelte and D3.js.