Let's build GPT: from scratch, in code, spelled out.

Building a functional GPT-style language model involves constructing a neural network that uses self-attention to allow tokens to communicate with one another, iteratively processing text to predict upcoming characters.

• Data preparation — Text is converted into integer sequences for processing and split into training and validation groups to evaluate how well the model generalizes [12:51].

• Self-attention mechanism — Each token creates "query" and "key" vectors; the dot product of these determines how much information the model aggregates from other tokens in the sequence [1:04:01].

• Causal masking — A lower-triangular matrix prevents tokens from accessing future data, which ensures the model remains autoregressive during training [56:57].

• Deep network stability — The architecture incorporates two essential features to keep the model trainable as depth increases:

  • Residual connections — These provide a path for gradients to flow unimpeded, preventing signal degradation [1:28:51].
  • Layer normalization — This maintains a consistent distribution of features across tokens [1:33:20].

• Multi-head parallelization — Multiple attention channels running simultaneously allow the model to learn different types of token relationships at once [1:22:43].

• Training stages — The base model is trained to complete documents, but behaving as an assistant requires separate fine-tuning steps, such as using reinforcement learning [1:52:21].

• How does the "scaled" version of attention improve model stability?

• What is the difference between an encoder block and a decoder block?