This video provides a hands-on tutorial on building a simplified version of GPT from scratch. The goal is to demystify the inner workings of large language models like ChatGPT by implementing a Transformer-based language model in Python, training it on a smaller dataset (tiny Shakespeare), and generating text.
Building a Character-Level Language Model: The video starts by building a basic character-level language model using the "tiny Shakespeare" dataset. This involves tokenization (converting characters to integers), creating an encoder and decoder, and splitting the data into training and validation sets.
The Bigram Language Model and Loss Function: A simple bigram language model is implemented as a baseline. The negative log-likelihood loss (cross-entropy) is used to evaluate the model's performance.
Self-Attention Mechanism: The core concept of self-attention is explained, showing how it allows tokens to interact with each other based on their content and position. The video demonstrates efficient implementation using matrix multiplication and masking to prevent future tokens from influencing past ones.
Multi-Headed Self-Attention and Feedforward Layers: The video builds on the self-attention mechanism by introducing multi-headed attention (multiple attention heads working in parallel) and feedforward layers (allowing each token to process information individually after the communication step).
Residual Connections and Layer Normalization: The importance of residual connections (skip connections) and layer normalization is explained as techniques to improve the training of deeper networks. These are incorporated into the model to enhance optimization and prevent vanishing gradients. The video contrasts layer normalization with batch normalization.
Scaling Up the Model: The video shows how to scale up the model by increasing the number of layers, batch size, embedding dimensions, and incorporating dropout for regularization.
