Understanding GPT and Transformers

Introduction to GPT

• GPT stands for Generative Pretrained Transformer.
  • Generative: Generates new text.
  • Pretrained: Model initially learns from extensive data, allowing fine-tuning for specific tasks.
  • Transformer: A type of neural network central to modern AI.

History and Application of Transformers

• Initially introduced by Google in 2017 for text translation.
• Used in models converting audio to text and vice versa.
• Basis for tools like DALL-E and Midjourney, which generate images from text.
• ChatGPT and similar models predict the next word using context.

Data Flow in Transformers

• Transformers break input into tokens (words or parts of words).
• Tokens are associated with vectors, representing their meaning in high-dimensional space.
• Attention block allows tokens to update meanings based on context.
• Multi-layer perceptron processes vectors in parallel.
• Final output is a probability distribution over possible next tokens.

Matrix Multiplication in Transformers

• Most operations involve matrix-vector multiplications.
• Weights: Parameters learned during training, organized into matrices.
• Example: GPT-3 has 175 billion parameters across ~28,000 matrices.

Word Embeddings

• Words are turned into vectors using an embedding matrix.
• Vectors represent words in high-dimensional space, capturing semantic meaning.
• Example: Relationship between king-queen is similar to man-woman.
• Dot product measures vector alignment, useful for capturing relationships like singular vs plural.

Context and Prediction

• Context size in GPT-3 is 2048, limiting how much text it can process at once.
• Model predicts next word based on learned context-rich embeddings.
• Unembedding matrix maps vector back to vocabulary for prediction.

Softmax Function

• Converts logits into a probability distribution.
• Temperature parameter adjusts probability distribution's spread.

Training and Parameters

• Transformers learn embeddings and weights during training.
• Embedding matrix and unembedding matrix each contribute significantly to total parameters.

Closing Notes

• Understanding word embeddings, softmax, and matrix multiplication is essential before exploring the attention mechanism further.
• The next chapter will dive deeper into attention blocks, crucial to transformer success.