I. Key Concepts Language Models & ChatGPT: Predict next tokens (characters/words) using transformer architecture. Transformers: Use self-attention to process sequential data; introduced in "Attention is All You Need" (2017). Character-Level Models: Treat individual characters as tokens, suitable for smaller datasets like the Tiny Shakespeare dataset. Tokenization & Encoding: Convert text into numerical representations for processing.
II. Data Preprocessing Tokenization: Map characters to integers. Splitting Dataset: Train/validation split (90/10%).
III. Transformer Basics Block Size: Defines the context length for predictions. Batch Processing: Enables parallelization for GPU efficiency.
IV. Bigram Language Model Considers the previous character for predictions. Serves as a simple foundation before implementing transformers.
V. Self-Attention Mechanics Averaging Embeddings: Simplifies context representation. Matrix Multiplication Trick: Efficiently calculates weighted averages of prior tokens.
VI. Building the Transformer Positional Embeddings: Add sequence position information. Self-Attention: Computes relationships using keys, queries, and values. Multi-Head Attention: Captures diverse token relationships. Feedforward Network: Applies transformations for feature learning.
VII. Optimizing Deep Networks Residual Connections: Prevent gradient vanishing. Layer Normalization: Stabilizes training by normalizing features.
VIII. Scaling & Results Larger Models: Increase dimensions, layers, and use dropout for regularization. Evaluation: Improved validation loss and coherence in generated text.
IX. From nanoGPT to ChatGPT nanoGPT: A decoder-only transformer for text generation. ChatGPT: Fine-tunes GPT with supervised learning and reinforcement techniques for conversational tasks.
X. nanoGPT Codebase Key Files: bigram.py for training logic, gpt.py for architecture.
https://colab.research.google.com/drive/1wBt7NAgoCwMRfSApxfiq00Ix8DgNbdYx#scrollTo=STZOe6iz91Mk