Deep Dive into GPT-2 and Its Architecture : Implement your own NanoGPT

Deep Dive into GPT-2 and Its Architecture

• Before jumping into the implementation, provide a brief history of the GPT (Generative Pre-trained Transformer) models, emphasizing the advancements GPT-2 brought into the AI community.
• Explain the transformer architecture briefly, highlighting attention mechanisms, and why they’re revolutionary. Use diagrams to illustrate how GPT-2 differs from a standard transformer model, focusing on its generative capabilities.

Understanding Tokenization

• Go into detail about what tokenization is and why it’s critical in NLP. Discuss subword tokenization, which GPT-2 uses, and why it’s efficient, especially for larger texts or for handling out-of-vocabulary words.
• Explain the parameters `truncation` and `max_length` in the tokenizer, emphasizing the need to standardize input lengths for model training.

The Role of Data Loaders in PyTorch

• Discuss the challenges of loading entire datasets into memory, and how batch processing and shuffling with `DataLoader` help manage memory usage efficiently.
• Explain why batch processing is critical in stochastic gradient descent and its variants, and how it affects the model’s learning dynamics.

Optimizer, Learning Rates, and Schedulers

• Take a step back and explain the concept of optimization in deep learning, emphasizing the role of the Adam optimizer and why it’s often preferred in practice.
• Discuss the concept of learning rates and their significance in training neural networks. Extend this discussion to learning rate scheduling, explaining what it is and how `get_linear_schedule_with_warmup` contributes to more effective training.

Loss Functions and Model Evaluation

• Explain the concept of loss functions in machine learning, and how they guide the optimization process. Discuss why we’re using a specific loss function for this model (likely Cross Entropy Loss for language models), and how it quantifies the difference between predicted probabilities and actual labels.
• Expand on the validation process, explaining why models might have good training performance but poor validation performance due to overfitting. Discuss strategies to mitigate this, such as dropout, regularization, or early stopping.

The Significance of Model Checkpoints

• Discuss in more depth the importance of model checkpointing, not just for capturing the best model but also for disaster recovery.
• Explain scenarios (like power loss or system crash) where training might be interrupted, and how checkpointing allows for the resumption of training without loss of progress.

Next Steps and Practical Applications

• Offer ideas on how readers can extend this project, such as by fine-tuning the model on specific genres of books, or adapting the model for other NLP tasks like summarization or question-answering.
• Discuss some practical considerations and potential challenges when deploying models like GPT-2, such as compute resources, inference time, and ethical considerations.