Table Of Contents:

1. Introduction to Large Language Models
2. Understanding PyTorch
3. Preprocessing Data for Large Language Models
4. Fine Tuning vs. Training from Scratch
5. Choosing the Right Pretrained Model
6. Evaluating Model Performance
7. Hyperparameter Tuning
8. Implementing Transfer Learning with PyTorch
9. Training Strategies for Large Language Models
10. Optimizing Loss Functions
11. Dealing with Class Imbalance and Rare Tokens
12. Regularization Techniques
13. Handling Long Sequences
14. Model Interpretability
15. Deploying Fine Tuned Large Language Models
16. Beyond Fine Tuned Large Language Models: Future Directions
17. Conclusion