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Pretrain Vision and Large Language Models in Python

You're reading from   Pretrain Vision and Large Language Models in Python End-to-end techniques for building and deploying foundation models on AWS

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804618257
Length 258 pages
Edition 1st Edition
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Author (1):
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Emily Webber Emily Webber
Author Profile Icon Emily Webber
Emily Webber
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Before Pretraining
2. Chapter 1: An Introduction to Pretraining Foundation Models FREE CHAPTER 3. Chapter 2: Dataset Preparation: Part One 4. Chapter 3: Model Preparation 5. Part 2: Configure Your Environment
6. Chapter 4: Containers and Accelerators on the Cloud 7. Chapter 5: Distribution Fundamentals 8. Chapter 6: Dataset Preparation: Part Two, the Data Loader 9. Part 3: Train Your Model
10. Chapter 7: Finding the Right Hyperparameters 11. Chapter 8: Large-Scale Training on SageMaker 12. Chapter 9: Advanced Training Concepts 13. Part 4: Evaluate Your Model
14. Chapter 10: Fine-Tuning and Evaluating 15. Chapter 11: Detecting, Mitigating, and Monitoring Bias 16. Chapter 12: How to Deploy Your Model 17. Part 5: Deploy Your Model
18. Chapter 13: Prompt Engineering 19. Chapter 14: MLOps for Vision and Language 20. Chapter 15: Future Trends in Pretraining Foundation Models 21. Index 22. Other Books You May Enjoy

Hyperparameter tuning for foundation models

Foundation models present some unique challenges for hyperparameter tuning. Let’s try to understand them:

  • Model size – Possibly the largest obstacle to tuning foundation models is their sheer size. Many of the classic tuning strategies we looked at previously rely on training the model as many times as possible. When simply holding one copy of the model in memory requires tens of accelerators, the economics around this approach fall apart.
  • Volume of downstream tasks – As we’ve seen throughout the book, the sheer volume of candidate downstream tasks for foundation models is enormous. This makes hyperparameter tuning much more complex because the objective metrics for each of these tasks are unique. Picking the right downstream task itself could be a kind of tuning challenge!
  • Variety of hyperparameters – At these scales, the relevant hyperparameters aren’t just indicators of the training...
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