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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

The future of foundation models

To me, a few key points seem incredibly obvious for where foundation models are trending:

  • Intense competition will continue between open source and proprietary model providers. As mentioned previously, right now we are in a perfect storm of hyper-focus on foundation models from most of the technology industry worldwide. A key axis here is proprietary versus open source. As suggested by this leaked Google document on May 4 (8), the capabilities of the open source world are advancing and in many cases, open source options are better than proprietary ones. They actually describe open source models as “pound-for-pound more capable.” This means that for the size of the model itself, the smaller ones produced by the open source world are better in a per-byte-size comparison.
  • Model consumers will get more options at a lower cost if they are flexible about model providers. To me, the result of this intense competition is clear; you...
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