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Mastering Transformers

You're reading from   Mastering Transformers The Journey from BERT to Large Language Models and Stable Diffusion

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781837633784
Length 462 pages
Edition 2nd Edition
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Authors (2):
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Savaş Yıldırım Savaş Yıldırım
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Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Author Profile Icon Meysam Asgari- Chenaghlu
Meysam Asgari- Chenaghlu
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Toc

Table of Contents (25) Chapters Close

Preface 1. Part 1: Recent Developments in the Field, Installations, and Hello World Applications
2. Chapter 1: From Bag-of-Words to the Transformers FREE CHAPTER 3. Chapter 2: A Hands-On Introduction to the Subject 4. Part 2: Transformer Models: From Autoencoders to Autoregressive Models
5. Chapter 3: Autoencoding Language Models 6. Chapter 4: From Generative Models to Large Language Models 7. Chapter 5: Fine-Tuning Language Models for Text Classification 8. Chapter 6: Fine-Tuning Language Models for Token Classification 9. Chapter 7: Text Representation 10. Chapter 8: Boosting Model Performance 11. Chapter 9: Parameter Efficient Fine-Tuning 12. Part 3: Advanced Topics
13. Chapter 10: Large Language Models 14. Chapter 11: Explainable AI (XAI) in NLP 15. Chapter 12: Working with Efficient Transformers 16. Chapter 13: Cross-Lingual and Multilingual Language Modeling 17. Chapter 14: Serving Transformer Models 18. Chapter 15: Model Tracking and Monitoring 19. Part 4: Transformers beyond NLP
20. Chapter 16: Vision Transformers 21. Chapter 17: Multimodal Generative Transformers 22. Chapter 18: Revisiting Transformers Architecture for Time Series 23. Index 24. Other Books You May Enjoy

SageMaker inference

Amazon, in collaboration with Hugging Face, created a nice way of deploying models using only a few lines of code. To deploy any model using SageMaker, you can simply visit the model page at Hugging Face and click the Amazon SageMaker button. Please also note that other methods such as Azure are also available:

Figure 14.9 – Amazon SageMaker button

Figure 14.9 – Amazon SageMaker button

This will give the related code to use SageMaker for inference but note that you need to set up the AWS SageMaker environment first. We did not include this part in the book because it is another topic entirely, but you can easily find it in the SageMaker documentation (https://aws.amazon.com/sagemaker/).

Let’s see how we can use SageMaker for inference:

  1. The first step is to install sagemaker and then import it:
    import sagemaker
    import boto3
    from sagemaker.huggingface import HuggingFaceModel
  2. The next step is to run this code to get the role:
    try:
       ...
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