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Mastering PyTorch

You're reading from   Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781801074308
Length 558 pages
Edition 2nd Edition
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (21) Chapters Close

Preface 1. Overview of Deep Learning Using PyTorch 2. Deep CNN Architectures FREE CHAPTER 3. Combining CNNs and LSTMs 4. Deep Recurrent Model Architectures 5. Advanced Hybrid Models 6. Graph Neural Networks 7. Music and Text Generation with PyTorch 8. Neural Style Transfer 9. Deep Convolutional GANs 10. Image Generation Using Diffusion 11. Deep Reinforcement Learning 12. Model Training Optimizations 13. Operationalizing PyTorch Models into Production 14. PyTorch on Mobile Devices 15. Rapid Prototyping with PyTorch 16. PyTorch and AutoML 17. PyTorch and Explainable AI 18. Recommendation Systems with PyTorch 19. PyTorch and Hugging Face 20. Index

Using the Hugging Face Hub for pre-trained models

In the previous section, we used a pre-trained BERT model as an example to demonstrate the interface between the transformers and torch libraries. In that example, we used the Hugging Face Hub [5] to download and then load a pre-trained BERT model. In this section, we will explore using the Hugging Face Hub to load pre-trained models further and demonstrate how to use the Hugging Face Hub via a Python library and via the Hugging Face website. All the code for this section is available on GitHub [6].

Note

You need to use an API token to access many of the models available on the Hugging Face Hub. You can access your API token from the following page: https://huggingface.co/settings/tokens.

To use the Hugging Face Hub Python library, we need to install it with the following command:

pip install huggingface_hub

Once it is installed, we can run the following command to import the library and fetch all...

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