LLM Course

0. Setup

1. Transformer models

2. Using 🤗 Transformers

3. Fine-tuning a pretrained model

4. Sharing models and tokenizers

5. The 🤗 Datasets library

6. The 🤗 Tokenizers library

7. Classical NLP tasks

8. How to ask for help

9. Building and sharing demos

10. Curate high-quality datasets

11. Fine-tune Large Language Models

12. Build Reasoning Models new

Course Events

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

Gradio, check!

This wraps up the chapter on building cool ML demos with Gradio - we hope you enjoyed it! To recap, in this chapter we learned:

How to create Gradio demos with the high-level Interface API, and how to configure different input and output modalities.
Different ways to share Gradio demos, through temporary links and hosting on Hugging Face Spaces.
How to integrate Gradio demos with models and Spaces on the Hugging Face Hub.
Advanced features like storing state in a demo or providing authentication.
How to have full control of the data flow and layout of your demo with Gradio Blocks.

If you’d like to test your understanding of the concepts covered in this chapter, check out the quiz in the next section!

If you want to learn more about Gradio you can

Take a look at Demos in the repo, there are quite a lot of examples there.
See the Guides page, where you can find guides about cool and advanced features.
Check the Docs page to learn the details.