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

Introduction What to do when you get an error Asking for help on the forums Debugging the training pipeline How to write a good issue Part 2 completed!End-of-chapter quiz

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

Part 2 completed!

Congratulations, you’ve made it through the second part of the course! We’re actively working on the third one, so subscribe to our newsletter to make sure you don’t miss its release.

You should now be able to tackle a range of NLP tasks, and fine-tune or pretrain a model on them. Don’t forget to share your results with the community on the Model Hub.

We can’t wait to see what you will build with the knowledge that you’ve gained!

Update on GitHub

←How to write a good issue End-of-chapter quiz→