A longstanding educational resource focused on natural language processing is undergoing a significant transformation to reflect the latest advances in artificial intelligence. Building on years of development, the material is being refreshed and expanded into a course centered on large language models. This update represents a commitment to keeping pace with rapid innovations in the field and ensuring that learners have access to both foundational topics and emerging research.
While the classic content covering traditional NLP tasks—such as text classification, named entity recognition, and retrieval—will continue to be available, these segments will be modernized. The updated chapters aim to incorporate contemporary approaches like Sentence Transformers, improved techniques for zero-shot classification, and the insights detailed in ModernBert. The enduring value of these simpler, locally-run methodologies remains a priority for students seeking clear and interpretable models.
The refresh also brings the promise of entirely new chapters. These additions are designed to make state-of-the-art research more accessible through integration with popular tools and platforms. The revamped content will include topics such as fine-tuning, inference, and advanced retrieval techniques. A notable strength of the new chapters is the collaborative approach, which involves contributions from multiple communities and draws on various frameworks beyond just the Hugging Face libraries. For instance, chapters detailing fine-tuning large language models and exploring reasoning models have been developed in partnership with:
This collaborative spirit is set to grow as the course evolves, welcoming more authors, maintainers, and organizations to contribute to the best tools and methodologies in use today.
Interactive learning will also play a role where it proves most beneficial. Besides a solid base of written explanations and coding exercises that are designed to remain relevant over time, additional interactive exercises and live sessions will be introduced for topics that generate high levels of community engagement.
Anyone interested in exploring these changes and contributing to the ongoing evolution of the course is encouraged to get involved. You can follow the organization’s updates on their hub at https://huggingface.co/huggingface-course, join the discussion, suggest new interactive elements, or propose live sessions that could enhance the learning experience.
Additional details and continuous updates regarding the course can be tracked via the update posted on the project’s GitHub repository at https://github.com/huggingface/blog/blob/main/llm-course.md.
Image credit: Hugging Face – Blog
