Hugging Face LLM Course

This course will teach you about large language models (LLMs) and natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub.

We’ll also cover libraries outside the Hugging Face ecosystem. These are amazing contributions to the AI community and incredibly useful tools.

It’s completely free and without ads.

While this course was originally focused on NLP (Natural Language Processing), it has evolved to emphasize Large Language Models (LLMs), which represent the latest advancement in the field.

Throughout this course, you’ll learn about both traditional NLP concepts and cutting-edge LLM techniques, as understanding the foundations of NLP is crucial for working effectively with LLMs.

Here is a brief overview of the course:

• Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the Hugging Face Hub, fine-tune it on a dataset, and share your results on the Hub!
• Chapters 5 to 8 teach the basics of 🤗 Datasets and 🤗 Tokenizers before diving into classic NLP tasks and LLM techniques. By the end of this part, you will be able to tackle the most common language processing challenges by yourself.
• Chapter 9 goes beyond NLP to cover how to build and share demos of your models on the 🤗 Hub. By the end of this part, you will be ready to showcase your 🤗 Transformers application to the world!
• Chapters 10 to 12 dive into advanced LLM topics like fine-tuning, curating high-quality datasets, and building reasoning models.

This course:

• Requires a good knowledge of Python
• Is better taken after an introductory deep learning course, such as fast.ai’s Practical Deep Learning for Coders or one of the programs developed by DeepLearning.AI
• Does not expect prior PyTorch or TensorFlow knowledge, though some familiarity with either of those will help