The Hugging Face Diffusion Models Course provides course materials for studying diffusion theory and image generation.
Open source
24 alternativesHugging Face LLM Course
The Hugging Face LLM Course teaches large language model concepts through documentation and related learning resources.
About Hugging Face LLM Course
The Hugging Face LLM Course teaches large language models and natural language processing using Transformers, Datasets, Tokenizers, Accelerate, and the Hugging Face Hub, along with selected libraries outside the Hugging Face ecosystem. Chapters move from Transformer basics, using models and fine-tuning, through Datasets, Tokenizers and common language tasks, to building demos and advanced topics such as dataset curation and reasoning models.
It requires good knowledge of Python and is intended to be taken after an introductory deep learning course. Prior PyTorch or TensorFlow knowledge is not expected, though some familiarity helps.
Key features
- Use Transformer models from the Hugging Face Hub
- Fine-tune models on datasets and share results on Hub
- Learn Datasets and Tokenizers basics for language tasks
- Build and share model demos on the Hub
- Study fine-tuning, dataset curation, and reasoning models
- Explains NLP foundations alongside large language model techniques
Best for: Python users with introductory deep learning background who want to learn LLMs with Hugging Face tools.
Good for
- Completely free and without ads
- Covers Hugging Face libraries and selected outside libraries
- Structured progression from Transformer basics to advanced LLM topics
Watch out for
- Requires good knowledge of Python
- Better taken after an introductory deep learning course
Hugging Face LLM Course at a glance
- Type
- Online tool
- Pricing
- Free
- Platforms
- Web
- Open source
- Not stated
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Hugging Face LLM Course FAQ
Do I need to know PyTorch or TensorFlow?
No. The course does not expect prior PyTorch or TensorFlow knowledge, though some familiarity will help.
What background is needed for the course?
It requires good knowledge of Python and is better taken after an introductory deep learning course, such as fast.ai's Practical Deep Learning for Coders.
What topics does the course cover?
Chapters 1 to 4 cover Transformers basics, chapters 5 to 8 cover Datasets, Tokenizers and language tasks, chapter 9 covers building demos, and chapters 10 to 12 cover advanced LLM topics.
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