Hugging Face Resources Every AI Developer Should Know 🤗
A practical collection of the courses, libraries and hands-on tools I keep coming back to when learning or building with open-source AI.
If you're getting into AI, LLMs, or open-source models, Hugging Face is one of those platforms you should probably bookmark. But the ecosystem is huge, and it can be difficult to know where to start.
Here are a few resources I keep coming back to.
1. 🤗 Hugging Face Learn
The best place to start if you actually want to understand what's happening under the hood. There are courses covering LLMs, Agents, Computer Vision, Audio, Diffusion, Deep RL, and more.
The LLM Course is especially useful if you're working with Transformers and LLMs. Start the LLM Course.
2. 📚 Hugging Face Course
If you prefer learning by building, check out the official Hugging Face course. It covers Transformers, Datasets, Tokenizers, Accelerate and the Hugging Face Hub.
Open the course or browse the course source on GitHub. It's completely free and open source.
3. 🧠 Transformers
This is probably the Hugging Face repository you'll see the most. You can use it to work with pretrained models for text, vision, audio and multimodal tasks.
Visit the Transformers repository and its documentation. If you're new, start with the Quickstart instead of trying to understand the entire repository.
4. 🤖 smolagents
Want to build AI agents without dealing with a massive framework? Take a look at smolagents.
It's a lightweight open-source framework from Hugging Face for building agents that can use tools and execute actions. Visit smolagents on GitHub or read the documentation.
5. 💡 GeeksforGeeks
Sometimes you don't need 100 pages of documentation. The Hugging Face tutorials on GeeksforGeeks are useful when you want a simpler explanation of concepts like Transformers, tokenization, pipelines and RAG.
6. 🔥 Explore the Hub
And finally, don't just read about models. Play with them. The Hugging Face Hub has models, datasets and Spaces that you can explore, test and build on.
My recommended path
GeeksforGeeks → Hugging Face Learn → Transformers → Hugging Face Hub → Build something → Read the source code
Don't try to learn the entire ecosystem first. Pick one model, build one small project, break it, fix it, and repeat.
That's probably a much better way to learn AI than collecting 50 bookmarks you'll never open again.