Welcome to the 2nd best place on the internet to learn the Hugging Face ecosystem (the first is the official docs) and a bunch of AI and ML concepts along the way.
This website dedicated to teaching the Hugging Face ecosystem with practical examples.
Each example will include an end-to-end approach of starting with a dataset (custom or existing), building and evaluating a model and creating a demo to share.
Teaching style: a machine learning cooking show! 👨🍳
We focus on starting with raw ingredients such as a dataset or base model and then we customize them to our own liking before uploading the fine-tuned model and custom demo to Hugging Face so other people can try it out.
This will be our (rough) workflow:
A general Hugging Face workflow from idea to shared model and demo using tools from the Hugging Face ecosystem. These kind of workflows are not set in stone and are more of guide than specific directions. See information on each of the tools in the Hugging Face documentation.
Tip
If you would like step-by-step guidance through each of the projects, they are all available as video courses on the Zero to Mastery platform.
You can sign up and go through them at your own liesure.
| Project | Description | Dataset | Model | Demo | Video Course |
|---|---|---|---|---|---|
| 0 - Text classification | Build project “Food Not Food”, a text classification model to classify image captions into “food” if they’re about food or “not_food” if they’re not about food. This is the ideal place to get started if you’ve never used the Hugging Face ecosystem. | Dataset | Model | Demo | Video Course |
| 1 - Object Detection | Build Trashify 🚮, an object detection model to detect “trash”, “hand”, “bin” to incentivize people to clean up their local area. Start with a dataset, customize an open-source object detection model and turn it into a demo application that others can use and try out on their own images. | Dataset | Model | Demo | Video Course |
| 2 - LLM Full Fine-tuning | Fully fine-tune Google’s Gemma 3 270M model to perform structured data extraction on any kind of text. | Dataset | Model | Demo | YouTube |
| 3 - VLM Fine-tuning | Fine-tune a small VLM model, SmolVLM2-500M for structured data extraction from images. | Dataset | Model | Demo | YouTube |
| 4 - Multimodal RAG (Retrieval Augmented Generation) | Level up your text-based RAG pipelines and learn how to embed text and images of documents into a shared embedding space. This allows you to query over a dataset of combined images and text. | Dataset | Model (embed) / Model (rerank) | Demo | YouTube |
| More to come soon! | Let me know if you’d like to see anything specific by leaving an issue. |
| Extension | Description |
|---|---|
| Perform batched inference with an LLM and Hugging Face Transformers | Take an existing LLM and learn how to speed up prediction times by batching together samples. This is helpful if you need to perform inference over a large dataset. Uses a small model to run on Google Colab or locally. |
- 16 Apr 2026 - Add batched inference with Hugging Face Transformers Notebook, this extends from the LLM fine-tuning notebook and helps speedup inference significantly.
- 1 Apr 2026 - Fully finished LLM fine-tuning notebook, recorded all video course videos for the notebook, they will be available soon on ZTM.
- 26 Feb 2026 - Update links to LLM fine-tuning, VLM fine-tuning, Multimodal RAG notebooks (these all work, however, I’m in the process of tidying them up). Add dark mode to notebooks.
- 08 Jan 2026 - Add first iteration of full fine-tuning notebook for Gemma 3 270M (fully fine-tune a Small Language Model for structured data extraction)
- 07 Nov 2025 - Videos for the object detection project are now available on ZTM in the Hugging Face Bootcamp!
- 18 June 2025 - All code has been completed for the object detection project, train a custom object detection model and make a demo with it for others to try! (video course to come soon)
- 1 Oct 2024 - Video course version of text classification is live on ZTM! Inside, we’ll walkthrough every line of code building the text classification project with Hugging Face Datasets, Transformers and Spaces.
Ideal for:
- Beginners who love things explained in detail.
- Someone who wants to create more of their own end-to-end machine learning projects.
Not ideal for:
- People with 2-3+ years of machine learning projects & experience^.
^Note: This being said, you may actually find some things helpful along the way. Best to explore and see!
The course is very hands-on. We teach concepts interweaved with code and always push towards deploying publishing real-world applications.
Our project style will be: data, model, demo.
- Data – Create a new/reuse an existing dataset.
- Model – Train/evaluate a model (Hugging Face hosts many 1000s of models we can download and use).
- Demo – Build a demo to share (a demo is one of the quickest ways to let other people try your work).
As practitioners, we’ve got several mottos:
- If in doubt, run the code. – Machine learning is very experimental. So it’s good to get in the habit of continually trying things (even if you think they won’t work).
- Visualize, visualize, visualize! - If you’re not sure of some dataset or some operation or some predictions, visualize it/them.
- Experiment, experiment, experiment! - Again, machine learning is very experimental. So keep trying different things!
- Data, model, demo! - Create/get a dataset, build/train/evaluate a model, create a demo to share.
- 3-6 months Python experience.
- 1x beginner machine learning or deep learning course (see my
begineer-friendly ML course to
learn Python + important ML concepts in one).
- PyTorch experience is a bonus (see my Learn PyTorch in a Day video or learnpytorch.io)
Hugging Face is a platform that offers access to many different kinds of open-source machine learning models and datasets.
They’re also the creators of the popular transformers
library (and many
more helpful libraries) which is a Python-based library for working with
pre-trained models as well as custom models.
If you’re getting into the world of AI and machine learning, you’re going to come across Hugging Face.
A handful of pieces from the Hugging Face ecosystem. There are many more available in Hugging Face documentation.
Many of the biggest companies in the world use Hugging Face for their open-source machine learning projects including Apple, Google, Facebook (Meta), Microsoft, OpenAI, ByteDance and more.
Not only does Hugging Face make it so you can use state-of-the-art machine learning models such as Stable Diffusion (for image generation) and Whipser (for audio transcription) easily, it also makes it so you can share your own models, datasets and resources.
Aside from your own website, consider Hugging Face the homepage of your AI/machine learning profile.
- Ecosystem overview: transformers, datasets, accelerate, Spaces, Hub, models etc
- Finish outline of this (index.md) page
- Copy a similar version to the README.md for GitHub
- Make share image for the whole thing
- Make index of different projects
- Where to get help? HF forums, HF GitHub, etc
- Finish setup page
- Local setup
- Finish deployment to learnhuggingface.com page
- Get started: text classification shows an end-to-end workflow with
detailed steps, I’d advise starting here to get to know the ecosystem
a bit
- Other projects are more focused on specific tasks with less explanations but still complete code examples
Is this an official Hugging Face website?
No, it’s a personal project by myself (Daniel Bourke) to learn and help others learn the Hugging Face ecosystem.
How is this website made?
This is a Quarto website.
To learn more about Quarto websites visit https://quarto.org/docs/websites.