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Hugging Face

The GitHub of AI: a place to find, test, share and run open models and datasets.

By Hugging Face

Jump to the full step-by-step walkthrough ↓

Quick demo

What it is

Hugging Face is a website and community where people publish AI models, datasets, and small demo apps called Spaces. It also makes popular open-source libraries such as Transformers. If a company releases open weights, they usually appear on Hugging Face first.

Why people use it

• Free access to thousands of open models for text, images, audio and video.
• You can try a model in the browser before writing any code.
• It is the standard place to publish your own model or dataset, which looks great on a portfolio.

How to get started

1. Create a free account at huggingface.co.
2. Browse Models and filter by task (text generation, text-to-image, speech, and so on).
3. Open a model page and read the model card: what it does, license, limits.
4. Try it in the page widget or a Space. No code needed.
5. When ready, run it in Google Colab (free GPU time) or call it through an API.

Try this

from transformers import pipeline

classifier = pipeline('sentiment-analysis')
print(classifier('Our new Medicare guide helped so many clients!'))

Your first code: 3 lines to run a sentiment model. Run it in Google Colab so you install nothing.

Why it matters for business

open models let a startup avoid per-use API costs and keep data in-house, at the price of managing infrastructure.

Full walkthrough: find, test and run your first model

1. The idea in business terms

Think of Hugging Face as an app store plus a library for AI. Instead of paying a company every time you use its AI, you can often download a free "open" model and run it yourself. Your job is to choose the right one and check you are allowed to use it.

2. Create your account and look around

1. Sign up free at huggingface.co and verify your email.
2. Click Models. On the left are filters for task (what the model does), language and license.
3. Choose a task, such as Text Classification or Text-to-Image, and sort by Trending or Most downloads.

3. Read a model card like a buyer

Open any model and read its model card (the main text on the page). Check these five things before you use it:

• What it does and what it was trained for.
• License: some are free for any use (such as Apache 2.0 or MIT); some limit commercial use. If you plan to make money, read this first.
• Limits and bias: the card should say what it is bad at.
• Size: a large model needs a powerful computer or a paid GPU.
• Popularity and updates: recent activity and many downloads usually mean it is maintained.

4. Try it with no code

Many pages have a box on the right where you can type text and see the answer. Others link to a Space, a small demo app. Test with 10 examples from your own work, not just the sample.

5. Run it in Google Colab (free)

1. Go to colab.research.google.com and sign in with a Google account. Click New notebook.
2. In the first cell, install the library by typing !pip install transformers and press Shift+Enter to run it.
3. In the next cell, paste the code from the Try this section above and run it.
4. The first run downloads the model, so it takes a minute. After that you see the result printed under the cell.
5. Change the sentence in the quotes and run again to test your own text.

If a model is big, choose Runtime, then Change runtime type and select a GPU. The free tier has limits.

6. Compare and decide

Test two or three models on the same 20 examples that you labeled yourself. Count how many each one got right. The cheapest model that is accurate enough usually wins.

7. Publish your own work

Once you build something (see Build Your Own Model), create a model page, upload it, and write a model card using the same five points above. Link it from your resume and GitHub profile.

Common problems

• "Out of memory": the model is too big. Choose a smaller version or use a GPU.
• Access denied: some models require you to accept terms on the model page first.
• Weird answers: you may be using a model made for a different task. Check the task label.
• Privacy: do not paste customer data into public Spaces. Run it yourself.

Business use case

Quick facts

• Free to use; paid options exist for hosting and enterprise features.
• Key words: Hub, model card, dataset, Space, Transformers, open weights, license.
• Always check the license before using a model commercially.

What to learn next

Follow the free Hugging Face LLM course, then do the 'Build your own model' guide in this repo.