Summary of "What Is Hugging Face and How To Use It"

Overview

This video introduces Hugging Face as an AI platform and walks through how to use it to:

Try existing models
Build and customize your own AI apps
Deploy and/or run them locally

What Hugging Face Is (Platform Overview)

Hugging Face is described as an open-source AI community and a global hub for open-source large language models, including models from companies such as:

Meta
Google
DeepSeek
Alibaba

It’s positioned as a place where companies publish new AI models first. The video also claims there are 1.5M+ open-source AI models available.

Cost notes

It’s free to try (for many features)
GPU usage can require payment, with the mentioned plan price being $9/month (the speaker subscribes)

Main Hugging Face Sections (Core Product Features)

1) Spaces (Best for Non-Technical Users)

Spaces is a page of ready-to-run apps categorized by type, such as:

Image generation
Video generation
Text/language generation
Speech synthesis
3D modeling

It includes discovery features like:

“Spaces of the Week”
“Trending running apps”
Filtering by space type

Demo examples

Relighting
- Upload an image
- Choose a background
- Click Relight
- Get a before/after result where the object is blended into the new background in ~3 seconds (intended to look more realistic)
Sesame CSM
- Generates/uses two speakers
- Speaks user-provided text with a voice persona-like effect
- The speaker notes it’s not perfect, but it’s quickly testable

2) Model Hub

The Model Hub provides access to models categorized by domain, such as:

Computer vision
NLP
Audio

Models are shown as open source and can be sorted—trending is the default view.

Model pages also link to:

Spaces that use the model
Options to deploy or use the model
A community/discussion area

3) Datasets

Datasets are described as inputs for model training and fine-tuning.

Key capabilities mentioned:

Filter by language and task
Inspect dataset contents
Run SQL queries (as mentioned)
Download datasets

The emphasized use case is fine-tuning your own model.

Tutorial: Building and Customizing an AI Image Editing App

The speaker demonstrates building an app using the latest Google Gemini update via the Gemini API.

Practice demo

Upload an image (e.g., a girl)
Provide an editing prompt (e.g., make her eat french fries instead of holding something)
The demo mentions trying the app for free initially
Later, you can use your own Gemini API key if desired

How to Customize and Run Your Own Version

The workflow described:

Duplicate the Space / repo
Add your secret (API key) and duplicate
For self-hosting/customization, use the public code
- Download/clone the repo
- Clone into Cursor (the code editor mentioned)
Use Cursor Agent to help step-by-step:
- Create a Python virtual environment
- Install requirements
- Configure the Gemini API key
Run the app locally:
- It serves a local URL
- After providing an API key, prompting works and image generation/editing begins

Example Customization Prompt

The speaker shows changing the output aesthetic/style through prompt instructions, for example:

“Sailor Moon colors and cute fonts”

After accepting changes, the app is run again to produce different outputs.

Note: The speaker mentions considering licensing/credits when reusing shared code.

Overall Takeaway / Intent

Hugging Face is presented as:

A large library (Model Hub)
A discovery/testing platform (Spaces)
A training resource (Datasets)

It also provides a practical way to prototype and host custom AI apps using shared repos and API keys.

Main Speakers / Sources

Speaker: “Hey guys” host (single speaker; no other named co-hosts)
Primary referenced product/system: Hugging Face (Spaces, Model Hub, Datasets)
External model/API referenced: Google Gemini (via Gemini API key)