When someone says ‘Hugging Face’ in the realm of artificial intelligence and machine learning, we will nod our head as if we know exactly what he is talking about. Indeed, yes, we can get AI models at Hugging Face, that’s it. But is that all? Why should you use the platform? And how does it work?

I go on to elaborate more about it below. To begin with: Hugging Face is a website where a person can create, share, and use, under one of the licenses, any number of uploaded pre-trained AI models.

In a broader view, it is something like GitHub/GitLab but for datasets and AI models, and not source code. You could also imagine Hugging Face as an equivalent of DockerHub in the world of AI. This is so because DockerHub publishes Docker images to the world, and Hugging Face models are the ones publishing AI models.

In other words, it is a paradise on earth for those enthusiasts of machine-learning and Generative AI. Here are over 9,00,000 models on the platform, and you can easily use each of them on your system in accordance with their usage instructions and license requirements.”

But, what makes Hugging Face so special? Let us take a closer look at it.

History of Hugging Face

The transformation to an artificial intelligence model database from a single project of a chatbot is cumbersome.
Well, that’s exactly what Hugging Face did. It all began in 2016, with the first thing on Hugging Face’s agenda being to create an AI interactive chatbot for teenagers.
All was fine until the company wanted to go open source with its bot. They figured they could be a force in the AI community. Then, pitched themselves as the central hub for AI models.
That year in 2023, it was estimated at $4.5 billion, with contributors including Amazon, Google, and Qualcomm.
Nowadays, you can’t take two steps into the conversation about most AI models without running into Hugging Face.

Here’s Why Hugging Face is a Game Changer

With many models accessible, it became extremely hard to collaborate and contribute in a machine learning project. Whether you’re a complete beginner or a seasoned vet, Hugging Face democratized access to AI models.

This time around, it was not just pulling their services out but actually getting deep into the back-end of the models themselves. Hugging Face made AI a bit more open in nature (even if not all the models are open-source). While the biggest companies discussed issues with their models, the community had already found the solution. But that is not where it stops; Hugging Face also gave people space to host their AI models.

This further led to easier collaboration with other people and, hence, more efficient models. Over and above that, you would be able to simply run the models with the inference API for a quick demo and do more with them as you scale. At that, add your contributions and activities with AI models to the portfolio you’re building. It also enables startups and companies of any size to easily deploy their AI models with a flexible hourly pricing plan.

Now we have enough insight into what really makes Hugging Face such a game-changer in the industry. So now, what are you actually able to use it for? What is inside it?”

Models on Hugging Face

While there are countless models available on the platform, I have listed the most prominent and useful types.

1. Transformer Models

A type of deep-learning model fundamental for Natural Language Processing (NLP). These models can translate text and speech in real life, making them super useful in Artificial intelligence.

A transformer model must first be trained on a sizable text dataset to be used. Thankfully, Hugging Face provides you with a pre-trained model. You can utilize the trained model for various natural language processing tasks.

A transformer model, for instance, can be used to create text, classify text, or respond to queries.

2. Image Classification Models

From dealing with MRI scans to image searches on your web answers, you have definitely made use of image classification models somewhere or the other. These models have found a use in almost every field.

These are algorithms that classify photos into pre-established groups or categories.

3. Image Generation Models

Whether you are a working professional, or just use artificially intelligent models for fun, almost everyone has heard of projects like Stable Diffusion. It is an image-generation model that generates images based on the prompts you provide.

You can expect similar AI models focused on generating images on Hugging Face.

4. Time Series Forecasting Models

Forecasting models are perhaps one of the most if not the most, widely used models in financial and industrial applications. These models are capable of predicting the future based on historical data.

These models are essential for following market trends, and keeping up with customer demands. While it is extremely useful, these models require a lot of data to set up and are harder to set up in real time. Thankfully, Hugging Face makes our lives just a tad bit easier.

One of the most downloaded models on Hugging Face is the Chronos T5, which is a time series forecasting model.

5. Voice Activity Detection Models

Have you ever wondered what kind of algorithm your Google Assistant or Siri uses to recognize and distinguish your voice? Half of the credit goes to Voice Activity Detection Models, or VAD for short.

The main purpose of these models is to distinguish between audio where speech is present and where speech is absent. This helps in speech recognition accuracy and preprocessing raw audio data.

Hugging Face provides you with the capability of creating many varieties of AI models. With the help of their database, you can create your favorite projects or help your business run.

At face value, Hugging Face seems like a perfect idea, but when you dive deeper into their ways, you might encounter some reasons for concern.

Hugging Face Isn’t Flawless

Earlier this year, a cloud security firm, Wiz found two serious architectural problems with Hugging Face.

The first concern was that someone could upload a malicious AI model, which could be used to gain unauthorized access to other customers’ data.

The second issue that raised eyebrows was that some AI-as-a-service platforms were discovered to have vulnerable container registries. Typically, container registries are utilized for storing and controlling container images. Attackers could tamper with others’ models by exploiting vulnerable container registries, potentially inserting harmful code.

Additionally:

• 100 malicious PyTorch and Tensorflow Keras models were identified on Hugging Face by JFrog.
• A PyTorch model named “baller423” was found to be exploiting Python’s pickle module to execute code remotely.

With services such as Hugging Face, cybercriminals will, in due course, avail of the opportunity to breach security and steal user data. Even so, the organization is trying its level best to improve security and protect users’ data.

Hugging Face has put scanners for malware and other measures in place to forestall these attacks. These capabilities scan all files in the repositories for malicious code, insecure deserialization, or sensitive information and alert users or moderators accordingly.

The fact that malicious actors were able to breach some of the imposed security barriers to get inside tells us that downloading models and trusting everything from Hugging Face should be done with care, at least with proper scrutiny.

And, what do you feel about Hugging Face? Does it make a useful platform? Share with me your thoughts by leaving a comment below.

A Note From the Author

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