Hugging Face

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What is Hugging Face?

Hugging Face (HF) is a company that provides artificial intelligence (AI) researchers, developers, and enthusiasts with the tools and resources they need to use, build and share machine learning (ML) models.


According to Clément Delangue, Hugging Face’s CEO, the company’s mission is to make state-of-the-art AI accessible to as wide an audience as possible and, in the process, increase transparency across the AI ecosystem.

Hugging Face is often described as “GitHub for AI.” Just as GitHub features a central repository and collaborative space for software development, Hugging Face features a central repository and collaborative space for machine learning models. Both platforms have very active communities. 

What is the Hugging Face Hub?

Hugging Face Hub (HF Hub) is a web-based development platform that allows registered members to upload and share pre-trained ML models, download and access pre-trained models, and fine-tune pre-trained models. 

The platform is designed to foster collaboration among the HF community and provides resources that allow community members to discover and explore machine learning models, track model usage and performance, and deploy models in production environments.

Hugging Face Hub was initially geared towards natural language processing (NLP) and text-to-text large language models (LLMs), but today the platform is widely regarded as the go-to place for pre-trained models that can analyze and understand visual information or process and understand audio signals. 

Currently, the Hugging Face Hub features almost 500,000 models, 100,000 datasets, and 150,000 demo apps (Spaces) that are hosted as Git repositories. While most platform features are accessible for free, some advanced functionalities, such as fine-tuning access for private models and resource allocation for training jobs, require a paid subscription.

Components of the Hugging Face Hub

Standard components of the Hugging Face Hub include: 

  • Hugging Face Model Repository: This repo allows members to store, share, and explore over 350k pre-trained machine-learning models. It also provides community members with the resources they need to store and share their own models. 
  • Hugging Face Model Cards: This repo provides community members with detailed information about a specific machine learning model’s capabilities and limitations. Some cards also provide suggestions for how to use the model to its best advantage.
  • Hugging Face Datasets: This repo provides community members with the ability to upload and/or share their own datasets and browse and download existing datasets. It also provides tools that allow members to explore and analyze specific datasets to understand the data’s suitability for a specific need and prepare downloaded datasets for training purposes.
  • Hugging Face Spaces: This repo provides community members with resources and tools for deploying and sharing live machine-learning models. Members can use shared access to evaluate model performance and gain a deeper understanding of a model’s capabilities and limitations.
  • Hugging Face Inference API: This repo provides community members with an application programming interface (API) they can use to deploy pre-trained machine learning models in their software applications. The API is designed to be user-friendly and make it as easy as possible for developers with varying skills to leverage advanced AI capabilities.

History of Hugging Face

Hugging Face was founded in 2016 and started out as a chatbot company. The company’s initial product was a conversational agent that could learn from user interactions. The chatbot, which was named after the hugging face emoji, was quite popular among younger users and was known for its engaging and humorous interactions.

While the Hugging Face chatbot was somewhat successful, the team behind Hugging Face realized that the underlying technology powering their chatbot had greater potential for democratizing access to machine learning and empowering individuals and organizations to build intelligent applications.

The field of natural language processing was rapidly advancing, and they saw an opportunity to develop an open-source platform that would make it easier for everyone to leverage the power of NLP and build AI-powered applications without needing to start from scratch. 

Hugging Face’s release of the Transformer Library in 2018 marked a significant turning point in the company’s trajectory. Originally intended for internal use, the library’s public release proved to be immensely popular and quite eye-opening.

It wasn’t long before Hugging Face began to gradually shift its focus from consumer-focused chatbots to providing tools and resources for developers to build applications that incorporated AI. 

As the library’s popularity continued to grow, Hugging Face continued to expand its developer offerings. In 2019, they launched a web-based model hub that allowed AI researchers and developers to share and try out pre-trained large language models and other AI models across various domains.

This further established the company’s position as a central repository for AI models, not just in NLP but also in other areas of AI.

In 2020, Hugging Face was able to raise significant funding from venture capitalists. The funding allowed the company to put all its focus on building out its platform for developers and expanding the number of tools and resources it provided community members. 

Today, over 15,000 companies are said to be using HF technology in production, there are thousands of active APIs, and the company is valued at $4.5 billion. The company currently offers several tiers of subscription plans with varying features and benefits.


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Margaret Rouse
Technology Expert
Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.