What is Hugging Face?

Hugging Face has cultivated one of the most vibrant AI communities in the world, with users contributing new AI models, datasets, tutorials and research daily. They offer a rich API that allows developers to integrate models directly into applications, and their platform supports a wide range of tasks across many use cases and industries. Here are some of the platform’s main advantages:

• Access to the latest models

• Simplified workflows

• Simple deployment and scaling

• Thriving community

• Focus on responsible AI

Access to the latest models

Hugging Face provides access through its Model Hub to thousands of pre-trained models for tasks like speech recognition, text classification, text generation, text summarization, question answering, image generation and more. The Model Hub behaves like a marketplace where users can easily find models and download and fine-tune them in just a few lines of code, saving developers and researchers time and resources compared to training from scratch.

Simplified workflows

Hugging Face libraries are known for being user-friendly and well-documented. Novices can quickly fine-tune powerful models and perform complex tasks like distributed training, tokenization, evaluation and deployment using Hugging Face tools. Access to fundamentals and advanced tools alike has opened up AI development to a much winder community of practitioners.

Simple deployment and scaling

Beyond training, Hugging Face makes it easy to deploy models into production. Hugging Face tools allow users to serve models to the web, mobile apps or internal systems without needing a deep infrastructure background. This full-stack support makes the platform especially attractive for startups and enterprises.

Thriving community

Apart from all of the readily-accessible technology, Hugging Face’s vibrant community has made it a destination for developers, data scientists and researchers. It’s a place for inexperienced developers to learn from seasoned practitioners and ask questions of people who may have already faced similar challenges.

Focus on responsible AI

Many Hugging Face models come with documentation about their limitations, biases and intended use cases. The company invests heavily in open governance and community-led discussions about AI ethics.

Before Hugging Face, the most powerful models were often difficult for people to use because they required specialized expertise and massive computing resources. Open-sourcing the tools helped to make these models easier to use, with all the code and documentation required. This allowed researchers, students and startups to experiment and build, which massively accelerated innovation globally. After Hugging Face, developers could easily share knowledge and benefit from one another’s efforts, enabling them to create better models together.

This open source emphasis also encouraged larger businesses to share their work, allowing the entire ecosystem to benefit. Microsoft has integrated Hugging Face models into their Azure services, providing enterprise customers with direct access to state-of-the-art AI tools. Similarly, NVIDIA has collaborated with Hugging Face to optimize model training and inference for GPUs, helping scale deep learning workflows to massive datasets.

Hugging Face was founded by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf in New York City in 2016.¹ The entrepreneurs were originally interested in building chatbots for teenagers, but recognizing the power of models underlying chatbot technology, pivoted to the models themselves.

They open-sourced their internal tools and launched the first version of the Hugging Face Transformers Library, which quickly became a popular with researchers and engineers. Hugging Face became a definitive source for pretrained transformer models, and in 2020, the company introduced the Hugging Face Hub, their model repository, which enabled users to easily upload, download and share models. The following year, they launched their Datasets library, which made sharing datasets easier, and Hugging Face Spaces for deploying interactive AI demos. In 2022, the company acquired Gradio, an open source AI library for developing machine learning applications in Python.²

Hugging Face has released tools for multimodal models, large language models (LLMs), diffusion models and reinforcement learning. In 2023, Hugging Face began collaborating with IBM on watsonx.ai, IBM’s AI studio that allows users to train, validate, tune and deploy both traditional ML and then-new generative AI capabilities. Later that year, IBM participated in a Series D funding round for Hugging Face.

Here are Hugging Face’s primary services:

Hugging Face Hub

The Hugging Face Hub is a central web-based platform where users can share, discover and collaborate on models, datasets and applications. It acts like a "GitHub for AI," hosting thousands of publicly available resources. Model and dataset pages include documentation, examples, version tracking and live demos in many cases. The Hub also supports private repositories for teams and enterprises for secure collaboration.

Transformers Library

The Transformers library is one of the most widely used tools for NLP, computer vision and deep learning models. It’s a Python library that users install on their computers or servers that provides code that lets them use the models they find on the Hub. It includes model architectures, preprocessing tools, training utilities and more. Built on top of popular frameworks like PyTorch and TensorFlow, the Transformers library allows users to load powerful ML models like BERT, GPT and others with just a few lines of code. It also offers extensive tools for fine-tuning open source models on custom datasets, making it more useful for research and production.

Other libraries

In addition to Transformers and the Hub, the Hugging Face ecosystem contains libraries for other tasks, such as dataset processing ("Datasets"), model evaluation ("Evaluate"), and machine learning demos ("Gradio").