Hugging Face

Hugging Face, Inc. is a French-American company based in New York City, founded in 2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf. The company started as a chatbot app for teenagers but quickly pivoted to focus on machine learning tools. Today, Hugging Face is renowned for its Transformers library, which provides open-source models for natural language processing, computer vision, and audio tasks. This library supports popular deep learning frameworks like PyTorch, TensorFlow, and JAX, making it a versatile tool for AI developers.

The company's platform, Hugging Face Hub, allows users to share machine learning models, datasets, and web applications, fostering a collaborative environment for AI research and development. Hugging Face has also expanded its offerings with additional libraries like Gradio for machine learning demos, Datasets for data manipulation, and PEFT for efficient model fine-tuning. Their services are used by over 50,000 organisations, including major companies like Google, Amazon, and Microsoft.

In recent years, Hugging Face has seen significant growth, with 170 employees and a $4.5 billion valuation after its latest funding round. The company continues to innovate, partnering with Amazon Web Services and launching initiatives like the AI accelerator program for European startups. With a focus on open-source development and community collaboration, Hugging Face is at the forefront of advancing AI technology and making it accessible to a wider audience.

Hugging Face, Inc. was founded in 2016 by three French entrepreneurs, Clément Delangue, Julien Chaumond, and Thomas Wolf. The company started in New York City, initially developing a chatbot app designed to engage teenagers. Named after the "hugging face" emoji, the company quickly realised the potential of their underlying technology and decided to pivot its focus towards machine learning.

Their big break came with the creation of the Transformers library, an open-source toolkit designed to make advanced natural language processing (NLP) accessible to developers. The library provides implementations of transformer models for text, image, and audio tasks. These models, such as BERT and GPT-2, have become essential tools in the AI community. The library supports major deep learning frameworks like PyTorch, TensorFlow, and JAX, making it a versatile resource for AI researchers and practitioners.

Hugging Face didn't stop there. They launched the Hugging Face Hub, a platform where users can host and share machine learning models, datasets, and web applications. This hub has become a centralised service for the AI community, allowing developers to collaborate, share their work, and access a vast array of pre-trained models for various tasks. The platform supports tasks across different modalities, including text classification, image segmentation, and audio recognition.

In March 2021, Hugging Face raised $40 million in a Series B funding round, which allowed them to expand their operations and further develop their offerings. A significant milestone was the launch of the BigScience Research Workshop in April 2021, a collaborative effort with other research groups to create an open large language model. This initiative culminated in the creation of BLOOM, a multilingual large language model with 176 billion parameters, announced in 2022.

Continuing its growth, Hugging Face acquired Gradio in December 2022, an open-source library for building machine learning applications in Python. This acquisition added to their suite of tools, making it easier for developers to create interactive machine-learning applications. In May 2022, the company announced a Series C funding round led by Coatue and Sequoia, valuing the company at $2 billion. This round was followed by the launch of the Private Hub in August 2022, an enterprise version of their public hub, offering both SaaS and on-premises deployment options.

In February 2023, Hugging Face partnered with Amazon Web Services (AWS), integrating their products with AWS services to provide more robust solutions for developers. This partnership also included plans to run the next generation of BLOOM on AWS's proprietary machine-learning chip, Trainium. By August 2023, Hugging Face had raised $235 million in a Series D funding round, achieving a valuation of $4.5 billion. This funding round was led by Salesforce, with significant participation from Google, Amazon, Nvidia, AMD, Intel, IBM, and Qualcomm.

Further solidifying their position in the AI ecosystem, Hugging Face, along with Meta and Scaleway, announced in June 2024 the launch of a new AI accelerator programme for European startups. This programme, based at STATION F in Paris, aims to help startups integrate open foundation models into their products, thereby accelerating the development of the EU AI ecosystem. The programme offers mentoring, access to AI models and tools, and computing power provided by Scaleway.

Throughout their journey, Hugging Face has maintained a strong focus on open-source development and community collaboration. With over 50,000 organisations using their services, including industry giants like Google, Amazon, and Microsoft, Hugging Face has firmly established itself as a leader in the AI and machine learning space. Their commitment to making advanced AI tools accessible and fostering a collaborative community continues to drive their innovations and success.

Hugging Face's mission is to democratise artificial intelligence by making advanced AI tools accessible to everyone. They aim to empower developers and researchers by providing easy-to-use, open-source machine-learning models and datasets. Their goal is to promote collaboration within the AI community, encouraging innovation and sharing of knowledge. Hugging Face believes in the power of community-driven development and strives to create an inclusive environment where anyone can contribute and benefit from AI technology. They are committed to transparency, open science, and ethical AI practices, ensuring their tools are used to positively impact society.

Hugging Face's vision is to create a future where artificial intelligence is a force for good, benefiting people worldwide. They aim to be the leading platform for machine learning, providing the best tools and resources for developers and researchers. By promoting a collaborative and inclusive AI community, they envision a world where advanced AI technology is accessible to everyone, driving innovation and solving real-world problems. Hugging Face is dedicated to maintaining transparency, promoting ethical AI practices, and ensuring its tools are used responsibly to create positive social and economic impacts globally.

Hugging Face offers a range of products and services designed to make advanced machine-learning tools accessible and easy to use for developers, researchers, and businesses. Their core offerings include the Transformers library, the Hugging Face Hub, and a variety of other specialised libraries and tools.

Transformers Library

The Transformers library is Hugging Face's flagship product. It is an open-source library that provides pre-trained models for natural language processing (NLP) tasks. These models include BERT, GPT-2, RoBERTa, and many others, which are used for tasks such as text classification, translation, summarisation, and question-answering. The library is compatible with major deep learning frameworks like PyTorch, TensorFlow, and JAX, making it flexible and widely usable. It simplifies the implementation of transformer models, allowing users to integrate advanced NLP capabilities into their applications with ease.

Hugging Face Hub

The Hugging Face Hub is a collaborative platform where users can share machine learning models, datasets, and applications. It serves as a repository for over 100,000 models covering a wide range of tasks and modalities, including text, image, and audio processing. Users can host their models and datasets on the Hub, making it a centralised space for the machine learning community to collaborate and innovate. The Hub also supports version control, discussions, and pull requests, facilitating a community-driven approach to AI development.

Datasets Library

Hugging Face's Datasets library is designed to make it easy to find, use, and share datasets. It includes thousands of datasets for various tasks, such as text, image, and audio processing. The library provides efficient data loading and processing tools, enabling users to work with large datasets without significant overhead. This library is a valuable resource for anyone looking to train machine learning models, as it simplifies the data preparation process.

Gradio

Gradio is an open-source library that allows users to quickly create interactive user interfaces for their machine-learning models. These interfaces can be used to demo models, gather user feedback, and make machine learning applications more accessible to non-technical users. Gradio supports various input and output types, including text, images, audio, and video, making it versatile for different kinds of applications.

Spaces

Spaces are a feature on the Hugging Face Hub that allows users to host and share web applications built with their machine learning models. These applications can be simple demos or full-fledged services. Spaces are powered by popular frameworks like Streamlit, Gradio, and Flask, providing an easy way for users to showcase their models and interact with them online.

Private Hub

The Private Hub is an enterprise version of the Hugging Face Hub. It provides all the features of the public Hub but with added security and privacy controls. This is particularly useful for businesses that need to keep their models and data confidential. The Private Hub can be deployed on-premises or used as a software-as-a-service (SaaS) solution, offering flexibility to meet different organisational needs.

Model Evaluation

Hugging Face offers the Evaluate library, which provides tools for assessing the performance of machine learning models. It includes standard metrics for various tasks and supports custom evaluation metrics. This library helps users understand how well their models are performing and identify areas for improvement.

Tokenizers

Hugging Face's Tokenizers library is designed for fast and efficient text tokenisation. It is optimised for both research and production environments, providing tools to convert text into tokens that machine learning models can process. The library supports various tokenisation techniques, ensuring compatibility with different models and languages.

Training Tools

Hugging Face provides a suite of tools to facilitate model training. This includes the Accelerate library, which simplifies training models on different hardware configurations, such as GPUs and TPUs. They also offer Optimum, a set of tools to optimise model performance during training and inference.