This article relies excessively on referencestoprimary sources. Please improve this article by adding secondary or tertiary sources.
Find sources: "Hugging Face" – news · newspapers · books · scholar · JSTOR (February 2023) (Learn how and when to remove this message) |
Hugging Face, Inc. is a French-American company incorporated under the Delaware General Corporation Law[1] and based in New York City that develops computation tools for building applications using machine learning. It is most notable for its transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets and showcase their work.
Company type | Private |
---|---|
Industry | Artificial intelligence, machine learning, software development |
Founded | 2016; 8 years ago (2016) |
Headquarters | |
Area served | Worldwide |
Key people |
|
Products | Models, datasets, spaces |
Revenue | 15,000,000 United States dollar (2022) |
Number of employees | 170 (2023) |
Website | huggingface |
The company was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf in New York City, originally as a company that developed a chatbot app targeted at teenagers.[2] The company was named after the "hugging face" emoji.[2] After open sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised US$40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5][6]
In December 2022, the company acquired Gradio, an open source library built for developing machine learning applications in Python.[7]
On May 5, 2022, the company announced its Series C funding round led by Coatue and Sequoia.[8] The company received a $2 billion valuation.
On August 3, 2022, the company announced the Private Hub, an enterprise version of its public Hugging Face Hub that supports SaaSoron-premises deployment.[9]
In February 2023, the company announced partnership with Amazon Web Services (AWS) which would allow Hugging Face's products available to AWS customers to use them as the building blocks for their custom applications. The company also said the next generation of BLOOM will be run on Trainium, a proprietary machine learning chip created by AWS.[10][11][12]
In August 2023, the company announced that it raised $235 million in a Series D funding, at a $4.5 billion valuation. The funding was led by Salesforce, and notable participation came from Google, Amazon, Nvidia, AMD, Intel, IBM, and Qualcomm.[13]
In June 2024, the company announced, along with Meta and Scaleway, their launch of a new AI accelerator program for European startups. This initiative aims to help startups integrate open foundation models into their products, accelerating the EU AI ecosystem. The program, based at STATION F in Paris, will run from September 2024 to February 2025. Selected startups will receive mentoring, access to AI models and tools, and Scaleway’s computing power.[14]
The Transformers library is a Python package that contains open-source implementations of transformer models for text, image, and audio tasks. It is compatible with the PyTorch, TensorFlow and JAX deep learning libraries and includes implementations of notable models like BERT and GPT-2.[15] The library was originally called "pytorch-pretrained-bert"[16] which was then renamed to "pytorch-transformers" and finally "transformers."
Ajavascript version (transformers.js[17]) have also been developed, allowing to run models directly in the browser.
The Hugging Face Hub is a platform (centralized web service) for hosting:[18]
There are numerous pre-trained models that support common tasks in different modalities, such as:
In addition to Transformers and the Hugging Face Hub, the Hugging Face ecosystem contains libraries for other tasks, such as dataset processing ("Datasets"), model evaluation ("Evaluate"), and machine learning demos ("Gradio").[19]