Hugging Face

Last updated
Hugging Face, Inc.
Company type Private
Industry Artificial intelligence, machine learning, software development
Founded2016;8 years ago (2016)
Headquarters
Manhattan, New York City
Area served
Worldwide
Key people
  • Clément Delangue (CEO)
  • Julien Chaumond (CTO)
  • Thomas Wolf (CSO)
ProductsModels, datasets, spaces
RevenueIncrease2.svgUS$15 million (2022)
Number of employees
170 (2023)
Website huggingface.co

Hugging Face, Inc. is an 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.

Contents

History

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 U+1F917🤗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 SaaS or on-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]

On September 23, 2024, to further the International Decade of Indigenous Languages, Hugging Face teamed up with Meta and UNESCO to launch a new online language translator [15] built on Meta's No Language Left Behind open-source AI model, enabling free text translation across 200 languages, including many low-resource languages. [16]

Services and technologies

Transformers Library

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. [17] The library was originally called "pytorch-pretrained-bert" [18] which was then renamed to "pytorch-transformers" and finally "transformers."

A javascript version (transformers.js [19] ) have also been developed, allowing to run models directly in the browser.

Hugging Face Hub

The Hugging Face Hub is a platform (centralized web service) for hosting: [20]

There are numerous pre-trained models that support common tasks in different modalities, such as:

Other libraries

Gradio UI Example Gradio example.png
Gradio UI Example

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"). [21]

Safetensors

The safetensors format was developed around 2021 to solve problems with the pickle format in python. It was designed for saving and loading tensors. Compared to pickle format, it allows lazy loading, and avoids security problems. [22] After a security audit, it became the default format in 2023. [23]

The file format:

See also

Related Research Articles

Redis is a source-available, in-memory storage, used as a distributed, in-memory key–value database, cache and message broker, with optional durability. Because it holds all data in memory and because of its design, Redis offers low-latency reads and writes, making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database, and one of the most popular databases overall. Redis is used in companies like Twitter, Airbnb, Tinder, Yahoo, Adobe, Hulu, Amazon and OpenAI.

Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.

Annapurna Labs is an Israeli microelectronics company. Since January 2015 it has been a wholly owned subsidiary of Amazon.com. Amazon reportedly acquired the company for its Amazon Web Services division for US$350–370M.

<span class="mw-page-title-main">TensorFlow</span> Machine learning software library

TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks. It is one of the most popular deep learning frameworks, alongside others such as PyTorch and PaddlePaddle. It is free and open-source software released under the Apache License 2.0.

spaCy Software library for natural language processing

spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. The library is published under the MIT license and its main developers are Matthew Honnibal and Ines Montani, the founders of the software company Explosion.

PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. It is one of the most popular deep learning frameworks, alongside others such as TensorFlow and PaddlePaddle, offering free and open-source software released under the modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface.

<span class="mw-page-title-main">ML.NET</span> Machine learning library

ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions.

Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. It can be used to deploy ML models on embedded systems and edge-devices. The platform was launched in November 2017.

<span class="mw-page-title-main">Stable Diffusion</span> Image-generating machine learning model

Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom.

deepset German natural language processing startup

deepset is an enterprise software vendor that provides developers with the tools to build production-ready natural language processing (NLP) systems. It was founded in 2018 in Berlin by Milos Rusic, Malte Pietsch, and Timo Möller. deepset authored and maintains the open source software Haystack and its commercial SaaS offering deepset Cloud.

<span class="mw-page-title-main">GPT-J</span> Open source artificial intelligence text generating language model developed by EleutherAI

GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt. The optional "6B" in the name refers to the fact that it has 6 billion parameters.

A large language model (LLM) is a type of computational model designed for natural language processing tasks such as language generation. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.

<span class="mw-page-title-main">Llama (language model)</span> Large language model by Meta AI

Llama is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023. The latest version is Llama 3.3, released in December 2024.

LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.

Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development.

MindsDB is an artificial intelligence company headquartered in California, an innovator bringing AI and Data together and is focused on enabling developers to build AI capabilities that can Reason, Plan and Orchestrate over enterprise data.

T5 is a series of large language models developed by Google AI introduced in 2019. Like the original Transformer model, T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.

llama.cpp Software library for LLM inference

llama.cpp is an open source software library that performs inference on various large language models such as Llama. It is co-developed alongside the GGML project, a general-purpose tensor library.

The Latent Diffusion Model (LDM) is a diffusion model architecture developed by the CompVis group at LMU Munich.

References

  1. ↑ "Terms of Service – Hugging Face". huggingface.co. Retrieved 2024-05-24.
  2. 1 2 "Hugging Face wants to become your artificial BFF". TechCrunch. 9 March 2017. Archived from the original on 2022-09-25. Retrieved 2023-09-17.
  3. ↑ "Hugging Face raises $40 million for its natural language processing library". 11 March 2021. Archived from the original on 28 July 2023. Retrieved 5 August 2022.
  4. ↑ "Inside BigScience, the quest to build a powerful open language model". 10 January 2022. Archived from the original on 1 July 2022. Retrieved 5 August 2022.
  5. ↑ "BLOOM". bigscience.huggingface.co. Archived from the original on 2022-11-14. Retrieved 2022-08-20.
  6. ↑ "Inside a radical new project to democratize AI". MIT Technology Review. Archived from the original on 2022-12-04. Retrieved 2023-08-25.
  7. ↑ Nataraj, Poornima (2021-12-23). "Hugging Face Acquires Gradio, A Customizable UI Components Library For Python". Analytics India Magazine. Retrieved 2024-01-26.
  8. ↑ Cai, Kenrick. "The $2 Billion Emoji: Hugging Face Wants To Be Launchpad For A Machine Learning Revolution". Forbes. Archived from the original on 2022-11-03. Retrieved 2022-08-20.
  9. ↑ "Introducing the Private Hub: A New Way to Build With Machine Learning". huggingface.co. Archived from the original on 2022-11-14. Retrieved 2022-08-20.
  10. ↑ Bass, Dina (2023-02-21). "Amazon's Cloud Unit Partners With Startup Hugging Face as AI Deals Heat Up". Bloomberg News . Archived from the original on 2023-05-22. Retrieved 2023-02-22.
  11. ↑ Nellis, Stephen (2023-02-21). "Amazon Web Services pairs with Hugging Face to target AI developers". Reuters. Archived from the original on 2023-05-30. Retrieved 2023-02-22.
  12. ↑ "AWS and Hugging Face collaborate to make generative AI more accessible and cost efficient | AWS Machine Learning Blog". aws.amazon.com. 2023-02-21. Archived from the original on 2023-08-25. Retrieved 2023-08-25.
  13. ↑ Leswing, Kif (2023-08-24). "Google, Amazon, Nvidia and other tech giants invest in AI startup Hugging Face, sending its valuation to $4.5 billion". CNBC. Archived from the original on 2023-08-24. Retrieved 2023-08-24.
  14. ↑ "META Collaboration Launches AI Accelerator for European Startups". Yahoo Finance. 2024-06-25. Retrieved 2024-07-11.
  15. ↑ "Hugging Face Spaces Translator". 2024-09-23.
  16. ↑ "UNESCO Translator Event". 2024-09-23.
  17. ↑ "🤗 Transformers". huggingface.co. Archived from the original on 2023-09-27. Retrieved 2022-08-20.
  18. ↑ "First release". GitHub. Nov 17, 2018. Archived from the original on 30 April 2023. Retrieved 28 March 2023.
  19. ↑ "xenova/transformers.js". GitHub.
  20. ↑ "Hugging Face Hub documentation". huggingface.co. Archived from the original on 2023-09-20. Retrieved 2022-08-20.
  21. ↑ "Hugging Face - Documentation". huggingface.co. Archived from the original on 2023-09-30. Retrieved 2023-02-18.
  22. ↑ huggingface/safetensors, Hugging Face, 2024-09-21, retrieved 2024-09-22
  23. ↑ "🐶Safetensors audited as really safe and becoming the default". huggingface.co. Retrieved 2024-09-22.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.