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Democratizing AI: The Hugging Face Ethos of Accessible Machine Learning

How 3 founders turned their passion for sharing knowledge into a thriving ML community

This is our fourth Corporate Chronicle (#1 OpenAI, #2 Anthropic, #3 Inflection) focusing on generative AI unicorns. We initially planned to spotlight Cohere, but Hugging Face disrupted the narrative. Because of that, the publication is hitting your inbox on Saturday instead of Friday, our apologies.

In August 2023, Hugging Face secured $235 million in a Series D round, boasting a valuation of $4.5 billion. It surged ahead, not just of Cohere but surpassing Inflection, with its ~$4.0 billion valuation.

In emoji terms, all we can say is this 👏 

According to our own Twitter sentiment analysis, Hugging Face is a widely admired player in both ML and AI. Everybody seems to love them.

  • How did they achieve it?

  • What makes this light-looking company such a heavyweight?

  • Who are the people behind it, and what is their vision? What did they build?

  • Are they responsible for the AI they work on? Are they reliable?

  • How do they plan to make money? etc

We'll answer these questions and more in our exploration of Hugging Face. Tune in.

  1. The starting point of the company

  2. Mission of the company and its founders

  3. Groundbreaking Initiatives

  4. The tech behind their most famous products

  5. Current situation including finances

  6. How the company is going to make money?

  7. Founders’ attitude toward AI risks

  8. Regulations

  9. Bonus: All important links about the founders

The starting point of the company

“I’m French, as you can hear from my accent, and moved to the US 10 years ago, barely speaking English,” said Clement Delangue, co-founder and CEO at Hugging Face, in his testimony to the US House of Representatives in June 2023. He was working for a startup called ‘Mention’ then, in 2013, that was acquired by ‘Mynewsdesk’ in 2014. He was CMO with a passion for sharing knowledge and building communities with maximum openness (Check this spotlight story from Sequoia Capital if you want to learn about Delangue’s childhood.)

In 2015, he and his long-term friend Julien Chaumond decided to enroll in a computer science class created by Richard Socher. Recently, Richard tweeted:

“I still remember when Clement et al were taking my Stanford class CS224d in 2015 remotely from NYC. They reached out after about the startup he and his team were working on. So impressive what they've been building since.”

In 2015, though, they were building a chatbot designed to be a fun companion for teenagers. If you remember 2015, Conversational AI was not yet well-developed. Their attempt was gutsy. The three founders – Thomas Wolf, who knew Julien from École Polytechnique and was also part of the Stanford learning group – dug deep into natural language processing (NLP) to bring their whimsically-faced chatbot to life. In 2016, Hugging Face was launched. With scientific credibility backed by Richard Socher, they raised $1.2 million in 2017 from SV Angel, Betaworks, and NBA star Kevin. "We really have this vision where we believe everyone will have an AI friend and interact daily with Hugging Face," Delangue said. If only he knew that with almost the same objective, a company called Inflection would raise over $1.5 billion in 2023... But in 2016-2017, to power their bot, three founders had to explore every inch of the NLP universe.

“We realized that Conversational AI is the hardest task of ML. Our Chief of Science Thomas Wolf was training really cool models and taking pre-trained models and adapting them to do Conversational AI. It was hard! Nonetheless, the tools required to do that were not limited to just achieving Conversational AI but could be applied to all NLP tasks and even most ML tasks too.”

said Julien Chaumond

Making their own research public was just natural for them. “Sharing knowledge benefits everybody” was part of their attitude towards business.

2017 changed everything in NLP – a transformative year punctuated by the unveiling of 'transformers,' a novel architecture conceived by researchers from Google and the University of Toronto and explained in their infamous paper “Attention is All You Need.” Transformers provided the scaffolding for formidable language models like BERT, ROBERTa, and GPT-3, all of which had the innate ability to understand the intricacies of language at a blistering speed, courtesy of parallel GPU processing.

These models were monumental but also monumental-ly inaccessible to the average Joe Developer. Working on their chatbot, Hugging Face developed an open-source transformers library, putting the power of cutting-edge NLP into the hands of developers regardless of their Silicon Valley pedigree. The game-changer, however, came when Hugging Face simplified Google's BERT model, refactoring it into PyTorch, and—gasp—open-sourced it.

Mission of the company and its founders

From this moment their mission crystallized from being mere chatbot developers to evangelists of accessible machine learning. "It just seemed to be something a lot of people would like to use," Delangue remarked, his words betraying a nonchalant profundity. And so, the mission was redefined: Hugging Face would be a sanctuary for machine learning enthusiasts, whether engineers, researchers, or tinkerers at a weekend hackathon. Since then they aimed to become the GitHub of machine learning.

This pivot ushered in an era of symbiotic cross-pollination within the community. Now Hugging Face wasn't just a tool; it was a movement, a pulsating hub of ingenuity. And just like that, a company transformed itself by transforming the very way we access and leverage machine learning. It's a fascinating narrative of how a change in mission can make not just a company, but an entire community, punch above its weight.

And when they now say it’s the fastest-growing community and most used platform for machine learning – it’s actually true.

In an interview in March, 2021 Hugging Face CTO Julien Chaumond said that the democratization of AI will be one of the biggest achievements for society. He added that no single company, not even a Big Tech business, can do it alone.

Their official mission from the website states:

We are on a mission to democratize good machine learning, one commit at a time.

Groundbreaking Initiatives

April 28, 2021 – the company launched the BigScience Research Workshop in collaboration with several other research groups to work on an open large language model.

In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.

On May 13, 2022, the company introduced its Student Ambassador Program to help fulfill its mission to teach machine learning to 5 million people by 2023.

They also have an impressive list of partnerships, including StabilityAI, AWS, and others that are tremendously beneficial for their community.

The tech behind their most famous products

About BLOOM: When we imagine the origins of large language models (LLMs), we often picture Silicon Valley giants or flush startups. But BLOOM shatters this narrative. Spawned by BigScience, a consortium of 1,200 researchers from 38 countries, this open-source model boasts 176 billion parameters and speaks 46 languages plus 13 coding tongues.

Under the hood, BLOOM is more than just a heavyweight. It employs causal-decoder transformer models but adds two innovations: ALiBi Positional Embeddings and Embedding LayerNorm, elevating both training and performance. The model trained on the ROOTS corpus, a smorgasbord of 498 datasets, adding linguistic diversity to its portfolio.

Behind the scenes, the Megatron-DeepSpeed framework orchestrates a symphony of efficient large-scale training, with a Zero Redundancy Optimizer as the conductor.

Megatron-LM provides the core Transformer implementation. It also enables tensor parallelism, allowing the model to split individual layers across multiple GPUs. This horizontal slicing of layers enables a larger-than-memory model to train efficiently.

Types of Parallelism:

  • Data Parallelism (DP): The model is replicated across different GPUs, each getting a slice of the data for parallel processing.

  • Tensor Parallelism (TP): Individual layers of the model are partitioned across multiple devices, enabling intra-layer parallelism.

  • Pipeline Parallelism (PP): Layers of the model are divided across different GPUs so that each device computes only a fraction of the layers, also known as vertical parallelism.


By integrating these types of parallelism—often termed 3D parallelism—the framework allows BLOOM to scale to hundreds of GPUs with incredibly high utilization.

Result? A peak performance of 156 TFLOPs on A100 GPUs. In essence, BLOOM isn't just another LLM; it’s a monument to the power of collective genius.

BLOOM is available in the following versions: bloom-560m, bloom-1b1, bloom-1b7, bloom-3b, bloom-7b1, bloom (176B parameters).

Current Situation including Fundraising, Acquisitions, Current state by the numbers, as well as Business model and Revenue streams →

Hugging Face – after playing with a chatbot idea – started as an open-source platform, prioritizing community-building over profits for its first five years. In 2021, the company started targeting enterprise clients with enhanced security and processing features.

  • Based in Brooklyn, New York and Paris, France. It’s also widely distributed and has over 200 people across the globe.

  • https://huggingface.co has 22.9M monthly visits; ~16% of which are from China, with the US in the second place with ~13% (according to SimilarWeb).

  • Hugging Face’s Twitter: https://twitter.com/huggingface

  • If you want to start on the platform, here is a step-by-step guide.

Fundraising Timeline

  • Angel Round, March 2017 – $1.2M from Betaworks

    • Notable investor: NBA star Kevin Durant

    • Objective: To add entertainment value to AI

  • Seed Round, May 2018 – $4M from Ronny Conway

    • Focused on expanding the open-source library

  • Series A, December 2019 – $15M from Lux Capital

    • Further development of the repository and initial steps toward enterprise solutions

  • Series B, March 2021 – $40M from Addition

    • The focus shifted to enterprise-grade solutions

  • Series C, May 2022 – $100M from Lux Capital

    • Accumulated $140M in cash reserves including previous rounds

    • Plans to delve into MLOps and address bias in ML models

  • Series D, August 2023 – $235M from Salesforce Ventures

    • Company valuation soared to $4.5 billion

    • Objective: To continue library expansion and accelerate commercialization


  • September 13, 2017 – Sam:) Random, anonymous, one-on-one chat (currently, non-operational).

  • On December 21, 2021 – Gradio, a software library used to make interactive browser demos of machine learning models.

Current State

With a robust $235M Series D funding and a cumulative valuation of $4.5 billion, Hugging Face is flush with cash, boasting reserves that enable aggressive growth and diversification strategies. Their fortified capital makes them a powerhouse in the machine learning ecosystem, primed to tackle challenges like inherent biases and expand beyond their NLP niche.

By the numbers: The company says it's now hosting 500,000 models, 250,000 data sets, and 250,000 apps, and aims to triple its numbers in 2024.

How the company is going to make money

Business Model

  • Hugging Face has Open Core Strategy: Core features like the Transformers library and Hugging Face Hub are free and open-source.

  • Freemium & Paid Plans: Free access for basic features, with paid plans offering advanced features like the Inference API and AutoTrain. Pricing.

Revenue Streams

  • Subscription & Consumption-based Plans: Charging for extra features and advanced services.

  • Revenue-Sharing Partnerships: With companies like AWS, directing traffic in exchange for a share of revenues.

  • Enterprise Contracts: Custom pricing for large enterprises that require additional services and features.

  • Cloud Partnerships: Revenue-sharing deals with cloud providers like Azure and AWS to host models.

It feels like one of Clemente's main powers is to make friendly deals with everyone.

According to The Information: “Hugging Face, for its part, is on pace to generate more than $30 million in revenue annually, one of the people said.”

Axios informs: “Forbes reported that it brought in $10 million in revenue in 2021, and now has an annualized revenue of between $30 million and $50 million. Delangue declined to comment on specific numbers but said that revenue has grown fivefold this year, and it now has 10,000 paying customers (it sells enterprise features).”

What exactly can you find on their platform?

If you want to start on the platform, here is a step-by-step guide.

Products & Services

So, in a nutshell, Hugging Face has wedged itself quite comfortably at the intersection of open-source idealism and pragmatic enterprise solutions, making it both an industry darling and a revenue magnet.

Founders’ attitude toward AI risks

Clement Delangue has a practical outlook on AI risks. His attention is less on the theoretical dangers of Artificial General Intelligence (AGI) and more on the immediate concerns.

“AI is the new paradigm to build all technology. It’s not more; it’s not less. It’s not a new human form. It’s not Skynet or a super-sentient being. But it is something massive. It’s bigger than the internet, and it’s bigger than traditional software. It’s going to create new capabilities for technology. In the same way, most technology companies write software, most technology companies will write AI,”

said Delangue

Thomas Wolf is particularly concerned about the societal risks of AI monopolies. He points out the dangers of letting "an opaque monopoly or oligopoly appear on a technology as groundbreaking as AI.” Wolf envisages a future where such monopolistic AI could unduly influence personal and political decisions. His remedy is “to build a set of responsible, open, diverse and educated ecosystems around all new usages of AI.” He recommends reading the book «Overview of Catastrophic AI Risks» by Dan Hendricks.

Hugging Face has acknowledged the pervasive issue of biases in AI models, particularly in Natural Language Processing (NLP). They have taken steps to address this, such as implementing the Model Card feature, initially developed by Dr. Margaret Mitchell, a former Google ethicist, who Clement Delangue hired to focus on research related to bias and fairness.

Margaret Mitchell and her colleague Sasha Luccioni are very vocal about biases and fairness in ML and regularly share their thoughts and research on these topics.

They and other Hugging Face employees create a constant flow of educational materials and – what is probably even more important – constantly question the companies and their decisions regarding openness in ML.

Inclusivity and public education about AI are important to Delangue. He believes that “If users don’t understand how this technology is built, it creates a lot of risks, a lot of misconceptions.” His solution is fostering a community where anyone invested in the future of AI can engage, learn, and contribute.

Both Delangue and Wolf see the bigger picture of AI; they understand its immense potential and the ethical pitfalls that come with it. They advocate for regulation, transparency, and public education as antidotes to the most immediate risks and ethical concerns surrounding AI. Furthermore, they aim to make Hugging Face a platform that not only advances the technology but also engages in critical conversations around its ethical ramifications. Their combined message seems clear: With great computing power comes great responsibility. And that responsibility includes avoiding monopolistic control and addressing biases.

So “Everyone seems to love them. How did they achieve it?” Personal note from the editor: I've been scouring hundreds of articles, blogs, and tweets from major AI companies, their leaders, and teams, and as a specialist in linguistic analysis, I can only admire the consistency of the narrative with the actual actions. Their tone steadily strikes a balance—friendly, engaged, and concerned, yet devoid of doomism. Having observed Hugging Face from the outside for the past three years (and I am not associated with them anyhow and haven’t met any of the founders or employees), I'm struck by the company's cohesive culture and the unity among its employees across all social media platforms. They maintain sincerity and coherence in both their words and actions, a noteworthy trait given the current climate of uncertainty surrounding AI. I can only wish that more companies would adopt Hugging Face's attitude.


Delangue is keen on regulation, especially for more transparency.

“I think it’s pretty urgent [to have AI regulation],” he claims, “Today, most people in the world interact with AI scenarios.” His stance on regulation comes to light in his testimony to the U.S. House of Representatives where he advocates for open science and open source. He argues these would “prevent black box systems, make companies more accountable and help [in] solving today’s challenges like mitigating biases, reducing misinformation.”

Instead of the Conclusion

In 2013, the year when Clement Delangue came to the US, Julien Chaumond wrote a blog post “The Quest for the Ideal CEO-type Co-Founder.”

Over 15 months, Julien met with over 30 CEO-type individuals from various backgrounds, looking for a complementary co-founder, primarily in Paris and London.

The Three Key Traits he was looking for:

  • The Good Hustler: Effective hustle, not just talking big, but showing it through actions such as successful PR campaigns, sales skills, and a track record of launching startups, raising funds, or closing significant deals.

  • Appreciation for Tech: Understanding the crucial role of technology in a tech startup, recognizing the co-founder's significant contribution, and not undermining its importance, especially if they have had successful exits before.

  • Idea Alignment: Finding a co-founder with either a shared passion for a startup idea or one who can get passionate about your idea. This shouldn't be the sole or the most critical trait but should align with the goals of the startup.

The blog post received 12 likes and one comment:

“Did you find your unicorn?”

He certainly did.

Bonus: All important links about the founders

Clem Delangue / Co-founder & CEO

  • His Twitter / LinkedIn Profile

  • According to Fast Company, Delangue teams up with Matthew Hartman at his new venture firm Factorial Capital to boost investment in the broader open-source AI ecosystem.

Julien Chaumond / CTO

Thomas Wolf / Chief Science Officer

It’s amazing that Thomas has PhD in Statistical / Quantum physics, Law Degree, and after that he self-educated to become an ML engineer. Here his reading path (in case you decide to become like him):

To be continued…

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