Hot take: Git was the wrong abstraction for 90% of ML data. Checkpoints, optimizer states, training logs, agent traces - none of this needs version control. It needs fast, cheap, mutable storage. So we built Buckets. S3-like storage on the @huggingface Hub with Xet dedup and zero egress. Train in a bucket. Publish to a repo. One platform. 🤗🤗🤗

Looks like we're going to welcome two more Hugging Faces to the family next year. My wife is a hero! 💛💛💛@ReginaSil2

I don’t follow @elonmusk and yet I get served dozens of his tweets in my timeline every day. Independently of politics, it feels like X has become his propaganda machine to be honest. Isn’t it the very thing he was trying to fight in the first place?

Once again, an AI system is not "thinking", it's "processing", "running predictions",... just like Google or computers do. Giving the false impression that technology systems are human is just cheap snake oil and marketing to fool you into thinking it's more clever than it is.

If you think @Apple is not doing much in AI, you're getting blindsided by the chatbot hype and not paying enough attention! They just released FastVLM and MobileCLIP2 on @huggingface. The models are up to 85x faster and 3.4x smaller than previous work, enabling real-time vision language model (VLM) applications! It can even do live video captioning 100% locally in your browser 🤯🤯🤯

Great research on open-source by @Harvard: - $4.15B invested in open-source generates $8.8T of value for companies (aka $1 invested in open-source = $2,000 of value created) - Companies would need to spend 3.5 times more on software than they currently do if OSS did not exist I suspect that these numbers and impact are even greater for AI than for software (would be great to study!)

Just 10 days after o1's public debut, we’re thrilled to unveil the open-source version of the groundbreaking technique behind its success: scaling test-time compute 🧠💡 By giving models more "time to think," LLaMA 1B outperforms LLaMA 8B in math—beating a model 8x its size. The full recipe is open-source🤯 This is the power of open science and open-source AI! 🌍✨

I would pick @ylecun over @elonmusk every single day of the week. Despite getting much less $$, recognition & visibility than entrepreneurs, the scientists who publish their groundbreaking research openly are the cornerstone of technological progress & massively contribute to making the world a better place!

Our science team has started working on fully reproducing and open-sourcing R1 including training data, training scripts,... Full power of open source AI so that everyone all over the world can take advantage of AI progress! Will help debunk some myths I’m sure too. Thanks @deepseek_ai!

lol choose the odd one out

Should we acquire Stability and open-source SD3?

I think India could become an AI superpower and we're starting to see early signs!

This was the most important wedding of the weekend! No crutches or lightnings could stop us. Can’t wait to spend the rest of my life with my cutie pie!

When @sama told me at the AI summit in Paris that they were serious about releasing open-source models & asked what would be useful, I couldn’t believe it. But six months of collaboration later, here it is: Welcome to OSS-GPT on @huggingface! It comes in two sizes, for both maximum reasoning capabilities & on-device, cheaper, faster option, all apache 2.0. It’s integrated with our inference partners that power the official demo. This open-source release is critically important & timely, because as @WhiteHouse emphasized in the US Action plan, we need stronger American open-source AI foundations. And who could do that better than the very startup that has been pioneering and leading the field in so many ways. Feels like a plot twist. Feels like a comeback. Feels like the beginning of something big, let’s go open-source AI 🔥🔥🔥

This isn’t a goal of ours because we have plenty of money in the bank but quite excited to see that @huggingface is profitable these days, with 220 team members and most of our platform being free (like model hosting) and open-source for the community! Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Great job team!

Six predictions for AI in 2024: - A hyped AI company will go bankrupt or get acquired for a ridiculously low price - Open-source LLMs will reach the level of the best closed-source LLMs - Big breakthroughs in AI for video, time-series, biology and chemistry - We will talk much more about the cost (monetary and environmental) of AI - A popular media will be mostly AI-generated - 10 millions AI builders on Hugging Face leading to no increase of unemployment

Super excited to welcome our new investors @SalesforceVC, @Google, @amazon, @nvidia, @AMD, @intel, @QualcommVenture, @IBM & @sound_ventures_ who all participated in @huggingface’s $235M series D at a $4.5B valuation to celebrate the crossing of 1,000,000 models, datasets and apps on the platform. These partners alone already shared over 1,000 open models and datasets and have over 10,000 users on Hugging Face. It takes a village to democratize good machine learning thanks to open-source and we’re just getting started! 🤗🎉🚀

1T parameters, open-weights, just released on @huggingface!

Every tech company can and should train their own deepseek R1, Llama or GPT5, just like every tech company writes their own code (and AI is no more than software 2.0). This is why we're releasing the Ultra-Scale Playbook. 200 pages to master: - 5D parallelism (DP, TP, PP, EP, FSDP) - ZeRO - Flash Attention - Compute/communication overlap and bottlenecks All with accessible theory intros and 4,000+ scaling experiments.

This is my 5-minute testimony before the US Congress! Open science and open source AI distribute economic gains by enabling hundreds of thousands of small companies and startups to build with AI. It fosters innovation, and fair competition between all. Thanks to ethical openness, it creates a safer path for development of artificial intelligence by giving civil society, non-profits, academia, and policy makers the capabilities they need to counterbalance the power of big private companies. Open science and open source AI prevent blackbox systems, make companies more accountable, and help solving today’s challenges like mitigating biases, reducing misinformation, promoting copyright, & rewarding all stake-holders including artists & content creators in the value creation process. Let's go!

Am I wrong in sensing a paradigm shift in AI? Feels like we’re moving from a world obsessed with generalist LLM APIs to one where more and more companies are training, optimizing, and running their own models built on open source (especially smaller, specialized ones) Some validating signs just in the past few weeks: - @karpathy released nanochat to train models in just a few lines of code - @thinkymachines launched a fine-tuning product - rising popularity of @vllm_project, @sgl_project, @PrimeIntellect, Loras, trl,... - 1M new repos on HF in the past 90 days (including the first open-source LLMs from @OpenAI) And now, @nvidia just announced DGX Spark, powerful enough for everyone to fine-tune their own models at home. Would you agree, or am I just seeing the future I want to exist? Also, why is this happening (just the advent of RL/post-training?)

Where’s the open-source @elonmusk ?

I believe we need open-source alternatives to ChatGPT for more transparency, inclusivity, accountability and distribution of power. Excited to introduce HuggingChat, an open-source early prototype interface, powered by OpenAssistant, a model that was released a few weeks ago.

Hanging out with the Paris @huggingface robotics team! Let’s make robotics open-source and community-driven

Hugging Face has quietly become the biggest AI app store with 400,000 total apps, 2,000 new apps created every day, getting visited 2.5M times every week! Now you can search through any of them with AI or categories. The future of AI will be distributed, have fun everyone!

My prediction: in 2024, most companies will realize that smaller, cheaper, more specialized models make more sense for 99% of AI use-cases. The current market & usage is fooled by companies sponsoring the cost of training and running big models (especially with cloud incentives).

The @LeRobotHF team is studying the @UnitreeRobotics G1 today in case you have any questions or fun stuff you want us to try!

We just released the best 3B model, 100% open-source, open dataset, architecture details, exact data mixtures and full training recipe including pre-training, mid-training, post-training, and synthetic data generation for everyone to train their own. Let's go open-source AI!

No excuse anymore not to train your own models! This 200+ pages with full transparency. Let's go open-source AI!

When you realize that open-source is at the frontier of AI despite: - less GPUs - less money - less public and policy support - no $100M salaries to attract talent - with closed-source taking advantage and copying all the innovations of open-source without contributing back theirs 🤯🤯🤯 And we’re just getting started!

Everyone is talking about how we need more AI data centers (especially the ones who would mostly benefit from them) but why is no one talking about on-device AI? Running AI on your device: - Free - Faster & takes advantage of existing hardware - 100% privacy and control (you don’t send your data to an API)

When you run AI on your device, it is more efficient and less big brother and free! So it's very cool to see the new llama.cpp UI, a chatgpt-like app that fully runs on your laptop without needing wifi or sending any data external to any API. It supports: - 150,000+ GGUF models - Drop in PDFs, images, or text documents - Branch and edit conversations anytime - Parallel chats and image processing - Math and code rendering - Constrained generation with JSON schema supported Well done @ggerganov and team!

Unsurprisingly, Kimi K2 Thinking is already number one trending on HF. The AI frontier is open-source!

The main breakthrough of GPT-5 was to route your messages between a couple of different models to give you the best, cheapest & fastest answer possible. This is cool but imagine if you could do this not only for a couple of models but hundreds of them, big and small, fast and slow, in any language or specialized for any task - all at inference time. This is what we're introducing with HuggingChat Omni, powered by over 100 open-source models including gpt-oss, deepseek, qwen, kimi, smolLM, gemma, aya and many more already! And this is just the beginning as there are over 2 millions open models not only for text but image, audio, video, biology, chemistry, time-series and more on @huggingface!

Now you can interact with 100k+ open-source models - including Stable Diffusion, bioGPT, Flan, Bloom,... - and your own private models, in JS! Let's build AI better together!

Meta starts open-sourcing a lot and is now becoming one of the best companies in the world at shipping AI features. Coincidence? I don’t think so. Contrary to popular belief, a company (or a country) sharing their research, models and datasets publicly in open-source makes them MORE competitive, not LESS, even more so in AI. IMO, that’s how the US and some companies like OAI & Google established their leadership in the past few years (even though they are not so open anymore). Some of the reasons why open-sourcing makes companies more competitive: - Open science and open source attracts and motivates the best talents who want to to contribute to the field - It focuses organization on the speed of building - not on taking advantage of the current tech - especially important on a fast moving domain like AI - It motivates the whole field to improve what you’re building on (bug fixing, optimization, new capabilities) that you can then really easily integrate in your products). Is your company sharing their research, models and datasets? If not, they’re missing out! Source: wired.com/story/meta-ai-chat…

False! Because it's open-source, fast and cheap, it will help create thousands of silicon valley startups, hundreds of thousands of jobs, massively step up competition and create hundreds of billions of value for silicon valley. IMO more positively impactful than any and all closed-source AI startups (and it should be a wake-up call for these startups to share more!). vox.com/technology/397330/de…

Hugging Face is becoming the best place to share the most viral AI apps with spaces. Kolors Virtual Try-on just crossed 6,000,000 unique visitors & is now the #5 most popular space. Congrats to the Kwai Kolors team! huggingface.co/spaces/Kwai-K…

June 2024. Happened faster than I thought

👀👀👀 huggingface.co/deepseek-ai

Similarly to @Github copilot, you can now do question-answering in Google Sheet thanks to the TAPAS model from @GoogleAI & the @huggingface inference API. Machine Learning making its way in each and every product! Great job @osanseviero!

If AI stays closed-source, proprietary and monopolistic like it is now, it will destroy lots of jobs and just make the richest companies richer and more powerful! If we open it up thanks to open science and open-source, foster competition and decentralization of value and control, it will create many more jobs and economic value than it destroys. Let’s go!

Open-source superintelligence for all!

Can someone make github.com/openai/codex run locally with open-source models from HF?

We're open-sourcing "The Amazing Hand", an eight-degree of freedom humanoid robot hand compatible with @lerobot that can be 3-D printed at home for less than $250 ✌️✌️✌️ Given the success of Reachy Mini (2,000+ robots sold in a few days), we won't have the bandwidth to manufacture this one ourselves but we release the bill of materials, the CAD files and assembly guides for everyone to build or sell their own, let's go open-source AI robotics!

.@nvidia is not holding back!

It's been released just a few days ago and already more than 500 derivative models of @deepseek_ai have been created all over the world on @huggingface with 2.5 million downloads (5x the original weights). The power of decentralized open-source AI!

Let's make AI robotics open-source!

Deepseek R1 just crossed 10,000 likes on HF and is now the most liked model amongst almost 1.5M public models. Mind-blowing!

I don't get why so many people are dunking on Meta. Building frontier AI models is freaking hard & they have consistently released 10 times more openly than others which is game-changer for the field. What about all the other big tech & startups with much more AI ressources that are releasing much less openly? Should we name names?

Deepseek R1 just became the most liked model ever on @huggingface just a few weeks after release - with thousands of variants downloaded over 10 million times now!

Startup vs big company! nitter.app/zerogravityhxp/status/…

Am I the only one tired of LLM releases to gain 5% of accuracy? Time for audio, video, time-series, medical, biology, chemistry AI releases to get more of the spotlight please!

Apple is back! 20 new coreML models for on-device AI & 4 new datasets just dropped on HF: huggingface.co/apple

We just crossed 1,000,000 free public models on Hugging Face! That’s the ones the media covers like Llama, Gemma, Phi, Flux, Mistral, Phi, Starcoder, Qwen, Stable diffusion, Grok, Whisper, Olmo, Command, Zephyr, OpenELM, Jamba, Yi but also 999,984 others. Why? Because contrary to the “1 model to rule them all” fallacy, smaller specialized customized optimized models for your use-case, your domain, your language, your hardware and generally your constraints are better. As a matter of fact, something that few people realize is that there are almost as many models on Hugging Face that are private only to one organization - for companies to build AI privately, specifically for their use-cases. Today a new repository (model, dataset or space) is created every 10 seconds on HF. Ultimately, there’s going to be as many models as code repositories and we’ll be here for it! Cheers to the community!

Is @elonmusk replicating the OpenAI playbook? From open-source (grok 1) to closed-source (grok 2) From pro startups to regulatory capture If you can’t beat them, join them I guess haha

The @huggingface team waiting for @OpenAI to release their open-source model

One week later...

Machine Learning is becoming the default way to build technology, mostly thanks to open-source & open-science but I don’t think we’re anywhere close to singularity/AGI/terminator/AI God (if it even exists) (1/5)

Building a startup in ML these days

NLP is going to be the most transformational tech of the decade!

Kimi K2 Thinking feels like a big milestone for open-source AI. The first time in a while that open-source gets ahead of proprietary APIs on their big area of focus (agents). Fun to see that it's happening at a time when the proprietary APIs have the most money/attention they've ever had (narrative violation to "they will win because they have more money/compute"). The paradigm shift continues!

Did you notice how most video AIs are commercial, closed-source & secret? It sucks and a big reason for the lack of open-source & transparency is that there are very few high-quality open video datasets. The @huggingface team is changing this today by releasing FineVideo, a high-quality annotated video dataset with a creative commons license. Time to build!

GPU-Poor no more: super excited to officially release ZeroGPU in beta today. Congrats @victormustar & team for the release! In the past few months, the open-source AI community has been thriving. Not only Meta but also Apple, NVIDIA, Bytedance, Snowflake, Databricks, Microsoft, Google, and more have released open models and datasets on Hugging Face, which now hosts over 1M models on the Hub which have been downloaded over a billion times. More than that, many are starting to be better than proprietary APIs. This movement has been supported not only by big tech but also by a thriving open-source AI community that includes academic labs, startups, and independent hobbyists. For example, more than 35,000 variation models of Llama have been shared on Hugging Face since Meta’s first version a year ago—including more than 7,000 based on Llama-3—ranging from quantized and merged models to specialized models in biology and Mandarin, to name a few. More than 4 million AI builders are now using Hugging Face. However, the open-source community doesn’t have the same resources available to train and demo these models that big tech have at their disposal, which is why ChatGPT remains the most used AI application today. @huggingface is fighting this by launching ZeroGPU, a shared infrastructure for indie and academic AI builders to run AI demos on Spaces, giving them the freedom to pursue their work without the financial burden of compute costs. Spaces have been the most popular way to build AI demos, with over 300,000 AI demos created so far on CPU or paid GPU (and a thousand more every day). To foster the continued development of the AI ecosystem, Hugging Face is committing $10M of free GPUs with the launch today of ZeroGPU. Technically speaking, ZeroGPU leverages Hugging Face's experience in hosting and serving more than 100 Petabytes monthly from the Hugging Face Hub. ZeroGPU allows Spaces to run on multiple GPUs by making Spaces efficiently hold and release GPUs as needed (as opposed to a classical GPU Space that holds exactly one GPU at any time). This architecture is also more energy-efficient since GPUs are shared rather than duplicated. ZeroGPU uses @nvidia A100 GPU devices under the hood. You can learn more about ZeroGPU here: huggingface.co/zero-gpu-expl… More than 1,300 ZeroGPU spaces have been built since we started giving early access to AI builders on May 1, 2024: huggingface.co/spaces/enzost… You can explore some examples from @victormustar: nitter.app/victormustar/sta… You can find the article from @kyliebytes: theverge.com/2024/5/16/24156… 🤗🤗🤗

We're 250 team members 😅😅😅

For me, Google is the #1 AI company in the world and kickstarted the whole revolution with "Attention is all you need", BERT, T5 and many more. I'm ecstatic to be able to collaborate with @sundarpichai @ThomasOrTK @JeffDean @fchollet and the whole Google org to democratize AI thanks to open-source AI and open science! Let's go!

IMO, if GPT 4.5 was released as an open-source base model (that everyone can distill), it would be the most impactful release of the year. As an API, it's meh...

No cloud, no cost, no data sent to anyone, no problem. Welcome to local AI on Hugging Face!

For years, we've been saying that bigger isn't always better for AI and that smaller specialized models are usually faster, cheaper and more accurate for your specific constraints. So super happy to release the long-overdue capability of finding the best model based on size on @huggingface! It took us a bit more time than expected to release because we needed to wait for safetensors & GGUF to become ubiquitous to do it easily but it's now live. Time for you to stop listening to AI influencers and find the best model (small or large) for you and your use-case!

Give us a billion more and we’ll add an emoji in there 😅🤗

Trump just suspended the visa program that allowed me to move to the US to start @huggingface! Unfortunately, I won’t be able to vote in a few months but if you can, please vote him out, he's destroying what made America great in so many different ways! axios.com/immigration-ban-h1…

I'd like to live in a world where all countries & organizations, big or small, can train their own AI, instead of just a few of the richest & biggest ones. This is IMO one of the most important topics ever and will shape the future of the world. That's why I'm super proud when we release things like the ultra-scale playbook. We hope the community will like it and get inspired to share more of their learning too!

Should we acquire Humane to open-source the pin?

Requiring a license to train models would be like requiring a license to write code. IMO, it would further concentrate power in the hands of a few & drastically slow down progress, fairness & transparency.

I've said it and will say it again: #1 Smaller, cheaper, faster, more customized models will cover 99% of use-cases. You don't need a million dollar formula 1 to get to work everyday and you don't need a banking customer success chatbot to tell you the meaning of life! #2 All companies will ultimately want to build AI themselves (based on open-source AI) versus outsource to third-party APIs and there will be as many models as code repos today. AI is a foundational technology to build tech and the same way you don't outsource or have the same code-base as your competitors, you won't want to outsource your AI development or use the same models as your competitors.

Baidu just released 23 models at the same time on @huggingface - from 0.3B to 424B parameters. Let’s go!

Every day, over 1,500 terabytes of open models and datasets are downloaded and uploaded between @huggingface and @googlecloud by millions of AI builders. We suspect it generates over a billion dollars of cloud spend annually already. So we’re excited to announce today a new partnership to make it faster, safer & cheaper for Google Cloud customers to work with Hugging Face. With them, we will: - reduce Hugging Face model & dataset upload and download times through Vertex AI and Google Kubernetes Engine thanks to a new gateway for Hugging Face repositories that will cache directly on Google Cloud - offer native support for TPUs on all open models sourced through Hugging Face - provide a safer experience through Google Cloud’s built-in security capabilities. Ultimately, our intuition is that the majority of cloud spend will be AI related and based on open-source (rather than proprietary APIs) as all technology builders will become AI builders. And both Google Cloud and Hugging Face will be there for it, let's go!

Hugging Face was founded by immigrants

If you're a researcher or engineer releasing open science papers & open models and datasets, I bow to you 🙇🙇🙇 From what I'm hearing, doing so, especially in US big tech, often means fighting your manager and colleagues, going through countless legal meetings, threatening to quit or taking a lower paycheck, and sometimes the result is only that you'll get scolded when what you shared is used by competitors. But, please remember: research papers and open models and datasets is how progress happens! Your efforts are pushing AI toward a more open and collaborative future. Thanks to openness, your research or models get a chance to be noticed, seen & built upon by people you respect to accelerate progress, grow your network & accelerate your impact. It might be tough right now but open science will ultimately prevail as it always did! The researchers & engineers that we'll remember in ten years are the ones who share what they build, not the ones that keep it behind closed-doors for company profit maximization. Please keep fighting for openness. We see you and we thank you! 💚💛 💙💜

1,000,000 🤗

Reminder that you can't trust one company (or a few) to build safe AGI. The safest path is open, transparent and decentralized!

AI is not a zero-sum game. Open-source AI is the tide that lifts all boats!

The more I grow, the more I believe the #1 mission of a startup ceo is to fight the natural destructive tendencies of growing orgs like processes, meetings, verticalization and centralization, politics and in general everything that slows down your ability to keep increasing your impact.

Six predictions for AI in 2025 (and a review of how my 2024 predictions turned out): - There will be the first major public protest related to AI - A big company will see its market cap divided by two or more because of AI - At least 100,000 personal AI robots will be pre-ordered - China will start to lead the AI race (as a consequence of leading the open-source AI race). - There will be big breakthroughs in AI for biology and chemistry. - We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face. How my predictions for 2024 turned out: - A hyped AI company will go bankrupt or get acquired for a ridiculously low price ✅ (Inflexion, AdeptAI,...) - Open-source LLMs will reach the level of the best closed-source LLMs ✅ with QwQ and dozens of others - Big breakthroughs in AI for video, time-series, biology and chemistry ✅ for video 🔴for time-series, biology and chemistry - We will talk much more about the cost (monetary and environmental) of AI ✅Monetary 🔴Environmental (😢) - A popular media will be mostly AI-generated ✅  with NotebookLM by Google - 10 millions AI builders on Hugging Face leading to no increase of unemployment 🔜currently 7M of AI builders on Hugging Face

Love how @Apple is advocating for on-device AI at WWDC . Local, smaller, specialized models are the future of private, secure and efficient AI.

Could we stop comparing raw models (llama, stable diffusion,...) with APIs (gpt4, claude,...). Most APIs probably include a lot of engineering tricks and even several models under the hood chained or MOEd together so these are not the same things at all and can't be compared.

let's open superintelligence for all!

We crossed 1M models on Hugging Face!

With the Google announcement last week, I think we're now officially the only AI startup out there who has commercial collaborations with all the major cloud providers (AWS, GCP, Azure) and hardware providers (Nvidia, AMD, Intel, Qualcomm,...), making our vision of being the independent and agnostic platform for all AI builders truer than ever! Let's go!

Maybe we should buy Cline 😅😅😅

I've said it and will say it again. Concentration of power is the biggest risk in AI!

We were chilling until @levelsio triggered our monetization anxiety. New premium team plan now live 😅😅😅

We collaborated with the European Space Agency to open-source the largest ever earth observation dataset: Major TOM Core! About half of the entire planet is covered. That's 2,245,886 patches of 1068 x 1068 pixels. At 10m resolution, we've got 256 million square km with over 2.5 trillion pixels. More datasets from different satellites are in preparation and anyone can join this collaborative effort thanks to their organization on Hugging Face: huggingface.co/Major-TOM. Quoting @mikonvergence @esa: “democratizing Earth Observation model development and deployment with transparent, reproducible, and traceable tools - starts with the data!” You can explore the data here huggingface.co/spaces/Major-… & access the dataset here: huggingface.co/datasets/Majo…

100%! nature.com/articles/d41586-0…

We thought we would get xAI open-source but got zAI so even better 😅😅😅

To me, huge ML models are to machine learning what formula 1 is to the car industry!

The GPT4 of datasets took down Hugging Face, sorry all 😅😅😅

8 years later, we finally got the .com domain of @huggingface! If anything, maybe an example not to sweat the small details too early & trust your guts as so many people told us this was a shitty name!

Pumped to announce the brand new open LLM leaderboard. We burned 300 H100 to re-run new evaluations like MMLU-pro for all major open LLMs! Some learning: - Qwen 72B is the king and Chinese open models are dominating overall - Previous evaluations have become too easy for recent models, much like grading high school students on middle school problems - There are indications that AI builders have started to focus on the main evaluations too much at the expense of model performances on other ones - Bigger is not always smarter Really excited about how the field is maturing on evaluation, let's go!

Google is back baby! Taking the first spot for open models on the @huggingface LLM leaderboard for its sizes (2B & 7B): huggingface.co/collections/g…