Hello Tom, good to see you.
Hi there, good to see you again.
Everything good, yeah. Just back from Web Summit, super happy to be here as well.
Thank you for being here. Hey Tom, we have a lot of people today that are really looking forward to this conversation. I prepared a number of questions for everybody who's online. Looking at the streaming today, you will have two buttons in the platform: one is about Q&A, to chat with everybody else online. Sorry, yes, there's a chat and there's a Q&A. So if you want to ask us a question, use the Q&A please, and then I'm going to look at the questions as we go through. Tom, I think everybody knows about Hugging Face. We just saw that you pulled off the biggest heist in history with the emoji, which is great. It's a nice way to start. Let me just say a few things about Hugging Face anyway, just as an introduction. It was founded in 2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf, who's here today with us. Over the last several years, it has contributed significantly to democratizing AI by making all range of models accessible to everybody. I think Hugging Face hosts around 1 million models at this moment. You might have the latest and greatest numbers, but anyway, this is probably a good estimate, and sees millions of downloads annually. I think one of the Llama models alone was downloaded more than 20 million times in a single month, so it's a lot of traffic. Tom specifically has the role of Chief Science Officer at Hugging Face, which is very broad if I understand well, leading scientific research, open source projects, and engaging with the community, advocating for open source, and enhancing the user experience of NLP tools. I think, correct me if I'm wrong, but that should be more or less Tom.
Right, yeah, that's right. I mean, it's a specificity of Hugging Face. I would say also that a lot of people here cover a very wide domain, from open source to science to community. You even see them talking about what they do openly, so it's one of the nice perks of being a Hugging Face team member.
Great. Hey Tom, so I'm going to fire off a number of questions and then we go to the audience. But let me start first with something which is about Hugging Face, the evolution of Hugging Face. So at some point you have become the hub for open source globally, right? And that happened at some point. What is the single event that made you believe that was what Hugging Face was becoming?
Yeah, it's very hard to pinpoint. I would say like every community-based company, it grew really over time. So definitely, I think very early on, my co-founder Julian had really this feeling that there would be many models in the future. And at the same time, I had this idea that transfer learning, which was the kind of pre-generative AI idea that one model could do many things, but at that time we were always fine-tuning, we didn't yet have this foundation model. But transfer learning sounded very early on like a good idea, basically mutualize this kind of training compute, which is huge. And I think, yeah, just we saw the community growing progressively over the years, first in NLP, then with maybe Stable Diffusion was also a breakthrough in image in terms of visibility. More recently, we see speech interfaces catching up, right? We've started at OpenAI and then a couple of open source projects as well. And in the future, we are moving to robotics. I'm very big on robotics. I think next year is going to be maybe the ChatGPT year of robotics. So I think, yeah, it's quite progressive. I was definitely not expecting in the first year, where I had like the first four, eight models, that we would reach one day several million models. Like you said, we passed 1 million public models, but it's also true we have a lot of private models, so it's actually several times this number in total, which is crazy.
It is crazy and impressive, of course. And by the way, I'm coming back later on with this robotics thread, right? Because there is something that is super interesting that might shape the way that we look at AI in open source AI in the next years for sure, but also shorter perhaps. But since we are here talking about models and so on, can you say at this moment which are the models, libraries, or tools that trend most in Hugging Face, and what do people do with them? Where do you see the uses?
I think the very nice thing about Hugging Face is everyone can actually just go to the front page, right? And you have this trending section which lists the trending model of every week. It's usually based on the last seven days, and you can see how it changes a lot. So a couple of weeks ago, it would have been definitely the release of Flux, which was an image generation model released by a new lab, Black Forest Labs, which went crazy because of a level of photorealism that had never really been reached yet. And then we had the first video model, Pyramid Flow, the first open source video model. So we have already many closed source, but this was the first open source that reached an impressive quality. And right now we are back to NLP. I see a Qwen2 Coder, which is kind of a code generation model from Qwen. So Qwen is a team in Alibaba, they release extremely strong models. We have also another very interesting model for agents, which is the new OmniParser agent from Microsoft, which is able to basically understand the screen and output it in a very synthetic representation that is extremely useful for agents, saying okay, there is text here, there is an image there, and that kind of summarizes this image. So it's always very diverse. I think it's really great. We see all modalities basically progressing together, and we see also this mix of models but also datasets being released. And recently, I think it really started with ChatGPT, so about two years ago, this idea that you should also release a demo where people can try the models, then models should be very accessible. I think something that really OpenAI pioneered and that we expect now also from open source models. So there's a lot of Spaces, which is the demo that you can run on Hugging Face, that are regularly trending.
Yeah, right. And so, you know, the theme of this conference here is agents in production. So we are going to talk about agents clearly, but also agents to production, so agents that are not only the theory but actually make a difference in applications and so on. So when it comes to agents, what's your view about how they're going to develop? So how much role will there be for agentic type of AI in Hugging Face? Are you planning to create beyond what you have done now frameworks for this, open source frameworks or so? How do you see this evolving in terms of Hugging Face?
Yeah, it's interesting also because this term agent covers so many use cases that are very different from one and the other, right? Some of these agents, I mean, like a code completion agent, some of them are very close to maybe software developer tools. Some of them are much more related to business use cases, automatization of business processes, and some of them are very close to robotics, robots that evolve, that move in your kitchen, it's also an agent. So I think it's really hard to think about one single framework or library that would cover all of this. What is sure is there is some common points. For instance, the use of tools. Many of these agents need to interact with APIs, with software. For robots, they need to interact with real-world tools in the future. And so that's a common point. So I think tool use is something we've started to explore a bit, hosting tools on Hugging Face. I think it's very useful. But in the end, what I believe also is to build applications today, it's very interesting to focus on your verticals, maybe focus on the set of tools that you need for your agent. So I think a very general agent might come at some point, and we see the leading companies like Anthropic, OpenAI playing with this idea of a very general agent. But we also see that it's not yet fully functional, you know, or one does not always, sometimes thinks for a very long time but the answers are not fully there yet. So I think today, the real good way to tackle an agent project, I think, is really to understand, okay, which tool do I need, in which framework should I be, and be kind of specific, you know, more generic until this day comes of a generic agent model.
Do you see a marketplace for tools in particular? Because that requires you some form of standardization. There are some commonality across these tools. Everybody sort of developing tools that feed agents and so on, but you know, everybody does it its own way. So do you see some sort of either standardization or marketplace for all this?
