Hugging Face

“What we're seeing in, in machine learning is that it's actually making its way into production after, you know, a year, a few months, a few weeks sometimes a few days now. So this is, in my opinion, this is amazing. And that's what's driving Most of the speeds…”

“On Hugging Face we just crossed 250,000 models, right? A quarter of a million models uploaded by almost 15,000 companies now.”

“What we've always done and I think we'll always do with hugging face is to make sure that, you know, Whenever, always kind of like make sure to spend at least like 30 or 40% of the company's efforts on explorating new things and kind of like finding the long-t…”

“The truth is that there's always going to be both, right? I think if you see most, most technologies, you know, if you look at search, you always have the elastic search, the Algolia, or like, if you look at databases, you have that MongoDB and the proverter a…”

“When you look at why are companies using so many models on Hugging Face, you usually realize that you know, a more specialized model is more efficient. It's cheaper to run. It's usually faster to run. And most of the time actually more accurate for the specifi…”

“We're leading the development of an open source library that is helping companies integrate that into their models and into their workflows.”

“We're starting to see on Hugging Face more and more opt-in data sets, meaning like data sets that have been trained only on data that the creators of the data have consented to having a model trained on.”

“We just crossed 50,000 of machine learning demos in the past year, year and a half on that.”

“And we did that for almost, almost three years got some level of traction billions of messages exchanged between, between users and the chatbots.”

“The three main tasks right now are NLP, so text, right? From like information extraction, text generation, text classification. the second one is text to image and computer vision, right? So object detection, text to image, text image generation. the third o…”

“When we're talking about Bloomberg, for example, using us or Meta using us both being kind of like customers.”

“And then something that we did that I think worked really, really well for us is that we never hired any Kind of like community manager, any kind of like communication PR kind of team members because we wanted it to be part of every single team members work.”

“Everyone, everyone in the team has access to the Twitter account and are tweeting from the Twitter account.”

“Right now we have like 15,000 companies using, using the platform and we have 3000 companies paying us, right?”

“When you get so much usage, so much network effects, and you actually like for a lot of these projects, you are like the starting point. Of projects like the same way. I think probably companies are starting on GitHub to see the open source projects before sta…”

“We've partnered with Clem and his team. They test as they release any of those language models they're testing on AMD with our Instinct GPUs equally as they're testing on NVIDIA.”

“If you have a model up on hugging face and somebody downloads a final gigabyte file and the thousands of students and researchers just kind of pounding away on that, I think there's a good chance they're going to find issues a lot better than a very small safe…”