julien_c

> Ollama eventually added ollama run hf.co/{repo}:{quant} to pull directly from Hugging Face, which partially addresses the availability problem.

uh actually, _we_ did (generates a Docker-style manifest on the fly)

and a lot of traction on paid (private in particular) storage these days; sneak peek at new landing page: https://huggingface.co/storage

[i work at Hugging Face]

BTW the reporting in this article is sloppy/incorrect

Flamingo-style, see for instance the recently released IDEFICS: https://huggingface.co/blog/idefics

we have a secret plan to change our logo to a Face Hugger some time in the future :)

that would make for a quite different branding.

Might think about it:)

quite cool! haven't tried it yet, but what's the latency on hot-loading a model? (for instance, loading `stabilityai/stable-diffusion-2-1` for the first API call)

what's the inference time on M1?

ArXiv link to the paper: https://arxiv.org/abs/2110.08207

GitHub repo: https://github.com/bigscience-workshop/promptsource

FWIW this is extractive question answering so the output of the model can only be a span of the input paragraph.

SQuAD is the prototypical example of a dataset for this task, see https://rajpurkar.github.io/SQuAD-explorer/

For France, you can check the French Tech Visa program: https://lafrenchtech.com/en/how-france-helps-startups/french...

I've done it for one of my team members, it's pretty easy.

Not really on unsupervised/self-supervised data though, right?

(nor on the same scale of corpora, as far as I can tell)