It's not naive and it's hard to answer, because the answer is equally naive:
It's sort of like asking what the business case is to have repos on GitHub instead of having a private git server / GitLab.
The value is because "that's where everything is happening": ex. I just did a 4 day hackathon wrapping llama.cpp on _all_ platforms for my as-yet unreleased app. If you need a local AI / llama.cpp model, you go to HuggingFace, full stop.
Then, I want to host these models on my own - I don't want to rely on the HF repos of 3rd parties being stable. Few clicks later, started my own, and uploaded the models. Then, I translate a Python function to Dart, and I can download these models, ranging from 2 GB to 28 GB, using the app, for free, without an API key.
That's much easier than S3, both in cost and integration time.
But still, the answer sounds naive and marginal I'm sure.
But in this case, Github had 3 major tailwinds: (1) the popularization of VCS, (2) the network effect due to the number of users and (3) it's more or less the mandatory part of modern software engineering.
For the use case that you mentioned, I agree that it is important and I understand. Still, for the folks that aren't relying upon LLMs or are doing some vanilla/traditional ML in some laggard industry, I have a hard time believing that those folks are going to HF.
(3) uploading weights, distributing weights, running demo on GPUs is mandatory part of ML engineering
=== interlude ===
I hope it doesn't sound like I'm being argumentative; discussion is especially interesting to me because it often weighs on me how hard it is to explain HuggingFace. So I enjoy trying and improving at it.
=== longer analogy ===
Imagine if all mobile developers in 2008* needed to host demos on iPhones captive in a server farm somewhere.* Some company offered that for free. On top of it apps were 30 GB, but the company hosted downloads for free. So everyone is putting their stuff on there. Then that feedback loop continues while the field takes a historic spike in interest and it's 4 years later.
* AI developers in 2020.
** GPUs captive in a server farm somewhere.
== Musings ==
This sort of highlights a thread of discussion for startups, the unreasonable effectiveness of specialization. Data scientists in 2020 use Python because they can, they're not really familiar with GitHub as in VCS so their mental model of it is more a dropbox. All of a sudden there's an $X billion (so far) opportunity to clone GitHub, but make it marginally easier to use via hiding stuff that's necessary for all other software, and then light money on fire hosting GPUs and S3.
Comments
It's not naive and it's hard to answer, because the answer is equally naive:
It's sort of like asking what the business case is to have repos on GitHub instead of having a private git server / GitLab.
The value is because "that's where everything is happening": ex. I just did a 4 day hackathon wrapping llama.cpp on _all_ platforms for my as-yet unreleased app. If you need a local AI / llama.cpp model, you go to HuggingFace, full stop.
Then, I want to host these models on my own - I don't want to rely on the HF repos of 3rd parties being stable. Few clicks later, started my own, and uploaded the models. Then, I translate a Python function to Dart, and I can download these models, ranging from 2 GB to 28 GB, using the app, for free, without an API key.
That's much easier than S3, both in cost and integration time.
But still, the answer sounds naive and marginal I'm sure.
But in this case, Github had 3 major tailwinds: (1) the popularization of VCS, (2) the network effect due to the number of users and (3) it's more or less the mandatory part of modern software engineering.
For the use case that you mentioned, I agree that it is important and I understand. Still, for the folks that aren't relying upon LLMs or are doing some vanilla/traditional ML in some laggard industry, I have a hard time believing that those folks are going to HF.
(1) popularization of pytorch
(2) network effect due to the number of users
(3) uploading weights, distributing weights, running demo on GPUs is mandatory part of ML engineering
=== interlude ===
I hope it doesn't sound like I'm being argumentative; discussion is especially interesting to me because it often weighs on me how hard it is to explain HuggingFace. So I enjoy trying and improving at it.
=== longer analogy ===
Imagine if all mobile developers in 2008* needed to host demos on iPhones captive in a server farm somewhere.* Some company offered that for free. On top of it apps were 30 GB, but the company hosted downloads for free. So everyone is putting their stuff on there. Then that feedback loop continues while the field takes a historic spike in interest and it's 4 years later.
* AI developers in 2020.
** GPUs captive in a server farm somewhere.
== Musings ==
This sort of highlights a thread of discussion for startups, the unreasonable effectiveness of specialization. Data scientists in 2020 use Python because they can, they're not really familiar with GitHub as in VCS so their mental model of it is more a dropbox. All of a sudden there's an $X billion (so far) opportunity to clone GitHub, but make it marginally easier to use via hiding stuff that's necessary for all other software, and then light money on fire hosting GPUs and S3.