After the whole debacle around the GPT4chan[1] and the whole gating mechanism for models, it’s hard for me to think how some entity can trust that they are not going to shutdown or do gating due to some ToS shenanigans. In other words: if you’re this man in the middle between models and clients, it’s not better to treat yourself as a “dumb pipe”?
N.B.: I think the company has a great culture seeing from the outside and I assume that I can be misinformed about their business model.
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.
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.
Once you want to scale to production, you're right, it doesn't make sense to use the HF repository, it makes more sense to clone it into S3 or something else that you have more ownership over.
I am also surpriswd this has its own wiki page, although it looks like it has been rather quickly put together woth not the most fluid writing.
See also the neutering of all the big commercial models. No one is running / giving access to a high quality virtually (or completely) uncensored model.
The community it’s great and I am user for a while. My doubt is that if there’s a lot of use cases where companies and/or MLEs/DS will do some “git pull model_v0.1” from any of the HF model store.
From what I can tell, based on the later sections:
> new experiences for Google Cloud customers to easily train and deploy Hugging Face models within Google Kubernetes Engine (GKE) and Vertex AI
I assume that means a new API field like `huggingface_model: "google/flan-t5-base"`?
> Models will be easily deployed for production on Google Cloud with Inference Endpoints
That seems to mean the GCP button which is currently disabled in the Inference "Create a new endpoint" page (https://ui.endpoints.huggingface.co) will now be enabled, which is the clearest part of the announcement.
[1] https://cloud.google.com/vertex-ai/docs/start/explore-models
One such example is the human-in-the-loop feature of document ai.
So partnering with Google on Open Source can make a lot of sense.
But it's in vogue to hate everything Google does on HN at the moment, so oh well.
Not too defend Google, but they have arguably the deepest AI knowledge in the industry and released many of the fundamental building blocks for today’s AI boom (transformers, Tensorflow, etc.)
Good for shareholders, that's all. Not really sure I believe their "open science" argument.
Though tensorflow was made by google and it is pretty good library.
