And in fact, there is much more than that.
There is lack of transparency, but what you get when you get a model out of hugging face or something like this.
And there is a lot of, you know, people don't really understand the terms that are with the licenses associated with those models.
And so there is general, you know, there's people make mistakes or fun, but so.
And so the model openness framework briefly, basically introduced ranking system to give transparency as to what's in model distribution.
And we are looking at two different dimensions, it's very important to understand this because people often focus on the openness, but there is also completeness.
So this is all based on the concept of open science.
So let me get a little bit more into the details.
When it comes to the completeness aspect, we are talking about what you actually get from this distribution.
We have identified 17 different components.
I won't go through them now, but there are three main types of components in a model distribution.
There is code, but there is also data and documentation.
So all of these participants in what's going to be in there and the completeness.
On the openness side, it has to do with what under what terms those components, those components are made available to you.
And so the model openness framework is very clear.
You must have permissive licenses, no restriction whatsoever, right?
And in fact, there is also another aspect which is often missed.
We have, as I showed earlier, three different types of components.
Oftentimes distributions only have one license that only really is applicable to one type of components.
So we also recommend that people use different licenses to cover the different components, the different types of components.
So as you see, there is an example, you have a patchy for instance for your code, cdla for your data and cc by for the document.
We put all of those components into three different classes.
There are corresponding to different use cases.
That's also very important because it's like openness from what point of view to achieve what.
The first class open model, basically addresses the most common use case that people have, which is to be able to use the the model and do whatever they want with it.
That includes fine tuning during performance announcements and so on.
The class two is the open tooling model, gives you a bit more insight as to how the model was trained, so you can do further analysis.
And the class one, which is the last one, is really the open science model, which addresses the use case of reproducibility.
You must be given all the information you need with the right licenses so that you can actually reproduce the same exact model.
We put all of this into a spec, which is available.
So I know it's quite a few cure codes, but so the first one leads you to the morph, which is a specification, which is only about 15 pages.
And then there's the mod, which is a website, there is a registry.
You can use to assess the openness of your model and you can actually record it, make it available on the website for others to find.
It will give you, you basically enter the different components that's in your distribution and what the licenses are for each of your components and it will tell you in which class you fall.
We also have a survey going on in generative AI commands that I invite you to look at.
It will help us, you know, give us information as to what we are going to work on.
This is only one of the products of generative AI commands.
And again, I want to, you know, emphasize that.
The next foundation has one good aspect, which is anybody's welcome to come participate.
There is no, no need to pay anything. You don't have to be a member.
So there is a website dedicated to generative AI commands initially, which is part of the LFAI and data organization.
And if any of this interest you, please join us.
Thank you.
Thank you very much, I know.
That's you.
So now, Martin, it was supposed to be with Paul, but Paul couldn't make it.
So Martin just presented the presentation about Kubernetes AI building themselves.
Thank you very much.
Hello everybody.
Remember now, I'm like a lecturer up here.
If I see you walk and don't, I got a name and shame you.
No, that's only a joke.
All right.
So we, AI folks, businesses are out there and are trying to leverage the advantage with AI.
I wanted to key aspects of that is being able to build better.