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GUS MARTINS: Thank you very much for spending your Thursday
afternoon with us.
I really hope that, by the end, you feel
like, oh, this was amazing.
It was really worth my time.
Again, I'm Gus.
I'm from the AI DevRel team.
My main goal is to make Google's AI tooling as easy as possible
for you, for the community.
And today, you've seen a lot of-- there's talk
about the vision behind Gemma, how
cool is it, the [INAUDIBLE] tool kit, why it's an open model.
But it's the Gemma Developer Day,
so I imagine you are all thinking,
when's the Developer Day?
And it's now, exactly now.
So we are going to see a lot of demos.
My colleagues there will show you a lot of live demos,
so let's all pray that everything goes well.
And we'll show you lots of the cool capabilities we can do,
but before we get there, I would like to teach you
how to start this journey.
I imagine some of you have started already.
Thank you very much for that.
And if you didn't, welcome.
This is exactly where you should start.
I'll give you all the tips for that.
But before we start that, when I start working on this project
for some time ago, one of the goals that we had-- we
had three goals.
We had many goals, but the three important ones for you are,
we wanted to make models that were very, very, very
good as we did that.
You saw the numbers.
They're very good.
The other thing, we wanted to make those models safe
and follow--
and following all the responsible AI perspective.
We did that, too.
But the one thing that I was a little bit
annoying to my friends, every day, all the time,
is I want these models to be as easy as possible
to use and to be everywhere.
I want them to be in every platform for everyone
to use, for everyone to benefit from it.
And that's what we did, so we launched them
in all our platforms, our tooling, our frameworks,
AI related of course, and our cloud.
We collaborate with our friends Hugging Face, from NVIDIA,
to make them even more available even faster,
and this was very good.
We were very happy with the beginning of that,
and we gave to the community everything that we thought,
and that was very good.
And two days after that, we saw that the community
got the models and added another 20 other frameworks
and platforms.
Many of those that I wasn't--
I didn't know they were so popular,
but the community just ran with it.
They just got it, start putting other platforms,
sending PR requests to other GitHub repositories.
More than 300,000 downloads, more than 300 spaces on Hugging
Face, so it became very popular.
And I think maybe a part of it is because we tried
to make this easy for you.
The idea is it has to be easy for the developer.
I know I received questions from people, hey, Gus,
I really want to start this machine learning journey,
and then it's like, yes, I understand your pain,
and I imagine you all had the same question.
And where should I start?
That's the follow-up question, which is very hard,
and we wanted to make this journey easy.
That's why we did this.
And to start with Gemma, you will go
to the ai.google.dev/gemma, where is the first page where
you can see all the benchmarks, all the information.
The main documentation is there, and that's where
you should start your journey.
But Gemma has a specific license.
As [INAUDIBLE] mentioned before, it's not an open-source license.
It's an open license.
It's commercially [? friend ?] license,
and you have to go accept this.
And the main place to do that is on Gemma's page
on Kaggle Models.
You can go there, and you can accept the license.
You can use your Kaggle user.
You can use your Hugging Face user, your Gmail,
another email account, any of those,
and then you get access to that.
So when you see that screen, you are
able to start playing with the model, and as a developer,
as I mentioned, that's what you want to try.
And before we go into trying the model
and doing any code whatsoever, there's
another cool thing you can do is, too, on Kaggle Models,
which is we have an inference widget where
you can play with the model right there on the web page.
Of course, this is not running on the web page.
We will hear about this a little bit later today.
This is a widget that was created by our colleagues
on Google Cloud, and you can play with the model,
try some prompts, see the results there.
This is a good start for you to, oh,
let me see what this model can do,
before going to any code related environment.
But then you say, OK, Gus, this is the developer day.
You told us a couple of minutes ago.
Yeah, yeah, yeah, got it.
Got it.
So let's start with the basic one.
We have the Keras implementation.
If you read here the box to the left,
there's basically two lines of code there.
There's some import, some styles thing that you set up,
but it's two lines of code.
You load the model, and you call the generate method,
and that's it.
With that, you can start playing with the model
with code right there.
Two lines of code, so it's very straightforward.
Of course, if you are not a Keras developer,
you are, I don't know, PyTorch developer or Jax developer,
we also have the same way of doing that, of course,
for specific PyTorch, specific for Jax.
But the idea is you get this quite fast.
And when you are ready to continue your journey,
you got the license, so now, what's next?
Great question.
You go to our documentation.
We have a lot of tutorials.
We added two new tutorials be-- if you
saw the documentation yesterday, now, it's even better.
We have more tutorials that were added this morning,
and you can continue your journey.
So you can play with inference.
You can do fine tuning.
You can do fine tuning multiple hardware.
You can play with other configurations.
You can understand how the details about the model,
how to do other advanced solutions, all
of that in our documentation.
Now, I give you the first steps, and you
can start playing with that.
You can start building your own applications.
You can start having fun.
But one question that I got from the developers, from you people,
from the community is, Gus, can you point me to a project
that I can start playing with?
How do I start?
I played with the tutorials.
I understand how it works.
Now what?
Don't worry.
We got you covered.
We have two Kaggle competitions running right now
based on the Gemma models.
We have the AI Assistant competition,
and we have the LLM Prompt Recovery.
Both of them are competitions based on the Gemma models,
and the idea is you try to solve a problem using the Gemma
models, and you compete against other teams.
You can create a team.
You can compete by yourself.
But why does this so cool?
The idea is, when--
as you've seen, when we start to learn machine learning,
there's a lot to learn, so it's very hard to do it by yourself.
When you start to use Kaggle, you
have a community of people, a community of people trying to do
very similar things as you.
They are all competing there.
They're trying their best to solve a problem.
They will share in the forum.
They will share code.
They will share their ideas, and you can learn from them.
So maybe you're not an expert now,
but you can learn from the experts.
They are not going to give you the secret sauce at first,
but they will give you a good start.
So there's a lot of great code there
that you can go and start learning with it,
so this community is amazing.
I highly suggest you start playing with it.
And there's also a prize, but the idea is learning.
I'm pretty sure that learning is more important than the prize.
And of course, if you are a researcher,
we also got you covered.
We have a program to give you credits, cloud credits, so you
can do your researches using Google Cloud and Gemma
because we don't want-- the model is
[INAUDIBLE] we say developers.
Maybe I should have defined this earlier.
When I mean developers, I mean developers,
researchers, builders.
I want everyone that wants to do something, create something,
and researchers are a very important part for us.
We would like to help you build new models, new capabilities.
The last week, I saw people building stuff, and I was like,
is that possible?
And I've been playing with this model for a while.
But people are-- I still get surprised by some things
that people do, so it's amazing.
We have this on our main page.
You can see the link to get more information.
If you are a researcher, we can give you more information
also during the break.
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