Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking
at the end of that.
But that's because the point is that you should go back now and do this yourself and see it.
And as you run that code and you see what's going on in the modal screens, I think it's going to make
complete sense.
Um, in case you have any problems with that, uh, hugging face token, I'm going to put better instructions
in the Jupyter lab.
So that's very clear for you.
And yeah, if I think you'll find that it will, that it will be fairly straightforward and you'll see
how that works and why it's working so fast.
There's the first time you run it for a while.
There's several minutes for it to warm up.
But then subsequently, because we've cached the model weights and we've loaded it into memory, it
should run quickly as it did just then.
So with that, you have now learned how to take a model and how to deploy it to production so that people
could call it just with Python code for production purposes within applications outside something like
a Jupyter Lab.
And hopefully you're now beginning to appreciate that.
We do have a big week, and it is an epic project, and there's a lot to be done.
In fact, uh, the next day's worth of activities is the biggest of the lot.
There's an awful lot happening.
Um, but just to remind yourself for today that that this was about deploying models in production using
modal, the serverless platform.
In some ways, it's similar to when we deployed a model or I deployed a model to the hugging face using
hugging face endpoints.
Um, but you can see the the extra functionality that you get with this, the ability to configure infrastructure
with code and the way that the pricing works, it's a very, very powerful platform.
Next time you'll be able to build an advanced Wragg solution, you're saying I already you already got
Wragg.
We've done Wragg.
We know Wragg well.
You're going to know it even more.
Next time.
We're going to use Wragg.
We're going to do it directly without chain.
We're pros now.
We don't need long chain.
We can do it ourselves.
We're going to look things up in a Chrome data store and use it to give context to a model, but it's
going to be an enormous great data store.
And we're going to build something called an ensemble model, which is a kind of model that combines
the best of multiple models.
And we're going to be able to deliver production ready code that will span multiple models.
So it's going to be about, uh, really strengthening your skill set, building expertise as you make
the transition from being a knowledgeable in LM engineering to being a master of LM engineering.
And with that, I'll see you next time.