Well.
Fantastic.
It's coming up to the end of the week, and that means it's coming up to a challenge for you again,
even though I've just given you a challenge to build a Gradio user interface for for what we just saw.
But that's an easy challenge.
You can do that.
No problem.
This you need a harder challenge.
At the end of the week, it's time for a harder challenge.
So the end of week challenge is to build an important business application that we will, in fact,
use later in the course.
Although yeah, you you won't need to to you won't need to have built it for that because because I'll
have done it.
But it's really helpful if you've done it.
And this is something that you'll be able to use no matter in any business.
This, this, this tool will apply to every business vertical and can be useful to you, I guarantee
it.
And this is what it is.
Create your own tool that generates synthetic testing data a test data generator, open source model.
This is something that is so valuable.
Generating data sets is something that you need for many different purposes.
And I want to give you a very, um, wide remit to decide how you want to go about doing this, but
I'm looking for something where you can describe a kind of data set you want, and maybe it's descriptions
of products, maybe it's descriptions of, uh, um, uh, job postings, whatever it is you want to
be able to, to tell your product what it is, what kind of data that you're working with and let it
dream up, uh, diverse outputs, diverse test set that you'll be able to use when experimenting with
your business area in the future.
So this synthetic data generator is going to be a valuable tool for yourself, for me and for for this,
both for this course and for future business problems you tackle.
So it's worth investing some time in, and it's worth giving it a gradio UI while you're doing it.
And that's going to be the super easy part.
So I have a shot at that.
It will apply to your business area no matter what you do.
It's going to be useful and you're going to really enjoy it.
And then that would then complete week three, wrapping up your third week of your journey towards being
a proficient LM engineer.
You can already, of course, code confidently with frontier models.
You must be sick of me saying that now because you're that good.
You can build an AI assistant.
You can have it be multimodal, you can have it use tools.
You can have it be consist of multiple smaller agents that carry out specialist tasks.
And of course, at this point you can create an LM solution that combines calls to frontier models.
And it can call open source models.
And you can use the pipeline API, using it to to carry out a large variety of common inference tasks.
And you can also use the lower level hugging face APIs, the Tokenizers, and the models for inference
tasks.
So congratulations once again, you should be very proud next week.
Next week we change topics.
There's a thorny question.
It's a question I get asked all the time.
It's something which is, uh, where there's actually a lot of great resources to help.
It's about how do you pick the right model for the for a given task that you have to work on.
There are so many models, there are so many options.
There's for staff.
There's there's do you go closed source or open source?
But then whichever path you take, there are so many possibilities.
And how do you navigate through this to decide which one is right for a particular problem.
And that is the key.
It depends on the problem.
Different models will be appropriate for different problems.
I'm going to show you how to figure that out.
We're going to compare LMS.
We're going to use leaderboards.
We're going to use arenas.
And we're going to do some some work with arenas ourselves.
And that's going to be fun.
And then as our practical work, we're going to to go a different direction than we've gone in the past,
except we did it very briefly once, but we're going to be looking at code generation when we're using
frontier models and open source models to be generating code and tackling some code generation problems.
So that will be a new, interesting perspective for you.
So I'm really excited about next week, and I'm so, so impressed by how much progress you've made already
and how many skills that you've already acquired.
And I will see you for week four for picking the right LLM.