Well, how fabulous is that?
I hope that you are as wowed as I am by our new airline, I assistant and everything it can do.
I've taken another screenshot here of of a conversation I'd had, and you can see again that gorgeous
image of London.
A very different approach now.
Not the montage, but something rather rather simpler.
Uh, it I find it astounding that you get such variety, such diversity of images.
Um, and I also find it astounding that it's so easy to put together these sophisticated frameworks
involving lots of functionality.
Remember, we also had our tool running there, looking up the prices.
Uh, everything we had together was a very sophisticated, complex app, complete with user interface.
And we did it all just in a few hours worth of work.
So it's a congratulations.
But as always, there's a challenge for you and if I may say, one more time, the best way to learn
is by doing.
It is incredibly important that you now go and do some exercises and work on this to improve it.
It's also a lot of fun as an extra bonus.
So here are some of the things you can do.
We talked before about adding in another tool to make a booking.
In theory, not obviously a real booking, but make a booking and then it should print to an output
that a booking has been made.
Or maybe if you if you want, you could have it write to a file or something like that to give you a
sense that the booking has happened.
Add that as a tool.
Hopefully you've done it already, but if not, now's a good time to do it.
Then add another agent.
Uh, have an agent that is able to translate all of the responses to a different language.
Uh, something that we'd suggested for a previous project.
But do that and then show it on the right hand side and use a different frontier model.
How about Claude, for example, use Claude as a way to translate to another language of your choosing,
and then you'd have to do some radio work to add another panel.
With that translation.
There will be a little bit of futzing around with Gradio when you do that.
So you may you may find that that requires a little bit of googling, but hopefully you'll get an idea
or you don't need to Google.
You could actually ask, uh, Claude yourself for some advice on how to extend that Gradio app to add
in that extra section to reflect the translation that it makes into another language.
You'll find that when you do something like that, you provide a bunch of code and ask it to extend
it, to do more, to add more capabilities, that these models are excellent at that kind of, uh,
that kind of iterating on code.
And then finally, since we've been enjoying multi-modality, one more multimodal task for you is audio
to text, uh, add an agent that can listen to audio from your audio input source and turn it into text
as the input to the AI assistant.
And then you've really completed the loop.
You'll be able to talk to it, and it will be able to talk back and draw images.
When you were looking to ask for ticket prices.
And that will then complete the week two challenge.
And at that point you will be very familiar with Multi-modality and with using these, uh, stitching
together these different agents to carry out a bigger task.
And at that point, May I tell you?
You are now 25% of the way to mastering LM engineering.
25% of the way.
A quarter of the way through.
You can describe Transformers comfortably, including all of the terminology.
You can code against the APIs, and you can build multimodal assistance using UIs, using tools, using
agents.
This is practically second nature to you at this point.
Uh.
Next week, change in topic to something that is absolutely wonderful the thriving open source community
and and thriving capabilities that you have access to through open source, you're going to get to know
hugging face really well, really, really well.
You're going to work with pipelines and also with Tokenizers and with the models themselves with transformer
models.
And ultimately you're going to be running inference of open source models using Google Colab on their
boxes with GPUs.
And so by the end of the week, you'll be highly proficient with inference of open source models.
And I can't wait to get to it.
And I will see you then.