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