Welcome, everybody to the last day of week three.
Week three.
Day five.
We're here already wrapping up open source model inference with hugging face.
And today, today is the day that you're going pro.
Today is the day when we're putting together everything you've learned in the last four days of lectures
and really solidifying it with an excellent, uh, juicy project, a business project which is going
to give you some, some true experience in the field, what you can do already, if you don't mind me
telling you one more time, you can code with frontier models.
You can build AI assistants with tools, multi-modality, generating images, making sounds.
Uh, and you can use pipelines, tokenizers and models within the hugging face Transformers library.
Today, you're going to be even more confident with Tokenizers and models.
You're going to be able to run inference across open source models with ease, and you're going to have
implemented an LLM solution combining frontier and open source models together into one nice package.
There's also going to be a good business challenge for you to keep working on this, so let's get started.
The business problem that we have is a feature that is in many applications that we all know, and so
it's a good, real kind of product.
We want to build a solution that can create minutes of meetings including things like actions and owners
and so on.
Uh, it will be able to take an audio recording and then use a frontier model, use an API to convert
the audio to text.
It's actually a task that I had given you as a follow on exercise from one of the projects last week,
so you may have already experimented with this, but if not, we're going to do it together.
We're going to call a frontier model to convert audio to text.
We are then going to use an open source model to turn that text into meeting minutes, summarizing it,
plucking out actions and owners and the like.
And we will stream back results and show them in markdown.
So these these are the activities we're going to do.
That's how we're going to put it together.
And it's going to build a product that will be useful.
This is what we want to come up with.
We want to be able to have a solution that produces minutes like this with discussion points, takeaways,
action items, and as the input data to start with the resource that we'll be using.
There are audio files of publicly available council meetings from councils across the US available on
hugging face.
And that is where we'll begin.
I've already downloaded one of the audio files and taken a chunk out of it.
In the interest of time, we'll do just a piece of the Denver City Council meeting rather than the whole
meeting.
But the idea is that that's going to help us show that it works.
And then perhaps this is something that you'll be able to use for your own meetings, for real, when
we have a working product.
So without further ado, let's go to Google Colab and let's build our application.