So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using the
power of Olama.
You've probably had similar experience to me, which is.
I certainly found that Quinn 2.5 is perhaps the most powerful model when it comes to grasp of different
languages.
Some of the other models are better, I think, at explaining.
So I'd be very interested to hear your observations.
Please do post them with the course or message me direct.
I'd love to hear what you've discovered and this kind of experimenting with different models and and
finding the one that works best for your your problem.
That is a critical skill for an LM engineer.
So this was valuable time spent.
All right.
Let's talk about the next eight weeks.
So I am looking to take you from where you are today, over on the left to being a master of LM engineering
in eight weeks time.
And this is how we'll do it.
We'll start this week by looking at models at the frontier of what's possible today, which people call
frontier models.
Things like GPT 4001 preview and Claude 3.5, and a number of other pioneering models that are closed
source and are able to achieve amazing things.
And we'll do that through the web user interface like ChatGPT, and also then through the APIs.
And we're going to build a commercial project, something immediately that will be useful, and there'll
be an interesting commercial exercise for you as well.
Then next week we will slap a user interface on top of it using a platform which I love, which is called
Gradio.
And we will have good fun with it and you'll see that I love it.
I go on about it a bit, but it's so easy to use and it's so easy for people like me who are terrible
at front end to build a nice, sharp user interface very quickly indeed.
We'll do it to solve a classic JNI use case, which is the building an AI assistant, a chatbot, and
we'll but we'll do so in a way that has audio and pictures.
So it's multimodal and it will be able to use tools, which means that it's able to call out to code
running on your computer, which sounds kind of spooky, but it's going to make sense when we do it.
So that is all.
Week two.
In week three, we turn to open source, and we use the ubiquitous Hugging Face platform, which is
used by data scientists and LM engineers across the board and will use it to build both.
We'll use the simple API in hugging face called the pipelines API, and then we'll use the more advanced
API, and we'll explore things like Tokenizers and models in Hugging Face.
In week four, we're going to talk about something which is a particularly thorny issue in the world
of AI, which is there are so many models to choose from.
How do you go about selecting what is the right model for the task you have at hand?
So we'll we'll work on things like benchmarks and leaderboards and figure out how do you go about that
decision path.
And then we're going to take on a particularly different kind of commercial problem about generating
code.
We're going to build an application which is able to rewrite Python code as C plus plus high performance
C plus plus code.
And we're going to then try it out with a bunch of closed source and open source models.
And one of them will be the winner.
The one that's the winner is going to take our test Python code.
It's going to rewrite it and the new code is going to run 60,000 times faster, which is shocking.
And you will see that yourself.
And then there'll be some exercises for you to build other kinds of code generation tools.
In week five, we will turn to one of the the topics that is super hot at the moment, which is rag
retrieval, augmented generation, using, uh, data stores of information to add expertise to your
LLM will be building our own Rag pipeline for answering questions that pertain to an organization.
And then there'll be a difficult commercial challenge for you and exercise in which you apply this to
your own information.
And I'm really excited to see what people make of this, and to see some of your projects of rebuilding
a Rag pipeline for yourself.
In week six, we begin our three week flagship project for this course.
Uh, week six, we will set up the business problem.
We'll do a lot of work on data, and we're then going to create some traditional machine learning models,
which is very important to do to build a baseline.
And then we'll try models at the frontier, and we'll fine tune models at the frontier as well, to
do as well as we possibly can with this business problem.
In week seven, we'll apply it to open source.
We're going to take open source models and they're initially going to perform terribly, and we're going
to make it our mission to improve those open source models by fine tuning until at least we can compete
with GPT four.
The model at the frontier.
And I'm not going to tell you what happens, but I will tell you that I believe that the results will
astonish you.
I will tell you that.
So it is very much worth hanging on and seeing what happens in week seven.
But then it all comes together in the finale in week eight, which is a fitting conclusion to the eight
weeks we are going to build a fully autonomous Agentic AI solution, which will have seven agents collaborating
to solve a real commercial problem.
And the end.
Not only will it be doing something where it scans the internet for various things, but will end up
sending you push notifications with some of its discoveries.
So it's going to be really fabulous.
It's going to have a terrific result at the end of it.
It will be a good way to to be a culmination of everything that you've learned each week, building
on top of it, of the of the prior week, and resulting in true commercial projects that you'll be able
to put into action in your day job right away.