That concludes a mammoth project.
Three weeks in the making.
In the course of those three weeks, starting with the data curation, the working with frontier models,
and then ending with the complete user interface in Gradio with even that very unnecessary but great
plot 3D chart on the bottom right, together with the trace from the agents and the results of the memory.
Um, and all of it came together.
Remember, the user interface was not really the point.
The user interface was a bit extra, so we can monitor it.
The main point is that that just runs and it just keeps running, and as it runs, it will be continually
notifying me every few minutes with a new deal that it hasn't surfaced before.
And that is the conclusion of a very satisfying, great project that we have built end to end.
And so with that, let's take a moment to do a retro of the last eight weeks.
One more time.
I've gone on about this so many times, and so you'll be happy, or maybe a bit sad that this is the
final retrospective you started eight weeks ago.
Over on the left, we wanted to get to being an LM engineer, someone who had mastered LM engineering,
highly proficient, advanced, and this is how we got there.
In the first week we played with lots of models.
We found out how many times the letter A appeared in sentences.
We took a quick look at one preview amongst other things.
In week two we first saw gradient and we played with some multi-modality, which was fun.
We also saw an early version of agent ization.
In week three we got stuck into hugging face.
We had pipelines, we had tokenizers, we had models.
In week four we got deeper into hugging face.
We selected Llms, we generated code and we had that remarkable project, with the 60 000 time performance
improvement in week five.
We built our Rag solution for insert film, which used chroma, and also briefly we used face as well
and created our expert.
And maybe you did the big project associated with that.
Week six we fine tuned a frontier model, although most of which six was spent curating data.
But that is such an important activity.
Week seven.
We did fine tune an open source model that then beat the frontier, and in week eight, we packaged
it together to a genetic AI solution complete with seven agents and a user interface.
And it was fabulous.
So that was the journey.
I need to take a moment to thank you.
Thank you so much for staying through to the end.
You can't, I can't I can't explain how much I appreciate it.
It's really, uh, so, so wonderful to have had people come all the way through the course, gone through
the eight week journey, and take advantage of everything that we've been doing and get to this point.
And I've, I've heard from several of you along the way, and it's been really, really rewarding for
me to experience this.
Uh, super grateful.
I hope you've enjoyed it.
I obviously I've enjoyed it a lot.
Uh, far too much.
I hope you've enjoyed it as well.
I really hope you can stay in touch.
By all means, please do LinkedIn with me if you're open to that.
If you're okay with that, I'm very much welcome.
LinkedIn connections.
And we can have a community, um, and message me if you've got to this point, I definitely want to
hear it.
Uh, and of course, you've got this big challenge.
Now, can you take what you've learned and use it to build your own project?
Maybe that idea I had about using the finance data would be an interesting one.
See if you can build something that could make some money.
If you do, then I expect a lunch out of it.
At the very least, perhaps.
Uh, but that would be a fun challenge.
Whatever you do with it, I want to hear about it.
If you built a great platform that is using some of this learning, then please share it.
I'd love other students to see that too, and it's great to have that kind of output as a tiny little
extra.
I don't know if you remember all the way, way, way, way back in week one, I did mention there was
going to be a little extra juicy nugget at the very end, and this is what it is.
I wanted to tell you that I did a personal project where I fine tuned an LLM.
It was in fact a llama two LLM from the beginning of this year, uh, on all of my text message history.
It turns out that I have 240,000 text messages that have built up over time on my iPhone, uh, since
I had the first iPhone some time ago.
And so I had a lot of text message history, and I was able to use that to train llama two to make a
simulation of me.
Uh, and there's a write up on my website, on my blog, Edward Dot com.
You can take a look and there's instructions for how you can do it too.
And of course, I was using llama two and now llama 3.1 is so much better along with some of the others
like like Kwan and so on.
So you could definitely have a stab at this, and you will probably have even better results than I
had.
And the results I had were spooky.
They really were very good indeed.
And so with that, I have to bring up the final slide.
Congratulations.
I hope that you're proud of what you've accomplished.
I hope you do feel that sense that you have now reached an advanced point in your learning.
You have got to the summit of the mountain.
I'm so, so very happy.
And I hope, I really, really hope that you're able to take this and use it in your day job, in your
career to move forwards.
And I very much want to hear all about it.
So do stay in touch.
Thank you once again.
And a huge congratulations.