Well.
Hello there everybody.
I am so grateful that you've made it through to the start of week seven.
It is tremendous and I plan to not disappoint.
I will make it worth your while.
It's going.
There's some really great content ahead.
You may be feeling a bit of an anti-climax from what happened last week with our disappointing results.
Fine tuning frontier model after so much hype.
But what I can tell you is it's like a good movie where there's a period of time where things feel tense
and disappointing.
There's going to be redemption, there's going to be redemption.
We will see if you're like me and you kept hammering away after after those results, you will have,
I expect, got to a point where you have got some improvements.
You get to a point where you can fine tune and actually get better results than we originally got with
the Untuned version, but not significantly so, but a little bit better than I did in the last day.
But anyways, for now we are moving swiftly onwards.
We are launching into week seven and what a week do I have in store for you?
Uh, it is a massive week.
It is a really massive week.
It is an advanced week where we're raising the bar.
We're getting to a point where you're now acquiring advanced skills in this kind of deep learning field
of of llms.
And as we move more deeply into training, what you can already do, of course, you can already generate
text and code with frontier models, with APIs, and with hugging face open source code.
You can build rag pipelines and then you can now build data sets.
Quite a long time we spent on data sets and baseline models and frontier models that you can fine tune,
albeit with not amazing results.
For our use case today we get advanced.
We talk about using a technique called Lora for fine tuning open source models.
We describe quantization.
We talk about Q Laura, and we're getting into three key hyperparameters R alpha and target modules,
which if you've not encountered them before, sounds like something straight out of Star Trek, but
in fact will make complete sense by the end of today.
And before we launch into it, I do need to take a moment to remind you of your eight week plan, and
this may feel like this is boring for you at this point, but I think it's important to take pride in
your accomplishments and to remember where everything that you've learned, you started.
When we we first had our session to talk about, uh, uh, the, the opportunity with Llms and Frontier
models and we compared them, um, we then looked at using the UIs, we use Gradio, we used agent ization
Multi-modality.
Then we got to hugging face the pipelines and then Tokenizers and models.
Then in week four, we generated code by selecting LMS for the task.
In week five, that was rag week.
We used the wonderful Chroma and Lang chain to build some rag pipelines there, and hopefully you did
the project, which I'm really excited to see what people do there.
Uh, and then last week we fine tuned a frontier model.
Uh, now you know how to do it.
You understand about creating the data sets.
You know how it works, and you understand when it's most applicable, when you're dealing with that
kind of nuance of tone, and perhaps when it's not so applicable, in our case, when you're trying
to actually build in functionality because a frontier model is already so massive.
Um, so now we arrive at week seven, we are going to be fine tuning an open source model, one that
is significantly smaller than a frontier model, and see where we get to with that.
And week eight is where it all comes together.
And so with that introduction, the main topic of the day is Laura, and we will launch right into that.