Actually slight change in plan.
I'm going to wrap up the day.
Day three at this point, and say that we'll take day four to look at the results in weights and biases,
and to examine the progress of training, because I think that we should let it run for a bit and then
take some, some serious time to do that.
Um, I'll also at that point be able to show you the model in the Huggingface hub.
And I also I do I feel like I might have been a bit glib about the amount of money that it costs to
train these models.
I want to be clear that you really don't need to spend any material amount of money for for to have
a lot of fun with this and investigate hyperparameter optimization as part of this course, it would
only cost cents.
Um, and so I want to quickly explain how you can do things like reduce your training data set to a
more manageable size, and have more sensible parameters so that you could train on a, um, a normal
spec GPU box and be spending only a few cents on this project.
That's that's all that's required if you wish to be a complete nerd like me and go all out and run lots
of runs with top end boxes and spend 5 or $10 then then, uh, on your head, be it as it will be on
mine.
But it's not necessary at all.
But you do need absolutely to take a moment to congratulate yourself on where you've got to.
You have a training run happening.
I do hope right now while while I speak it is running.
And you are in a position now that you can explain.
Q Laura pretty well for fine tuning open source models.
You're so fed up with me talking about target modules, I'm sure, uh, and now, uh, explaining things
like learning rates, uh, ah and alpha, uh, and uh, dropout and all of the various other things
like optimizers and the like, it's all second nature to you, and it's all complex stuff.
This is, uh, upskilling in a big way.
So congratulations.
It's tremendous progress.
And next time we'll go over to weights and biases and see what's happening.