Welcome back to an action packed time of of training.
So now, after waiting about five minutes when I run this same, uh, list events function, we get
to see lots of things going on.
What you see in the last ten events is that training is happening.
And you can see in this message the step zero, step one, step two, step three out of 500, representing
the 500 different training data points that it's going to go through.
And you'll also see here training loss.
Training loss is something that we want to come down and that will show us, uh, a good outcome, which
is of course, what we're going for.
And every time if I rerun this, we'll see now that it's already on step 118 as we go now, it's kind
of hard to see what's going on here.
And so this would be this would be a bit challenging if this is what we had to put up with.
But we don't because we use weights and biases and here, here is our gorgeous weights and biases screen
which allows us to follow along with the training.
And what we can see is the the results of training loss and watch what's happening.
It's just updated because the page was refreshing.
Um, and we'll get to see and get some insight into what's going on.
Now, one thing that you'll always see when you're running this kind of training is that the first few
batch steps will typically show a dramatic drop.
Um, and that's actually not necessarily something to get too excited about.
Typically, what's happening there is that the model is very quickly, uh, learning, um, the very
much the parts of the structure which almost go without saying, like the fact that there's a dollar
sign, um, and uh, and where to put the decimal point and a few other things that, that, um, I
think in this case, it realizes that just guessing zero zero for the sense is more likely than, than
anything else to be the right number of cents, because there's many things that are just a whole number
of dollars.
So there's this is that's total speculation, but it's things like that, the kind of obvious stuff
about the construct that allows training to to come down very quickly for the first few steps to get
the obvious stuff out of the way, so that it's now following a decent pattern.
What you're really looking for is continual progress from that point, and you typically, if you've
got a training set up well and there's no right or wrong answers here.
So so there's exceptions to everything I'm saying now.
But but generally speaking you do want to see some variation.
It's good to see some some batch steps that have greater and lower loss because you are you're trying
to optimize and you're looking to explore different, different possibilities.
Um, and what you're trying to what you're looking for is a kind of trend where over time, whilst any
individual batch step may go up or down, you do expect or hope to see a gradual decrease in loss,
um, over time and as potentially a slight concern for us that might make us a bit worried, is that
we're seeing something where this doesn't particularly seem to be coming down.
It just seems to show a lot of volatility, um, representing the fact that some of the of the data
points are doing better than others in terms of the the guessing.
But it's not as if there seems to be much of an improving trend or a trend in any direction yet.
Um, at least that's what appears to be the case.
So hopefully you are also running this, and you're going to be watching carefully to see what happens.
Uh, it's going to take a total, I think, of about 10 or 15 minutes to run all the way through, but
let's see where it is now in this list.
We're now about halfway through.
When it gets to the end, there's going to be a pause while it goes through a step of validating the
results and and and some, some confirmation checks on the OpenAI side that it that it tells you about
in the, in the statuses.
Um, and then you actually get an email to tell you that it's completed at the same time as this run
completes.
And when we pick up in the next video, it will have run.
I will see you there.