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