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