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WEBVTT
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And it's this time again, when we look at the podium of how our models are performing across the board.
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You already know the answer, but we should look at it anyway and and and have a giggle.
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Uh, so we first look at the constant model that just guesses the average value from the training data
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set.
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And if we look at that number, it was 146.
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It's looking distinctly lower in here right now.
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And I think you probably know why.
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Uh, then we looked at a traditional machine learning model that was able to beat the average, which
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we would hope it would, but not by masses.
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Uh, we looked at the random forest approach, which was the best of the traditional machine learning
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approaches coming in at 97.
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We did compare it with a human being and showed that human could beat the basic, simple features,
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but was beaten by random forest model.
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Uh, GPT four zero was the best of the various frontier models we looked at, and absolutely crushed
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it with 76 way outperforming this human.
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And then today we've looked at the, uh, fine tuned bass llama, 3.18 billion parameters quantized
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all the way down to four bits.
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Uh, and we saw that that was at 396, devastatingly bad.
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Uh, and it was only slightly better when we quantized to eight bits, instead coming in at $301 of
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error.
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Uh, so clearly we're off to a bad start with llama 3.1, but in some ways that's exciting because the
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challenge is on.
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Uh, we've got the the model here ready for us to try and explore how we can make it better.
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And our target is to try and be competitive with a model like GPT four, which is, you know, has trillions
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of parameters.
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If we can be competitive with that in this particular task, and we can do it with an open source model
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that's for free, then we have achieved a great thing.
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Okay.
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And so in summary, at this point, at this juncture, you have reached 80% of your way in this journey
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and that is absolutely fantastic.
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I'm so happy you.
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Are sticking at it.
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I'm so happy you're here.
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I got to tell you, the 20% that's still to come.
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It's the best.
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It is the best.
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The juiciest content is what's to come.
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As we look at training this model.
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And then in the finale next week, we really put everything together.
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I mean, it's just it's going to be a crescendo.
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It's going to get better and better.
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Hang in there.
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All of the good stuff is to come.
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So what we're doing next time, there's some more hyperparameters.
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I know that these hyperparameters can be a bit grueling, but this is where the really important stuff
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is learned.
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So there's some hyperparameters that control training that I'll explain to you.
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We're then going to set up something called a supervised fine tuning SFT trainer, which is where it
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all happens.
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And then we're going to kick off training.
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You're going to be training your own proprietary large language model, uh, based on, uh, llama 3.1
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based model.
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And we're going to be doing it, uh, in the next session.
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So I will see you there.