From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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157 lines
4.5 KiB
157 lines
4.5 KiB
WEBVTT |
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So it's quite an adventure that we had at the frontier of what's capable with Llms today, solving a |
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particular problem that required world knowledge and to take you through what we saw from the performance |
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of everything as a reminder from last time we started. |
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Well, actually, we started with the random model, but we'll forget about that because that was silly. |
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We're proper first starting point was a constant model that just predicted an average number. |
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We obviously were able to do better, but not that much better. |
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With a model that used feature engineering, you may have improved on that with better features. |
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Um, but our best one was a random forest model based on a not a bag of words. |
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A word to vec vectorized. |
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Uh, look at the prompts with 400 dimensional vectors. |
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And that brought our error. |
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The average difference between the prediction and the actual price of a product based on its description |
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down to $97 being after being trained on 400,000 example data points. |
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We then, uh, unveiled the human today. |
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That was our first model. |
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Uh, and the human got 127 in terms of error. |
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So you'll see, I was able to, uh, at least do better than the very primitive feature engineering. |
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And at least I did better than than constant. |
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I think I wouldn't have, uh, I might not have included the whole result if I hadn't done better than |
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a constant number. |
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Uh, but, uh, obviously, uh, the next, uh, Claude clearly, uh, did significantly better than |
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me. |
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And Claude was very, very similar to Random Forest, uh, so very much on par. |
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And again, one has to bear in mind, Claude is doing this without seeing any training data. |
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It's just purely based on its world knowledge. |
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And then being given this product. |
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And I can tell you from bitter personal experience that that is a challenging task. |
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But GPT four mini did better and got down to an $80 error, and GPT four did even better yet and brought |
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it down to $76 in terms of the difference. |
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So it shows you that out of the box, working with frontier models and APIs, you can build solutions |
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to problems, even problems which feel like they are regression problems. |
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They're not. |
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They're numerical problems. |
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They're not. |
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They don't necessarily naturally sound like they should be ones where just text completion will be able |
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to solve them. |
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But even given that kind of problem still out of the box, GPT four mini is able to outperform a random |
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forest model, a traditional machine learning model with 400,000 training data points. |
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So it just goes to show you how powerful these models are and how they can be applied to so many types |
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of commercial problem. |
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But with that, we can now finally move on to the world of training. |
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The next subject is going to be about how we take this further, by fine tuning a frontier model to |
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take what it's got and do better with training examples. |
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The thing that it hasn't had so far. |
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So that is a big and exciting topic. |
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It will then complete this week before next week. |
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We take it to a whole different world where we try and fine tune our own open source model to see if |
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we can compete, bearing in mind that we'll be dealing with something with massively fewer parameters. |
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So a very different world. |
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And to see whether or not we have a hope of beating traditional machine learning or frontier models. |
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Lots to be excited about. |
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But first, I will see you tomorrow for fine tuning.
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