From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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187 lines
5.6 KiB
187 lines
5.6 KiB
WEBVTT |
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Let's now see our results side by side. |
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We started our journey with a constant model that was at $1.46 error from the average of all of the |
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data. |
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In the training data set, we looked at a traditional machine learning model with just a few features. |
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It got to 1.39. |
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It beat the average random forest. |
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The best of our traditional machine learning models got to 97. |
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The human got to 127, GPT four got to 76, and our fine tuned customized model has got us to $47. |
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And this, of course, brings me to the challenge for you. |
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The challenge for you is to improve on this $47. |
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Uh, this is something where hyperparameter optimization can go a long way. |
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And so the task at hand is now to play with the hyperparameters experiment, use weights and biases. |
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Uh, explore maybe different optimizers, different learning rates, different batch sizes, and see |
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what you can do to improve on this number. |
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You can also explore different ways of running the model at inference time to see if that improves it. |
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And then there is one factor that is actually perhaps the way that you can make the the biggest impact |
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on the results with the smallest change is to relook one more time at the data set, at the data curation |
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step, and challenge yourself to see whether you can think of different ways to be prompting or organizing |
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that information in order to be getting better outcomes. |
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And then there is one final thing that you could do, which is a bigger step, but very exciting. |
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And if you don't do it, I'm definitely going to do it, which is to try the other models on this. |
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Try Jama, try Kwon the powerhouse, try fi three, see how they perform. |
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There are a couple of places where it might be a bit fiddly, because they might not predict one token |
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as the right number, just maybe that's only at that inference function. |
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Otherwise everything else might just be fine. |
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But you'll need to experiment with that and convince yourself that it's that it's so good. |
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So try some different models. |
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You could also try doing the whole thing with a version of llama three that's quantized to eight bits |
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instead of four bits, depending on your appetite for that. |
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There are also some larger models. |
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There is a version of 53, I think that is 14 billion parameters that you could experiment with and |
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see whether that improves things. |
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So that is the objective. |
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I would love to hear from the first person that can get this below 40. |
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That has to be possible. |
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I think there is like a hard limit on how low you can get it, given the reality of uncertainty in pricing, |
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but I think you guys, someone is going to be able to get it below $40. |
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You'll build a model that can get within $40 across the 251st items in the test set, and I can't wait |
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to hear about that. |
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So please do reach out and tell me when you get below 40, and tell me your hyperparameters and your |
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model so that I can try and recreate it myself. |
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I would love that. |
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And with that, let's wrap up for the week. |
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It's the end now of week seven where you can now do, of course, obviously generating text and code |
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with frontier APIs and with open source models and hugging face, you can solve problems including dataset |
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curation, a baseline model, and fine tuning. |
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And at this point, you can confidently carry out the full process for selecting and training an open |
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source model that can outperform the frontier. |
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And that's a big accomplishment. |
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So next week is the finale of this course, and I promise you, I've kept the best to last. |
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It's going to be a triumph. |
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Next week is going to be so much fun. |
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You've you've got this far. |
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Hang on in there to the very end to see everything come together. |
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There's some stuff that's really important we're going to do about now packaging up what we've done |
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and being able to deploy it as behind an API so we can use it for production purposes, and then really |
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package everything into an application that can can make a real impact. |
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And at that point, you'll be in a position to be creating your own end to end solutions to commercial |
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problems. |
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Using groundbreaking llms that you'll be able to train yourself. |
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So there's a lot ahead, and I can't wait for next week. |
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And as always, I will see you there.
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