From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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166 lines
4.9 KiB
166 lines
4.9 KiB
WEBVTT |
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Well. |
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Hello there everybody. |
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I am so grateful that you've made it through to the start of week seven. |
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It is tremendous and I plan to not disappoint. |
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I will make it worth your while. |
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It's going. |
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There's some really great content ahead. |
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You may be feeling a bit of an anti-climax from what happened last week with our disappointing results. |
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Fine tuning frontier model after so much hype. |
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But what I can tell you is it's like a good movie where there's a period of time where things feel tense |
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and disappointing. |
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There's going to be redemption, there's going to be redemption. |
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We will see if you're like me and you kept hammering away after after those results, you will have, |
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I expect, got to a point where you have got some improvements. |
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You get to a point where you can fine tune and actually get better results than we originally got with |
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the Untuned version, but not significantly so, but a little bit better than I did in the last day. |
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But anyways, for now we are moving swiftly onwards. |
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We are launching into week seven and what a week do I have in store for you? |
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Uh, it is a massive week. |
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It is a really massive week. |
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It is an advanced week where we're raising the bar. |
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We're getting to a point where you're now acquiring advanced skills in this kind of deep learning field |
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of of llms. |
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And as we move more deeply into training, what you can already do, of course, you can already generate |
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text and code with frontier models, with APIs, and with hugging face open source code. |
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You can build rag pipelines and then you can now build data sets. |
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Quite a long time we spent on data sets and baseline models and frontier models that you can fine tune, |
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albeit with not amazing results. |
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For our use case today we get advanced. |
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We talk about using a technique called Lora for fine tuning open source models. |
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We describe quantization. |
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We talk about Q Laura, and we're getting into three key hyperparameters R alpha and target modules, |
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which if you've not encountered them before, sounds like something straight out of Star Trek, but |
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in fact will make complete sense by the end of today. |
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And before we launch into it, I do need to take a moment to remind you of your eight week plan, and |
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this may feel like this is boring for you at this point, but I think it's important to take pride in |
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your accomplishments and to remember where everything that you've learned, you started. |
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When we we first had our session to talk about, uh, uh, the, the opportunity with Llms and Frontier |
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models and we compared them, um, we then looked at using the UIs, we use Gradio, we used agent ization |
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Multi-modality. |
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Then we got to hugging face the pipelines and then Tokenizers and models. |
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Then in week four, we generated code by selecting LMS for the task. |
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In week five, that was rag week. |
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We used the wonderful Chroma and Lang chain to build some rag pipelines there, and hopefully you did |
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the project, which I'm really excited to see what people do there. |
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Uh, and then last week we fine tuned a frontier model. |
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Uh, now you know how to do it. |
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You understand about creating the data sets. |
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You know how it works, and you understand when it's most applicable, when you're dealing with that |
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kind of nuance of tone, and perhaps when it's not so applicable, in our case, when you're trying |
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to actually build in functionality because a frontier model is already so massive. |
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Um, so now we arrive at week seven, we are going to be fine tuning an open source model, one that |
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is significantly smaller than a frontier model, and see where we get to with that. |
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And week eight is where it all comes together. |
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And so with that introduction, the main topic of the day is Laura, and we will launch right into that.
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