WEBVTT 00:00.800 --> 00:01.310 Well. 00:01.310 --> 00:02.450 Hello there everybody. 00:02.450 --> 00:06.770 I am so grateful that you've made it through to the start of week seven. 00:06.800 --> 00:10.670 It is tremendous and I plan to not disappoint. 00:10.670 --> 00:12.530 I will make it worth your while. 00:12.530 --> 00:13.190 It's going. 00:13.220 --> 00:15.830 There's some really great content ahead. 00:15.830 --> 00:22.250 You may be feeling a bit of an anti-climax from what happened last week with our disappointing results. 00:22.250 --> 00:25.760 Fine tuning frontier model after so much hype. 00:25.850 --> 00:31.040 But what I can tell you is it's like a good movie where there's a period of time where things feel tense 00:31.040 --> 00:32.210 and disappointing. 00:32.420 --> 00:35.870 There's going to be redemption, there's going to be redemption. 00:35.870 --> 00:41.480 We will see if you're like me and you kept hammering away after after those results, you will have, 00:41.480 --> 00:45.260 I expect, got to a point where you have got some improvements. 00:45.260 --> 00:50.960 You get to a point where you can fine tune and actually get better results than we originally got with 00:51.200 --> 00:58.910 the Untuned version, but not significantly so, but a little bit better than I did in the last day. 00:58.910 --> 01:02.260 But anyways, for now we are moving swiftly onwards. 01:02.260 --> 01:08.860 We are launching into week seven and what a week do I have in store for you? 01:09.010 --> 01:10.780 Uh, it is a massive week. 01:10.780 --> 01:12.400 It is a really massive week. 01:12.400 --> 01:15.220 It is an advanced week where we're raising the bar. 01:15.220 --> 01:22.600 We're getting to a point where you're now acquiring advanced skills in this kind of deep learning field 01:22.600 --> 01:23.920 of of llms. 01:23.920 --> 01:29.860 And as we move more deeply into training, what you can already do, of course, you can already generate 01:29.890 --> 01:35.350 text and code with frontier models, with APIs, and with hugging face open source code. 01:35.350 --> 01:41.080 You can build rag pipelines and then you can now build data sets. 01:41.380 --> 01:47.260 Quite a long time we spent on data sets and baseline models and frontier models that you can fine tune, 01:47.260 --> 01:49.450 albeit with not amazing results. 01:49.450 --> 01:53.830 For our use case today we get advanced. 01:53.860 --> 01:59.160 We talk about using a technique called Lora for fine tuning open source models. 01:59.160 --> 02:00.870 We describe quantization. 02:00.870 --> 02:09.480 We talk about Q Laura, and we're getting into three key hyperparameters R alpha and target modules, 02:09.780 --> 02:13.980 which if you've not encountered them before, sounds like something straight out of Star Trek, but 02:13.980 --> 02:17.460 in fact will make complete sense by the end of today. 02:17.580 --> 02:23.550 And before we launch into it, I do need to take a moment to remind you of your eight week plan, and 02:23.580 --> 02:29.130 this may feel like this is boring for you at this point, but I think it's important to take pride in 02:29.130 --> 02:33.960 your accomplishments and to remember where everything that you've learned, you started. 02:34.290 --> 02:41.220 When we we first had our session to talk about, uh, uh, the, the opportunity with Llms and Frontier 02:41.250 --> 02:49.290 models and we compared them, um, we then looked at using the UIs, we use Gradio, we used agent ization 02:49.290 --> 02:50.460 Multi-modality. 02:50.490 --> 02:55.530 Then we got to hugging face the pipelines and then Tokenizers and models. 02:55.530 --> 03:01.900 Then in week four, we generated code by selecting LMS for the task. 03:01.900 --> 03:04.060 In week five, that was rag week. 03:04.060 --> 03:09.580 We used the wonderful Chroma and Lang chain to build some rag pipelines there, and hopefully you did 03:09.580 --> 03:12.580 the project, which I'm really excited to see what people do there. 03:12.820 --> 03:16.510 Uh, and then last week we fine tuned a frontier model. 03:16.510 --> 03:17.980 Uh, now you know how to do it. 03:17.980 --> 03:19.660 You understand about creating the data sets. 03:19.660 --> 03:23.950 You know how it works, and you understand when it's most applicable, when you're dealing with that 03:23.950 --> 03:28.870 kind of nuance of tone, and perhaps when it's not so applicable, in our case, when you're trying 03:28.870 --> 03:33.820 to actually build in functionality because a frontier model is already so massive. 03:34.270 --> 03:41.470 Um, so now we arrive at week seven, we are going to be fine tuning an open source model, one that 03:41.470 --> 03:46.540 is significantly smaller than a frontier model, and see where we get to with that. 03:46.540 --> 03:49.120 And week eight is where it all comes together. 03:49.210 --> 03:55.300 And so with that introduction, the main topic of the day is Laura, and we will launch right into that.