WEBVTT 00:01.100 --> 00:02.660 Actually slight change in plan. 00:02.660 --> 00:04.910 I'm going to wrap up the day. 00:04.940 --> 00:11.540 Day three at this point, and say that we'll take day four to look at the results in weights and biases, 00:11.540 --> 00:16.460 and to examine the progress of training, because I think that we should let it run for a bit and then 00:16.460 --> 00:18.620 take some, some serious time to do that. 00:18.860 --> 00:23.150 Um, I'll also at that point be able to show you the model in the Huggingface hub. 00:23.150 --> 00:28.820 And I also I do I feel like I might have been a bit glib about the amount of money that it costs to 00:28.850 --> 00:30.050 train these models. 00:30.050 --> 00:35.660 I want to be clear that you really don't need to spend any material amount of money for for to have 00:35.660 --> 00:41.960 a lot of fun with this and investigate hyperparameter optimization as part of this course, it would 00:41.960 --> 00:43.370 only cost cents. 00:43.580 --> 00:49.280 Um, and so I want to quickly explain how you can do things like reduce your training data set to a 00:49.280 --> 00:56.720 more manageable size, and have more sensible parameters so that you could train on a, um, a normal 00:56.720 --> 01:02.120 spec GPU box and be spending only a few cents on this project. 01:02.120 --> 01:07.890 That's that's all that's required if you wish to be a complete nerd like me and go all out and run lots 01:07.890 --> 01:15.180 of runs with top end boxes and spend 5 or $10 then then, uh, on your head, be it as it will be on 01:15.180 --> 01:15.690 mine. 01:15.990 --> 01:18.060 But it's not necessary at all. 01:18.810 --> 01:26.580 But you do need absolutely to take a moment to congratulate yourself on where you've got to. 01:26.610 --> 01:28.830 You have a training run happening. 01:28.860 --> 01:32.880 I do hope right now while while I speak it is running. 01:33.300 --> 01:35.700 And you are in a position now that you can explain. 01:35.700 --> 01:38.730 Q Laura pretty well for fine tuning open source models. 01:38.730 --> 01:45.360 You're so fed up with me talking about target modules, I'm sure, uh, and now, uh, explaining things 01:45.360 --> 01:52.410 like learning rates, uh, ah and alpha, uh, and uh, dropout and all of the various other things 01:52.410 --> 01:57.330 like optimizers and the like, it's all second nature to you, and it's all complex stuff. 01:57.330 --> 02:00.030 This is, uh, upskilling in a big way. 02:00.030 --> 02:01.800 So congratulations. 02:01.800 --> 02:03.780 It's tremendous progress. 02:03.810 --> 02:08.730 And next time we'll go over to weights and biases and see what's happening.