WEBVTT 00:00.920 --> 00:06.260 And it's this time again, when we look at the podium of how our models are performing across the board. 00:06.260 --> 00:12.050 You already know the answer, but we should look at it anyway and and and have a giggle. 00:12.320 --> 00:17.900 Uh, so we first look at the constant model that just guesses the average value from the training data 00:17.930 --> 00:18.590 set. 00:18.950 --> 00:22.880 And if we look at that number, it was 146. 00:23.000 --> 00:26.150 It's looking distinctly lower in here right now. 00:26.150 --> 00:28.070 And I think you probably know why. 00:28.370 --> 00:34.100 Uh, then we looked at a traditional machine learning model that was able to beat the average, which 00:34.100 --> 00:36.260 we would hope it would, but not by masses. 00:36.470 --> 00:42.050 Uh, we looked at the random forest approach, which was the best of the traditional machine learning 00:42.050 --> 00:44.510 approaches coming in at 97. 00:44.510 --> 00:51.140 We did compare it with a human being and showed that human could beat the basic, simple features, 00:51.140 --> 00:53.870 but was beaten by random forest model. 00:54.140 --> 01:00.290 Uh, GPT four zero was the best of the various frontier models we looked at, and absolutely crushed 01:00.290 --> 01:04.610 it with 76 way outperforming this human. 01:04.820 --> 01:13.600 And then today we've looked at the, uh, fine tuned bass llama, 3.18 billion parameters quantized 01:13.600 --> 01:15.310 all the way down to four bits. 01:15.430 --> 01:21.190 Uh, and we saw that that was at 396, devastatingly bad. 01:21.280 --> 01:29.290 Uh, and it was only slightly better when we quantized to eight bits, instead coming in at $301 of 01:29.290 --> 01:29.950 error. 01:30.160 --> 01:36.550 Uh, so clearly we're off to a bad start with llama 3.1, but in some ways that's exciting because the 01:36.550 --> 01:37.780 challenge is on. 01:37.870 --> 01:45.640 Uh, we've got the the model here ready for us to try and explore how we can make it better. 01:45.640 --> 01:53.410 And our target is to try and be competitive with a model like GPT four, which is, you know, has trillions 01:53.410 --> 01:54.460 of parameters. 01:54.460 --> 01:59.530 If we can be competitive with that in this particular task, and we can do it with an open source model 01:59.530 --> 02:04.240 that's for free, then we have achieved a great thing. 02:04.240 --> 02:05.080 Okay. 02:05.110 --> 02:11.690 And so in summary, at this point, at this juncture, you have reached 80% of your way in this journey 02:11.690 --> 02:14.120 and that is absolutely fantastic. 02:14.120 --> 02:15.260 I'm so happy you. 02:15.290 --> 02:16.190 Are sticking at it. 02:16.190 --> 02:17.660 I'm so happy you're here. 02:17.660 --> 02:20.690 I got to tell you, the 20% that's still to come. 02:20.690 --> 02:21.740 It's the best. 02:21.740 --> 02:23.060 It is the best. 02:23.090 --> 02:25.820 The juiciest content is what's to come. 02:25.820 --> 02:27.860 As we look at training this model. 02:27.860 --> 02:32.540 And then in the finale next week, we really put everything together. 02:32.540 --> 02:35.210 I mean, it's just it's going to be a crescendo. 02:35.210 --> 02:37.160 It's going to get better and better. 02:37.160 --> 02:38.300 Hang in there. 02:38.330 --> 02:40.250 All of the good stuff is to come. 02:40.250 --> 02:44.870 So what we're doing next time, there's some more hyperparameters. 02:44.870 --> 02:49.760 I know that these hyperparameters can be a bit grueling, but this is where the really important stuff 02:49.760 --> 02:50.390 is learned. 02:50.390 --> 02:54.530 So there's some hyperparameters that control training that I'll explain to you. 02:54.560 --> 03:00.020 We're then going to set up something called a supervised fine tuning SFT trainer, which is where it 03:00.020 --> 03:00.830 all happens. 03:00.830 --> 03:03.650 And then we're going to kick off training. 03:03.650 --> 03:10.970 You're going to be training your own proprietary large language model, uh, based on, uh, llama 3.1 03:10.970 --> 03:11.720 based model. 03:11.720 --> 03:15.320 And we're going to be doing it, uh, in the next session. 03:15.320 --> 03:17.210 So I will see you there.