WEBVTT 00:00.050 --> 00:05.870 So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using the 00:05.870 --> 00:07.400 power of Olama. 00:07.430 --> 00:10.040 You've probably had similar experience to me, which is. 00:10.040 --> 00:16.130 I certainly found that Quinn 2.5 is perhaps the most powerful model when it comes to grasp of different 00:16.130 --> 00:16.820 languages. 00:16.820 --> 00:20.300 Some of the other models are better, I think, at explaining. 00:20.360 --> 00:22.820 So I'd be very interested to hear your observations. 00:22.820 --> 00:26.750 Please do post them with the course or message me direct. 00:26.780 --> 00:31.370 I'd love to hear what you've discovered and this kind of experimenting with different models and and 00:31.370 --> 00:34.460 finding the one that works best for your your problem. 00:34.460 --> 00:37.160 That is a critical skill for an LM engineer. 00:37.160 --> 00:39.560 So this was valuable time spent. 00:39.590 --> 00:40.370 All right. 00:40.370 --> 00:42.620 Let's talk about the next eight weeks. 00:42.620 --> 00:50.570 So I am looking to take you from where you are today, over on the left to being a master of LM engineering 00:50.570 --> 00:51.650 in eight weeks time. 00:51.650 --> 00:53.090 And this is how we'll do it. 00:53.120 --> 00:59.150 We'll start this week by looking at models at the frontier of what's possible today, which people call 00:59.150 --> 01:00.200 frontier models. 01:00.260 --> 01:09.230 Things like GPT 4001 preview and Claude 3.5, and a number of other pioneering models that are closed 01:09.240 --> 01:11.730 source and are able to achieve amazing things. 01:11.730 --> 01:16.800 And we'll do that through the web user interface like ChatGPT, and also then through the APIs. 01:17.280 --> 01:22.410 And we're going to build a commercial project, something immediately that will be useful, and there'll 01:22.440 --> 01:25.290 be an interesting commercial exercise for you as well. 01:25.410 --> 01:31.380 Then next week we will slap a user interface on top of it using a platform which I love, which is called 01:31.380 --> 01:32.160 Gradio. 01:32.160 --> 01:34.980 And we will have good fun with it and you'll see that I love it. 01:34.980 --> 01:40.260 I go on about it a bit, but it's so easy to use and it's so easy for people like me who are terrible 01:40.260 --> 01:45.300 at front end to build a nice, sharp user interface very quickly indeed. 01:45.300 --> 01:53.400 We'll do it to solve a classic JNI use case, which is the building an AI assistant, a chatbot, and 01:53.400 --> 01:57.120 we'll but we'll do so in a way that has audio and pictures. 01:57.120 --> 02:03.720 So it's multimodal and it will be able to use tools, which means that it's able to call out to code 02:03.720 --> 02:07.650 running on your computer, which sounds kind of spooky, but it's going to make sense when we do it. 02:07.650 --> 02:08.760 So that is all. 02:08.760 --> 02:09.690 Week two. 02:10.290 --> 02:16.380 In week three, we turn to open source, and we use the ubiquitous Hugging Face platform, which is 02:16.380 --> 02:21.870 used by data scientists and LM engineers across the board and will use it to build both. 02:21.960 --> 02:27.270 We'll use the simple API in hugging face called the pipelines API, and then we'll use the more advanced 02:27.270 --> 02:32.040 API, and we'll explore things like Tokenizers and models in Hugging Face. 02:32.280 --> 02:38.460 In week four, we're going to talk about something which is a particularly thorny issue in the world 02:38.460 --> 02:42.840 of AI, which is there are so many models to choose from. 02:42.840 --> 02:47.220 How do you go about selecting what is the right model for the task you have at hand? 02:47.220 --> 02:52.650 So we'll we'll work on things like benchmarks and leaderboards and figure out how do you go about that 02:52.650 --> 02:53.940 decision path. 02:53.940 --> 02:58.560 And then we're going to take on a particularly different kind of commercial problem about generating 02:58.560 --> 02:59.160 code. 02:59.160 --> 03:06.330 We're going to build an application which is able to rewrite Python code as C plus plus high performance 03:06.330 --> 03:07.380 C plus plus code. 03:07.380 --> 03:11.010 And we're going to then try it out with a bunch of closed source and open source models. 03:11.010 --> 03:12.270 And one of them will be the winner. 03:12.300 --> 03:15.390 The one that's the winner is going to take our test Python code. 03:15.390 --> 03:22.620 It's going to rewrite it and the new code is going to run 60,000 times faster, which is shocking. 03:22.650 --> 03:24.210 And you will see that yourself. 03:24.270 --> 03:28.760 And then there'll be some exercises for you to build other kinds of code generation tools. 03:29.210 --> 03:35.780 In week five, we will turn to one of the the topics that is super hot at the moment, which is rag 03:35.780 --> 03:43.490 retrieval, augmented generation, using, uh, data stores of information to add expertise to your 03:43.490 --> 03:49.640 LLM will be building our own Rag pipeline for answering questions that pertain to an organization. 03:49.640 --> 03:54.860 And then there'll be a difficult commercial challenge for you and exercise in which you apply this to 03:54.890 --> 03:56.480 your own information. 03:56.480 --> 04:01.640 And I'm really excited to see what people make of this, and to see some of your projects of rebuilding 04:01.670 --> 04:03.860 a Rag pipeline for yourself. 04:04.790 --> 04:10.400 In week six, we begin our three week flagship project for this course. 04:10.400 --> 04:13.250 Uh, week six, we will set up the business problem. 04:13.250 --> 04:19.340 We'll do a lot of work on data, and we're then going to create some traditional machine learning models, 04:19.340 --> 04:21.410 which is very important to do to build a baseline. 04:21.410 --> 04:26.720 And then we'll try models at the frontier, and we'll fine tune models at the frontier as well, to 04:26.750 --> 04:29.810 do as well as we possibly can with this business problem. 04:29.810 --> 04:33.110 In week seven, we'll apply it to open source. 04:33.110 --> 04:38.080 We're going to take open source models and they're initially going to perform terribly, and we're going 04:38.080 --> 04:44.440 to make it our mission to improve those open source models by fine tuning until at least we can compete 04:44.440 --> 04:46.000 with GPT four. 04:46.210 --> 04:47.680 The model at the frontier. 04:47.680 --> 04:52.810 And I'm not going to tell you what happens, but I will tell you that I believe that the results will 04:52.810 --> 04:53.770 astonish you. 04:53.800 --> 04:54.880 I will tell you that. 04:54.880 --> 04:59.080 So it is very much worth hanging on and seeing what happens in week seven. 04:59.350 --> 05:05.200 But then it all comes together in the finale in week eight, which is a fitting conclusion to the eight 05:05.230 --> 05:13.060 weeks we are going to build a fully autonomous Agentic AI solution, which will have seven agents collaborating 05:13.060 --> 05:15.430 to solve a real commercial problem. 05:15.730 --> 05:17.170 And the end. 05:17.200 --> 05:21.400 Not only will it be doing something where it scans the internet for various things, but will end up 05:21.400 --> 05:24.880 sending you push notifications with some of its discoveries. 05:24.940 --> 05:27.340 So it's going to be really fabulous. 05:27.340 --> 05:30.100 It's going to have a terrific result at the end of it. 05:30.130 --> 05:35.830 It will be a good way to to be a culmination of everything that you've learned each week, building 05:35.830 --> 05:41.710 on top of it, of the of the prior week, and resulting in true commercial projects that you'll be able 05:41.710 --> 05:45.100 to put into action in your day job right away.