WEBVTT 00:00.410 --> 00:06.860 So here we are on Hugging Face's main landing page at Hugging Face Core. 00:06.890 --> 00:07.790 A URL you know. 00:07.790 --> 00:11.390 Well, since we produced the company brochure for Hugging Face a couple of times. 00:11.510 --> 00:14.270 And, uh, this is this is what you'll see. 00:14.270 --> 00:16.760 If you're not logged in, you don't have an account. 00:16.760 --> 00:18.530 You'll see a screen like this. 00:18.530 --> 00:20.870 And the first thing you need to do is go to sign up. 00:20.870 --> 00:27.470 If you don't already have a hugging face account, it is free to join email address and a password and 00:27.470 --> 00:29.210 then you will be in. 00:29.240 --> 00:32.810 And once you're in, you'll get to see something like this. 00:33.590 --> 00:40.040 Uh, the main navigation of Hugging Face is up here, and you can see the first three parts of the main 00:40.040 --> 00:42.680 navigation are models, datasets and spaces. 00:42.680 --> 00:45.050 And that is what we're going to look at right now. 00:45.350 --> 00:52.820 So the first tab models, this is where in the hugging face platform or the hub as it's known, you 00:52.820 --> 00:56.690 can see all of the models that are available to you. 00:56.900 --> 01:02.090 I said before that it's more than 800,000 is in fact more than 900,000. 01:02.120 --> 01:03.140 Look at that. 01:03.530 --> 01:10.460 And this is sorted, I think, by default by trending, which is some combination of recency and popularity. 01:10.490 --> 01:16.580 You can also turn that into by how popular it is, how many times it's been downloaded and how recently 01:16.580 --> 01:18.320 it was created or updated. 01:19.310 --> 01:24.920 As you can see here, the name of each of these models is typically the name of the organization and 01:24.920 --> 01:26.450 then the name of the model. 01:26.780 --> 01:34.610 Flux is one of the latest and most exciting text to image models, and I do believe we'll be playing 01:34.610 --> 01:35.780 with that at some point. 01:36.110 --> 01:42.170 Quanta, the powerhouse model from Alibaba Cloud that I've mentioned a few times now. 01:42.170 --> 01:48.590 And you can see typically you have the number of parameters often in the description of a lamb. 01:48.590 --> 01:56.540 And then instruct at the end tells us that this has been fine tuned to be most applicable during a sort 01:56.540 --> 02:01.980 of chat and instruct kind of interaction with the input. 02:02.700 --> 02:05.820 And we can see lots of other models here. 02:05.820 --> 02:07.740 There's of course a filter up here. 02:07.740 --> 02:16.830 So we can filter on something like llama to look at the llama model, which was Meta's model, which 02:16.830 --> 02:20.400 is so very well known, so very popular. 02:20.550 --> 02:21.240 Here it is. 02:21.270 --> 02:26.550 These are all various um, times that meta or llama will have been mentioned at some point in these 02:26.550 --> 02:27.000 descriptions. 02:27.000 --> 02:35.220 But you can see what we're really looking for is is here this is Meta llama 3.18 billion version model. 02:35.220 --> 02:39.720 And that's the same one fine tuned for the instruct use case. 02:39.750 --> 02:43.500 The this kind of uh um chat use case. 02:43.500 --> 02:45.000 So let's go in here. 02:45.000 --> 02:51.540 If you go into llama 3.18 billion, you see a ton of information about it. 02:51.540 --> 02:55.170 You see that it's been downloaded a large number of times. 02:55.320 --> 03:00.450 Uh, recently we'll be downloading it ourselves, I think, on many occasions over the coming weeks. 03:00.570 --> 03:06.990 Uh, you get some information about the model architecture, uh, the languages, the family, the how 03:06.990 --> 03:12.270 it's intended to be used, and then some code examples down at the end. 03:12.420 --> 03:15.420 Uh, so this is all useful stuff to read about. 03:15.420 --> 03:17.550 Also information about how it was trained. 03:17.820 --> 03:22.590 Um, and lots of other things worth reading about. 03:23.100 --> 03:30.330 There's also this tab here, Files and Versions, which opens up something that looks a bit like a git 03:30.330 --> 03:31.380 repository. 03:31.380 --> 03:36.480 And it's funny you should think that because it actually is a git repository. 03:36.510 --> 03:42.930 A lot of what you can think of the hugging face hub as is a sort of interface on top of a series of 03:42.960 --> 03:44.190 git repos. 03:44.190 --> 03:51.330 And often when you're downloading a model or downloading data, what you're really doing is doing a 03:51.360 --> 03:56.830 sort of a pull from git get and getting these files locally. 03:56.830 --> 04:08.110 So this is the the the the folder and file structure that sits behind the meta llama 3.18 billion. 04:08.440 --> 04:12.430 And there are various ways that you can use it. 04:12.520 --> 04:14.020 There's a button here. 04:14.020 --> 04:18.880 Use this model that will give you more information if you go into it on what you need to do. 04:18.880 --> 04:21.490 That gives you actual examples of code. 04:21.490 --> 04:24.370 We'll be using this code later, so don't worry about it right now. 04:24.670 --> 04:28.780 You don't need to to read this, but know that you can always go to use this model. 04:28.780 --> 04:34.390 Select Transformers, which means you want to use it using hugging face Transformers library, and then 04:34.390 --> 04:38.230 copy code examples directly from the user interface. 04:38.260 --> 04:39.850 As simple as that. 04:40.480 --> 04:43.270 And there's other stuff here that we'll look at another day. 04:43.300 --> 04:50.290 There's some tags of course, which also allows you to filter on different, uh, aspects of models 04:50.290 --> 04:51.940 very quickly and easily. 04:52.000 --> 04:55.420 We'll be looking at lots of other models in time. 04:55.420 --> 05:01.750 We'll be looking at things like the Phi model from Microsoft and which I think I mentioned. 05:01.780 --> 05:08.680 We'll be looking, of course, at quanta, and that you've actually already seen and plenty of others. 05:08.680 --> 05:14.350 Maybe I'll mention Google's Gemma that you'll see Gemma will come up when I do this. 05:14.590 --> 05:16.750 Um, it did come up and then it went away. 05:16.750 --> 05:19.900 So let's do Google slash Gemma. 05:19.930 --> 05:21.070 There we go. 05:21.340 --> 05:30.850 Um, so this, for example, the 2 billion very small on device version of Gemma and it as before has 05:30.850 --> 05:37.990 the description, the code examples, the files and versions and the ability to use this model just 05:37.990 --> 05:40.360 by clicking there like so. 05:40.870 --> 05:42.280 That's models. 05:42.280 --> 05:44.080 Let's move on to data sets. 05:44.110 --> 05:50.920 Data sets shows you the vast resource of data that's available on the Hugging Face hub. 05:50.920 --> 05:53.470 And again, you've got the ability to search. 05:53.500 --> 05:55.600 It's sorted by default on trending. 05:55.870 --> 06:03.250 And let me say later on, we're going to be doing some experiments using prices of products. 06:03.250 --> 06:07.810 And one of the things we'd love to see is some sort of scrape of product data. 06:07.840 --> 06:15.850 And it turns out there is in fact, there are a bunch of repositories of data related to prices on Amazon. 06:15.850 --> 06:17.170 Here is some of them. 06:17.200 --> 06:21.220 You can have a look around the popularity of them and which ones are useful. 06:21.220 --> 06:26.380 We are in fact going to be using this one here, which is very recent and which is very comprehensive 06:26.380 --> 06:29.830 indeed and has tons of useful data. 06:29.950 --> 06:32.950 So it's absolutely phenomenal resource. 06:33.100 --> 06:40.390 So do take a look at this and you can you can also use things like data set viewers and other tools 06:40.390 --> 06:42.730 that come with the data sets. 06:42.760 --> 06:50.360 Part of the Huggingface hub spaces I mentioned is where Gradio radio apps and other kinds of apps can 06:50.360 --> 06:56.960 run to do things that people in the community want to show off to others, or just get to get people 06:56.960 --> 06:57.470 using them. 06:57.470 --> 07:02.600 And you can do the same with your hugging face apps or your radio apps. 07:02.600 --> 07:05.090 There's a lot of things to try out here. 07:05.240 --> 07:07.850 Uh, there's uh, there's sort of spaces of the week. 07:07.850 --> 07:12.230 There's there's things that are trending, and then there's a few types of spaces that we will be looking 07:12.230 --> 07:19.340 at a lot more in the next few or probably next week, mostly about leaderboards, comparing different 07:19.370 --> 07:20.240 LMS. 07:20.480 --> 07:28.760 Um, there's a plenty of fun spaces where you can try out different interesting LMS or generative AI 07:28.760 --> 07:29.870 applications. 07:30.140 --> 07:35.330 Um, one of the things I do tend to find is that sometimes because a lot of these are running for free 07:35.330 --> 07:41.120 on free boxes, it can be a bit flaky in that if it's a popular one, then it's overused and it's quite 07:41.120 --> 07:45.050 hard to get it to run because it will tell you that it's too busy right now. 07:45.200 --> 07:48.750 Um, but I think that comes with the territory of free software. 07:49.050 --> 07:51.150 When it does work, it is wonderful. 07:51.150 --> 07:54.870 So, for example, I just tried out a couple that were in the top spaces. 07:54.870 --> 08:02.850 So this one here, the AI Comic Factory, you can give it a style, you can give it a title of your 08:02.850 --> 08:08.340 comic and give it some something about how you want it to think when it's producing it. 08:08.490 --> 08:15.720 And I did a super powered data scientist comic, and you get this very cross looking, presumably the 08:15.720 --> 08:21.390 villain, and then you get presumably the heroine saying data is power, but sometimes it needs a little 08:21.390 --> 08:22.050 push. 08:22.440 --> 08:24.630 And so it's great fun. 08:24.630 --> 08:31.740 This is an imagined created comic strip based on a topic that you choose, and I encourage you to come 08:31.740 --> 08:32.700 in and give it a try. 08:32.700 --> 08:40.410 It did take me 2 or 3 tries before I got this because it was too much in demand, but it's for me. 08:40.410 --> 08:44.880 It's on the first page of Most Popular right now, but if you find this or something like it, give 08:44.880 --> 08:45.590 it a try. 08:45.620 --> 08:50.480 Another thing that I tried out that was available was this. 08:50.480 --> 08:51.650 This is pretty funny. 08:51.710 --> 08:53.360 You can upload an image of yourself. 08:53.360 --> 08:58.400 I chose the one with me in front of the plane from before, which I cannot fly. 08:58.790 --> 09:00.800 But that hasn't stopped me from trying. 09:01.190 --> 09:07.790 And then you can pick a garment or upload a garment, and then it will show you wearing that garment. 09:07.820 --> 09:08.810 I mean, it's all right. 09:08.810 --> 09:09.860 It's not perfect. 09:09.860 --> 09:11.030 I don't know what's happening. 09:11.240 --> 09:14.720 I'm sort of hunched up like this a bit, but it gives you the idea. 09:14.720 --> 09:19.760 It's interesting to see that some sort of strange artifact has happened with the plane behind me. 09:19.940 --> 09:23.570 Uh, but, um, aside from that, it's fun. 09:23.570 --> 09:29.900 It's free, it's easy to use, and it's a classic example of people having good ideas about fun things 09:29.900 --> 09:36.050 you can do with AI, with Llms, and have surfaced it for others to play with. 09:36.080 --> 09:39.590 You will find many examples of this on spaces. 09:40.190 --> 09:47.460 Uh, so then the other thing I wanted to show you, is to show you what happens if you go to to the 09:47.460 --> 09:49.260 avatar menu and look at yourself. 09:49.260 --> 09:54.390 So here is, um, the, uh, I'm just going to go straight here. 09:54.570 --> 09:57.360 Uh, I can see my own what I've done. 09:57.360 --> 10:03.330 I have one space, I've got a bunch of different models, some of which will be playing with ourselves, 10:03.330 --> 10:05.880 and I've got a bunch of data sets, and they're all private. 10:05.880 --> 10:10.530 You can make them private if you only want to have access to them, or public if you want the world 10:10.530 --> 10:11.400 to see them. 10:11.610 --> 10:17.250 Um, and I've got various data sets that we will be talking about many of these in the next few weeks, 10:17.250 --> 10:18.720 and I think you'll have fun with them. 10:19.080 --> 10:25.890 Uh, and this space that I've got, for example, I also will might refer to, um, just just as an 10:25.890 --> 10:28.350 example of how easy it is to do this stuff. 10:28.440 --> 10:37.050 But in this case, this is public and this is a game that I built which allows you to have llms compete 10:37.050 --> 10:39.540 against each other to try to battle. 10:39.540 --> 10:45.360 It was inspired by the the battle, the leadership battle we had in the first week. 10:45.510 --> 10:53.340 And I beefed that up a bit to make something where different lambs can fight in a way, to try and outwit 10:53.370 --> 10:57.390 each other and take money from each other, following some rules. 10:57.390 --> 10:59.340 And you can play a game and watch it run. 10:59.340 --> 11:04.980 Maybe we'll do that when we talk about the differences between lambs next week, I'll see if we have 11:04.980 --> 11:05.370 time. 11:05.370 --> 11:09.570 If not, by all means come and give this a try yourself to see how I've done, and you can see it as 11:09.570 --> 11:16.500 an example of how it's easy to take an LLM application with either a gradio front end. 11:16.500 --> 11:21.120 In this case, this is called Streamlit, a different kind of user interface or others. 11:21.120 --> 11:25.740 And then publish it to be available to everyone on huggingface spaces. 11:26.490 --> 11:33.300 One more thing about this menu that's worth mentioning is that if you go to your profile and your, 11:33.300 --> 11:40.840 uh, sorry, if you go to your settings, I mean, down here to settings, uh, down here is a section 11:40.840 --> 11:42.790 called Access Tokens. 11:42.790 --> 11:45.910 This is something you need to do if you haven't done it before. 11:45.940 --> 11:50.950 You go to access tokens and then there's a simple button create new token. 11:50.950 --> 11:58.060 You press that to give yourself a new token, a new API token, where you will ask for both read and 11:58.060 --> 11:59.230 write permissions. 11:59.230 --> 12:07.030 That is a token that we'll be using in Jupyter in order to get access to the hub, and in order to both 12:07.030 --> 12:09.970 download and upload models and data. 12:09.970 --> 12:14.260 So that is a key part of setting up your hugging face account. 12:14.350 --> 12:18.250 And that concludes our very quick tour of all things hugging face. 12:18.250 --> 12:19.990 There's so much to explore. 12:20.050 --> 12:22.420 The to do for you is now go in. 12:22.450 --> 12:26.650 If you haven't already, set up your account, set up your API key, it's all free. 12:26.650 --> 12:34.300 And then go hunt up models for datasets and look around some of the spaces and try out some of the cool 12:34.300 --> 12:37.990 products that people have made available for all of the community to try. 12:38.110 --> 12:39.220 Enjoy that.