So here we are on Hugging Face's main landing page at Hugging Face Core.
A URL you know.
Well, since we produced the company brochure for Hugging Face a couple of times.
And, uh, this is this is what you'll see.
If you're not logged in, you don't have an account.
You'll see a screen like this.
And the first thing you need to do is go to sign up.
If you don't already have a hugging face account, it is free to join email address and a password and
then you will be in.
And once you're in, you'll get to see something like this.
Uh, the main navigation of Hugging Face is up here, and you can see the first three parts of the main
navigation are models, datasets and spaces.
And that is what we're going to look at right now.
So the first tab models, this is where in the hugging face platform or the hub as it's known, you
can see all of the models that are available to you.
I said before that it's more than 800,000 is in fact more than 900,000.
Look at that.
And this is sorted, I think, by default by trending, which is some combination of recency and popularity.
You can also turn that into by how popular it is, how many times it's been downloaded and how recently
it was created or updated.
As you can see here, the name of each of these models is typically the name of the organization and
then the name of the model.
Flux is one of the latest and most exciting text to image models, and I do believe we'll be playing
with that at some point.
Quanta, the powerhouse model from Alibaba Cloud that I've mentioned a few times now.
And you can see typically you have the number of parameters often in the description of a lamb.
And then instruct at the end tells us that this has been fine tuned to be most applicable during a sort
of chat and instruct kind of interaction with the input.
And we can see lots of other models here.
There's of course a filter up here.
So we can filter on something like llama to look at the llama model, which was Meta's model, which
is so very well known, so very popular.
Here it is.
These are all various um, times that meta or llama will have been mentioned at some point in these
descriptions.
But you can see what we're really looking for is is here this is Meta llama 3.18 billion version model.
And that's the same one fine tuned for the instruct use case.
The this kind of uh um chat use case.
So let's go in here.
If you go into llama 3.18 billion, you see a ton of information about it.
You see that it's been downloaded a large number of times.
Uh, recently we'll be downloading it ourselves, I think, on many occasions over the coming weeks.
Uh, you get some information about the model architecture, uh, the languages, the family, the how
it's intended to be used, and then some code examples down at the end.
Uh, so this is all useful stuff to read about.
Also information about how it was trained.
Um, and lots of other things worth reading about.
There's also this tab here, Files and Versions, which opens up something that looks a bit like a git
repository.
And it's funny you should think that because it actually is a git repository.
A lot of what you can think of the hugging face hub as is a sort of interface on top of a series of
git repos.
And often when you're downloading a model or downloading data, what you're really doing is doing a
sort of a pull from git get and getting these files locally.
So this is the the the the folder and file structure that sits behind the meta llama 3.18 billion.
And there are various ways that you can use it.
There's a button here.
Use this model that will give you more information if you go into it on what you need to do.
That gives you actual examples of code.
We'll be using this code later, so don't worry about it right now.
You don't need to to read this, but know that you can always go to use this model.
Select Transformers, which means you want to use it using hugging face Transformers library, and then
copy code examples directly from the user interface.
As simple as that.
And there's other stuff here that we'll look at another day.
There's some tags of course, which also allows you to filter on different, uh, aspects of models
very quickly and easily.
We'll be looking at lots of other models in time.
We'll be looking at things like the Phi model from Microsoft and which I think I mentioned.
We'll be looking, of course, at quanta, and that you've actually already seen and plenty of others.
Maybe I'll mention Google's Gemma that you'll see Gemma will come up when I do this.
Um, it did come up and then it went away.
So let's do Google slash Gemma.
There we go.
Um, so this, for example, the 2 billion very small on device version of Gemma and it as before has
the description, the code examples, the files and versions and the ability to use this model just
by clicking there like so.
That's models.
Let's move on to data sets.
Data sets shows you the vast resource of data that's available on the Hugging Face hub.
And again, you've got the ability to search.
It's sorted by default on trending.
And let me say later on, we're going to be doing some experiments using prices of products.
And one of the things we'd love to see is some sort of scrape of product data.
And it turns out there is in fact, there are a bunch of repositories of data related to prices on Amazon.
Here is some of them.
You can have a look around the popularity of them and which ones are useful.
We are in fact going to be using this one here, which is very recent and which is very comprehensive
indeed and has tons of useful data.
So it's absolutely phenomenal resource.
So do take a look at this and you can you can also use things like data set viewers and other tools
that come with the data sets.
Part of the Huggingface hub spaces I mentioned is where Gradio radio apps and other kinds of apps can
run to do things that people in the community want to show off to others, or just get to get people
using them.
And you can do the same with your hugging face apps or your radio apps.
There's a lot of things to try out here.
Uh, there's uh, there's sort of spaces of the week.
There's there's things that are trending, and then there's a few types of spaces that we will be looking
at a lot more in the next few or probably next week, mostly about leaderboards, comparing different
LMS.
Um, there's a plenty of fun spaces where you can try out different interesting LMS or generative AI
applications.
Um, one of the things I do tend to find is that sometimes because a lot of these are running for free
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
hard to get it to run because it will tell you that it's too busy right now.
Um, but I think that comes with the territory of free software.
When it does work, it is wonderful.
So, for example, I just tried out a couple that were in the top spaces.
So this one here, the AI Comic Factory, you can give it a style, you can give it a title of your
comic and give it some something about how you want it to think when it's producing it.
And I did a super powered data scientist comic, and you get this very cross looking, presumably the
villain, and then you get presumably the heroine saying data is power, but sometimes it needs a little
push.
And so it's great fun.
This is an imagined created comic strip based on a topic that you choose, and I encourage you to come
in and give it a try.
It did take me 2 or 3 tries before I got this because it was too much in demand, but it's for me.
It's on the first page of Most Popular right now, but if you find this or something like it, give
it a try.
Another thing that I tried out that was available was this.
This is pretty funny.
You can upload an image of yourself.
I chose the one with me in front of the plane from before, which I cannot fly.
But that hasn't stopped me from trying.
And then you can pick a garment or upload a garment, and then it will show you wearing that garment.
I mean, it's all right.
It's not perfect.
I don't know what's happening.
I'm sort of hunched up like this a bit, but it gives you the idea.
It's interesting to see that some sort of strange artifact has happened with the plane behind me.
Uh, but, um, aside from that, it's fun.
It's free, it's easy to use, and it's a classic example of people having good ideas about fun things
you can do with AI, with Llms, and have surfaced it for others to play with.
You will find many examples of this on spaces.
Uh, so then the other thing I wanted to show you, is to show you what happens if you go to to the
avatar menu and look at yourself.
So here is, um, the, uh, I'm just going to go straight here.
Uh, I can see my own what I've done.
I have one space, I've got a bunch of different models, some of which will be playing with ourselves,
and I've got a bunch of data sets, and they're all private.
You can make them private if you only want to have access to them, or public if you want the world
to see them.
Um, and I've got various data sets that we will be talking about many of these in the next few weeks,
and I think you'll have fun with them.
Uh, and this space that I've got, for example, I also will might refer to, um, just just as an
example of how easy it is to do this stuff.
But in this case, this is public and this is a game that I built which allows you to have llms compete
against each other to try to battle.
It was inspired by the the battle, the leadership battle we had in the first week.
And I beefed that up a bit to make something where different lambs can fight in a way, to try and outwit
each other and take money from each other, following some rules.
And you can play a game and watch it run.
Maybe we'll do that when we talk about the differences between lambs next week, I'll see if we have
time.
If not, by all means come and give this a try yourself to see how I've done, and you can see it as
an example of how it's easy to take an LLM application with either a gradio front end.
In this case, this is called Streamlit, a different kind of user interface or others.
And then publish it to be available to everyone on huggingface spaces.
One more thing about this menu that's worth mentioning is that if you go to your profile and your,
uh, sorry, if you go to your settings, I mean, down here to settings, uh, down here is a section
called Access Tokens.
This is something you need to do if you haven't done it before.
You go to access tokens and then there's a simple button create new token.
You press that to give yourself a new token, a new API token, where you will ask for both read and
write permissions.
That is a token that we'll be using in Jupyter in order to get access to the hub, and in order to both
download and upload models and data.
So that is a key part of setting up your hugging face account.
And that concludes our very quick tour of all things hugging face.
There's so much to explore.
The to do for you is now go in.
If you haven't already, set up your account, set up your API key, it's all free.
And then go hunt up models for datasets and look around some of the spaces and try out some of the cool
products that people have made available for all of the community to try.
Enjoy that.