So we're now going to run a large language model directly on your box using a platform called llama,
which lets you run C plus plus code version of Llms compiled directly on your machine.
I'm going to start by showing you on windows, and then we'll flip over to Mac.
So to start with llama you bring up your favorite browser.
Let's go to Microsoft Edge.
And you can simply go to llama.
Com or just search llama llama it's O followed by llama.
And the first hit here in the search results will be Alarm.com.
And here it is.
It's called llama because it was initially built around a model that's called llama, which is the open
source model from meta, which really stole the show a couple of years ago when it first came out.
So downloading is as simple as pressing the download button.
And once you press the download button, you press download for windows and it will start to download
the windows version.
And if I look here at my downloads, I will see that it has downloaded.
There's a Setup.exe.
If I press the open file button, it will prompt me.
Oh, it's still.
It's still downloading again.
I just ran this.
It will prompt me to install it locally, which I will do as soon as it's installed locally.
You then go to the start menu and you're probably familiar with this, but you bring up a PowerShell
by typing power in here, and as soon as you do, it prompts you and you press open and up comes a PowerShell,
which is where we are going right now to run a large language model.
Minutes into this course and the way we do it is we simply type once.
Once that install is completed and you pressed okay, you can just simply say olama o l l a m a run.
And now you put the name of the model next, and the name of the model we're going to use is going to
be llama 3.2, which is one of the very newest models.
And so here it goes.
And it's run.
It's happening right now.
That blinking cursor means that we have a large language model running on this computer.
It might take a bit longer for you the first time.
It has to download the 2 billion parameters associated with Lama, and depending on your internet connection,
that might take a little bit, but you'll get a progress bar that will show you what's going on.
So what we're now going to do is try and talk to this LLM, and we're going to try and do something
useful with a free LLM.
And here's what we'll do.
I'm actually trying to learn Spanish at the moment.
And I can tell you that my grasp of the Spanish language is distinctly less inferior to my grasp of
llms.
And the app that I'm using to try and teach me has a service where you can pay to chat with a with an
AI tutor.
Now, I don't want to pay for that because I know I can run an open source model and do it for free.
So right now we're going to build ourselves a Spanish tutor which will teach me Spanish.
All open source without paying a penny.
So I'm going to say I am trying to learn Spanish.
I am a complete beginner.
Please chat with me in basic Spanish to teach me.
And so that is the prompt that we will start with.
And let's see what happens.
It's thinking.
Hola.
Welcome to our conversation in Spanish.
To start, let's begin with some basic greetings.
Como estas?
So you can see that it's giving me a very good starting prompt.
It's giving me some examples.
It understands where I'm coming from.
And I can now say something like.
Put an accent on.
Uh, so.
Oh, there's a small mistake.
Okay, there we go.
It's correcting me.
I think it's the accent that I missed and maybe the question mark as well, so you get the idea.
We've actually just built something which has commercial value because indeed, this is sold at a price
immediately for free on our boxes using an open source LLM that is llms in action from the get go.
And now for Mac people, let's flip over to a mac.