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like 4

so this point one i just guessed it so one question is how do you determine

of chat GPT or maybe it is already done uh for your language you can for example

Twilio does have one of those but if you were looking at one of these that didn't,

plus 3 and so it's just basically it continues the generation in all the

There are people, you might not think about this, but there are people who are going to use your application

are the differences pret is uh just like preprocessing you're just um using like

beginning it's all zero then I used masked fill so what this is doing

Discord Community to actually have um a closer uh relationship with the

we're making a new line available here. And also you'll see that there's

run so we see that this runs and uh this currently looks kind of spous but uh

and we think that this is probably her. I'm gonna see, I'm gonna grab this really quick,

We did that, we did that in our API and then we are going to create a web-based API

stream. columns and then with voice recording column we can now copy over

One thing I want you to gain from that exercise though is that you now have the ability to identify whether or not

and ydev to evaluate the loss okay

Random offsets so because this is four we are ex is going to be a uh four

array y which we which we created during the

and therefore this is a no. But let's keep going through the rest of these,

be used for language modeling now the reason that the original paper had an

so with that response, the browser renders the page. When one of those links are clicked,

So while this is an asynchronous function, I can actually make it look more like it is in line

just saw that the voice recording actually is a dictionary and therefore

you'll be able to discuss what an API does. You'll understand why they exist and you'll be able

way in the loss we expect to get something around what we had originally

context size of 512 tokens or maybe you're going to use another embedding

you said I didn't need to know how to code. If you already know how to make a website,

look like that and so you're starting to slowly align it so it's going to expect

the actual script so we can do that by doing python main.py press enter and

here okay so basically we need to know what

So this is one of those numbers that you verified right when you created your account,

we're thinking that the um constraint to better performance right

manually as well there we go so yeah

going to store how we are going to chain the the

messages is not empty in the session State we don't have do messages it's

so what we'd like to do now is just as in the previous video we'd like to index

Define an index tracker beforehand and in the first run the index tracker is

and if you have enough GPU you can even make it like 5,000 50,000 even you can

but you can make, for this collection that we built, we can actually do the settings for the collection.

and that's um you might imagine that's very crowded that's a lot of points for

notes such that all the edges go only one way from left to right

the structure of this a little bit. So we'll say client messages

that's what we know and now we're interested in dd by dc

we are trying to do is x1 w1 plus x2 w2 plus b

a one sentence to explain it so think about in this NLP there are many many

they make their way into the new versions of the interface. Still abstracting away things for us,

now just as in the previous video we want to take these probabilities we want

dictionaries with additional keyword arguments the content example type and

that you have our documents added so now let's hop over to the app.py and test it

now we have to be careful because there's a times out.grad

Just call the API method and voila. You could probably create the uppercase string all

to be public 'cause we actually want anybody to be able to see it, not just Twilio.

each entry in this two-dimensional array will tell us how often that first

open up chat gbt is because we're going to ask it to write us some sentences

and x2 times w2 and then we need to add bias on top of

the input now is 100 and the output number of neurons will be

It's pretty cool right? Here we go. Message dot delete is not a function.

embedded into a thirty dimensional space you can think of it that way so we have

made it a bit more respectable so here's our data set here's all the parameters

do usually um is uh I have an estimate loss function and the estimate loss

you see that the output will become five by one because these 27

uh this is how you print a number of parameters I printed it and it's about

is going to predict that g is likely to come next in the sequence and it's going

to make that magic happen and that processing is happening elsewhere, "en la Nube."

this will now become meaningless because we've reinitialized these so

you might want to also add a couple of print statements to confirm where we are

don't need to create a new session and can save it under the same session I

Oh, I cannot tell you how good it feels as a developer to end up in a place where it feels

So I'm gonna click this and now note I'm inside of a web browser.

label and we will change how we define o

All right, so I got all sorted out. So I sent my email to the compliance folks

So again, I got to that, oops, accidentally pasted my Auth token in the wrong line there.

sensitivity does it respond what is the slope at that point does the function go

positioned in the space and that's why we need to encode them positionally and

hopefully what we're getting now is a tiny bit lower than 4.84

keep them as true we've went over this and this is how we

like for example if we just test a single word andre and

a t one one unified T5 encoder and the T5 decoder model

at one which is the integer at that location is indeed equal to this

and now I know that you can explore them with Curl. For now though, let's take things another step deeper.

model so I'm importing the pytorch um NN module uh for

so somehow we need to concatenate these inputs here together so that we can do

So let's look at that again. So list returned this object, this array of messages

scalar valued auto grant engine so it's working on the you know level of

now telling the exact value is really hard but what is the sign of that slope

large and micrograd is very very simple but if you

here effectively this looks really awkward but changing l

now on the entire data set of text is re-represented as just it's just

pytorch actually requires that we pass in requires grad is true

session State input so here we can Define it if send input not in session

set up your project I'll link a video up here so that you can take a look at that

those words only and then here i am shuffling up all the

on her horse, her magical horse there. So that's pretty cool, right? This is awesome.

So it's relative to that, which means I that if I'm going to display it,

who requests the URL, so let's do that. So I'm gonna go ahead,

new session so our index is new session obviously but if the session key equals

So Fetch returns a promise and it's going to return that response and that response has a method on it

ever search for, the first artist I would search for. And you can see that here it's showing

or 3 204 examples

First, I'd like to take some time upfront here and clearly break down what is meant

of the training set and then the targets here are in the