In this lesson we are going to talk about the foundation LM models.
So Foundation LM models, Foundation LMS and what we call LMS in this program are the same thing are
LMS train with all the internet data.
So this is not 100% accurate.
They are not trained with all the internet data, but almost a we can say they are trained with all
the public internet data.
Some people like Elon Musk say that this is not true, that really llms are trained with private data
as well.
But let's say that we go with the official story, and we assume that Llms are trained with all the
public internet data is a lot of data.
You may have a two different kinds of foundation LM models.
You have the private ones like ChatGPT or anthropic, and you have the open source one like lamassu,
Falcon and Mistral Way.
Will they remain open source?
We don't know.
In the case of llama two, we know that Facebook is behind, but right now they are free to use both
private and open source.
Uh, LM models have advantages and disadvantages and is very important to understand them well in order
to make the right choice when you are building your LM application.
We are going to use one analogy.
During this program, we are going to understand that llms like ChatGPT are like the engine we use for
our LLM applications.
And our LLM applications are going to be like the vehicles and the machines that use that engine, which
is the LLM.
So remember the foundation LLM model is what we have been calling just LLM.
So we will use just LLM during the program.
There is a it is so new what is happening that there are still a little bit there is still a little
bit confusion uh around some names.
So we are going to use LLM when we want to uh refer to foundation LLM models.
Okay.
In the next lesson, we are going to start talking about some very important basic concepts of LMS,
and you will see that LM limitations are in the origins of LM applications.
We will see that in the next lesson.