Hello guys.
So in this video I'm going to talk about the evolution of large language model.
Right now you may be hearing about various LM models that have been created by different different companies.
But to start with, you know, large language models probably initially came up in 1967 called as Eliza.
And this was specifically used to solve some of the LM, uh, some of the NLP use cases.
Then in 1970, we had this another model, then Then in 1918, we had this Excalibur.
And in 1988, from here, the advancement started happening, okay, which is with respect to the simple
RNN.
Then in 1997 you had this LSTM.
And if you don't know, in 2014 we had the GRU RNN, right.
So in the LSTM RNN here you could see that we had a feature that was added with respect to long term
memory and short term memory.
And later on in 2017, this was the amazing breakthrough wherein an attention mechanism was basically
added in this architecture, which is called as transformers.
So after coming into transformer in the next year itself, we got Bert and from this we got this amazing
GPT models.
Okay, now, uh, as we go ahead in 2019, you got GPT two, Robert.
Uh, and in 2020, you found out GPT three, right?
And that is where your open AI right now many people are specifically using ChatGPT.
And.
All right.
So OpenAI probably came up with this amazing chat GPT applications by using this GPT three.
Then in 2021, they moved to GPT 3.5.
In 2020, are still.
Google also came up with palm models Instructgpt and ChatGPT.
So these are some fine tuned models itself.
And if I probably consider with respect to 2023, meta also came into picture wherein they brought their
version of llama models.
Then uh, again, OpenAI came up with this GPT four, then palm two was their Bard, was their Dall-E
was there, and many other models were specifically there, like Falcon.
Um, going forward, you know, right now the advancement are still happening because if I probably
consider 2024.
So in 2024, OpenAI has already come up with this amazing model, and this time it has come up with
this multi model.
And the multi model is nothing, but it is GPT four.
Oh okay.
And with this you will be able to perform tasks with respect to both NLP and images.
Okay.
You will be able to even generate the images itself, right?
Um, along with this, uh, Google will not be left behind, right?
So in 2024, Google already they have come up with its Google Gemini Pro right.
Google Gemini Pro model.
So Gemini Pro 1.5 right.
And along with that, Google Gemini Pro 1.5 flash is also there.
Uh, that specific model is there.
And over here also we have some round three variants which you can go ahead and serve with respect to
the Google Gemini models itself, right.
Not only this, uh, in 2024, uh, there are many, many companies who are specifically coming up with
this, right?
Uh, llama three is also there.
Then, uh, you saw about anthropic, right?
With respect to anthropic, you have this cloud cloudy opus, right?
Cloudy opus models.
Right.
And um, uh, along with cloudy, you also have all these models in hugging face also.
So everywhere this is specifically present.
Even in hugging face you'll be able to access this particular model.
And right now, recently I guess uh, Google Gamma two model has also been coming.
And this is an open source model.
Right.
So Google Gamma two.
Um, if you if I probably consider Google Gemini Pro, this is a paid model itself.
And uh, for some for some hits or some, uh, request, this is given as a free.
But if I consider, uh, opening AI, GPT four or these all are paid models specifically, this is an
open source model.
Uh, so this is just to give you an idea over here, like what all models were there and the evolution
of this large language models, uh, it's like coming from 1967 with a lot of research, a lot of development
that is specifically happening now.
What we are going to basically do is that in our next video, we are just going to compare the speed
and accuracy of all this kind of models.
And, uh, it is very much important to probably know, like which model is best and how the model are
specifically doing, uh, in the industry, which people are specifically using it.
Right.
And that is what I will be discussing in the next video.
Thank you.