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Video Transcript
Hey y'all it's Dr. Know It All. I want to take some time today to look at the combination
of Lama 70 billion parameter open source model Lama 3 and GROC which is an incredibly high speed chip
that can produce tokens at over 200 tokens per second. The combination of Lama 3 which is
open source and GROC actually opens up a whole new way of doing chain of thought reasoning.
And so I want to take a look at some experimentation that I've been doing. Big credit to Matthew
you Berman for this, for inspiring this and actually coming up with the questions.
Although I have identified a couple of problems in his questions that may be leading to some
of the incorrect answers.
So Matthew, if you're watching, I'll make a note of those couple of changes as I go along.
So what we see here is the Grock interface.
You just go to Grock.com.
You sign in with whatever your Google account or whatever you want.
Super, super simple.
Doesn't cost a penny, which is just amazing.
So I love you guys at Grock.
Thank you so much.
And then up here at the top, you can pick what model you want to use.
you can use Mixed Drill, you can use Gemma, you can use the old Lama II, you can use the 8 billion
parameter Lama III. I'm going to use the 70 billion parameter model to give it the best possible
run at this because that's the best model that's out currently that's open source. So what I'm
going to do is ask at one logic puzzle and two math questions, all of which Matthew uses on his
channel to test out these large language models, large multimodal models, as I call them nowadays,
because large language doesn't make any sense. They are multimodal pretty much all of them.
But anyway, what he does is he asks them the question and then he looks at the response and gives it a passer fail
But what Grock actually enables because it is so fast what I've done is I've changed it to producing 10 answers rather than one
And then having the model go back and look at its own answers and determine which one it thinks is the best
And I think this is a sort of a way of allowing the model to do self-reflection
This isn't a very realistic possibility with something like open AI's chat GPT because it's just too slow
But you'll see here in a second that Grock is super fast and so it's able to produce 10 answers and
Select from those answers really really quickly and that makes all the difference in terms of being able to do chain of thought reasoning
A lot of people have questioned the use case for Grock because they've said look
It just produces these things super super fast. Why do we need that? Well, this is one potential reason for it
So first things first the question
Assume the laws of physics on earth a small marble is put into a normal cup and the cup is placed upside down on a table
Someone then takes the cup without changing its orientation and puts it inside the microwave.
Where is the marble now? Explain your reasoning step by step.
So basically you're taking a marble and the cup and you're putting it like this and putting it on a table.
And when you pick up the cup, obviously the marble is going to stay there.
But this really trips up these large multimodal models.
It gives them a lot of trouble because I think the orientation thing and just the fact that they're not embodied means that they don't really understand physics as well as a human being does.
And then my addition to the test is please give me 10 answers to this question explaining your reasoning for each answer
Each answer can be the same and by the way, I needed to add this because I noted that these models were trying to do different answers for each one
And so I think it was like trying to give me 10 completely different answers
And I was like no, no, it can be the same right if you if you figure out what the right answer is just go ahead and do it
Anyway, each answer could be the same
There is no need for each answer to be different though the answers can be different as well
Well, please use these 10 answers as a sort of scratch pad and try different ways of reasoning
about the problem.
Once you have written out the 10 answers, please go back and read over each of the 10 answers
and select the best answer from what you created and please identify the number of the answer.
