CMU Advanced NLP 2024 (15) A Tour of Modern Large Language Models/transcript.vtt

WEBVTT

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can everyone hear Al set okay great so

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um today I'll be talking about a tour of

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modern uh

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llms and basically the idea here is that

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there is many many large language models

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available nowadays but I wanted to go

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through some of the ones that are

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particularly interesting for various

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reasons either because they disclose a

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lot of

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information uh you know about exactly

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how they were trains so we can get an

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idea about what is involved in training

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uh a kind of state-ofthe-art large

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language model or because they're kind

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of the strongest models that you can

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download and use on your own um like the

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best open weights language models that

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are available or because they're

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specialized to some particular topic or

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because they're the best closed uh

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language models but I'm going to

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particularly focus on the first two um

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just so like everybody has an idea about

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you know what what is going into all the

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models that you're using for whatever uh

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you know tasks that you're trying to

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solve so one important thing is uh what

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makes a model so we talk about you know

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like llama 2 or M roll or mix roll or

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whatever else and I think you know this

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already but it's worth reiterating again

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here because I'm going to talk about it

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a lot today but it's basically the model

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architecture so what architecture do you

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decide to use

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um what data do you decide to use and

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what training algorithm or Training

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Method do you decide to use and all of

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these are important um and there was

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actually uh a Twitter thread with Tom

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Wolf who's I guess CSO or CTO or

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something like that at hugging face um

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and basically what he was saying is uh a

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lot of people don't realize that the

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data is actually one of the most

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important parts

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um and the architectures are a lot less

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important nowadays and I think that

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there's some truth to that there's also

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some you know a counterargument to that

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uh the truth to that which you'll see

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today is that almost all of the models

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that we're using use very similar

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architectures like almost all of the

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models use an architecture that's very

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similar Dilma um but despite the fact

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that they use very similar architectures

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they're um accuracy is vastly different

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or their their abilities are vastly

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different so that must come from the

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data or the training decisions right so

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that's an argument for the fact that

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architecture decisions are a lot less

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important my counterargument to that is

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we spent N9 to 10 years fine-tuning and

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finding the Llama architecture so now we

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have the Llama architecture which is a

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really good architecture it works really

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well when training very large models on

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lots of data and so now we don't need to

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use another architecture because the

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architecture using is good but if we

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were trying to do the same thing with

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the like lstm from 2014 uh then none of

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the stuff we're doing today would work

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so that's an argument in favor of you

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know architectures being also

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architectures can make things faster and

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that's included in s decisions

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that

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so um the first thing I'd like to talk

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about before I get into any of the

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actual details is um open versus closed

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access uh this is not like modeling

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stuff but I think it's important and

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also helps you understand the

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environment a little bit so um there's a

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nice blog by pyang and others uh at

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which is also in the reference and they

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discuss several different varieties of

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like openness of release of language

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models in advanced AI systems and there

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are some things that we can talk about

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we can talk about the weights being open

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um described or closed inference uh code

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being open or inference methods being

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described or it being fully closed

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training being open described or closed

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and data being open described or closed

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and um in general uh we have like the

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open weights models that are on hugging

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face that might just mean the weights

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are open the inference code also needs

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to be open because otherwise you can't

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do inference on them if they're on

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hugging face but that doesn't mean that

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the training code is open it also

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doesn't mean that the data is open um

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and so there's various degrees of

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openness

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um and then of course there are things

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like uh GPT for or GPT models where

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basically all of this is closed and we

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don't know anything about it or know

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very little about it another thing is

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about licenses and

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permissiveness and this is kind of

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important if you want to do a research

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project to know because

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it means it it an impact on the things

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that you legally can do or can't do in

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universities I mean we should be

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following the law but we're maybe people

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think about this a little bit less if

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you're in a big company this is

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something that becomes really important

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so it's uh it's important to think

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about so I'm going to go through several

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degrees of licenses uh that if you've

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done anything in open source you

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probably know but um the or you probably

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know a lot of these the first one is

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public domain or cc0

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and this basically means you can do

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anything with it like I could I could

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download it and um this includes the

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download it and redistribute it not give

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you any credit uh modify it in any way I

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want and this includes things like old

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copyrighted works and products of the US

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government workers so if you work for

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the US government in some capacities

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anything you generate becomes public

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domain um so old copyrighted Works um

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How how old do you think they need to be

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before they become uh

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uncopyrighted

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yeah uh I think that's pretty close

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so it's uh 70 years I

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guess oh sorry the life of the author

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plus an additional 70 years so like

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after the after the person has passed

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away 70 years I guess it says um does

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anyone know a work that just become

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became non-copyrighted yeah uh Mickey

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Mouse is still copyrighted

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yeah SBO uh did did it I okay so that

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that's some new news some other new news

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is wi the Poo um so Winnie the Poo just

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became non-copyrighted and actually I

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just heard uh last week that somebody

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made a horror movie where Winnie the

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Pooh was a a killer and that one uh a

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whole bunch of like bad movie awards in

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2023 so um that's the kind of things

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that can happen to your copyrighted

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works if they are released cc0 somebody

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can do anything they want with them uh

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you know so you need to be a little bit

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careful about

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that um next are MIT and bstd these are

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very common software licenses you'll see

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them on a lot of research projects these

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have very few restrictions um other than

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maybe maintaining the copyright notice

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for BC but that's about it you can do

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just about anything you want with it um

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actually I'm not sure if people know

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this but the Mac operating system is

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based on an old BSD Opera uh operating

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system where they uh took the they took

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the code they made it private they

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forked it made it private and now it's

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the proprietary Mac operating system so

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uh that's something you can do with an m

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m or BSD

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licensed um there's also a Pachi and CC

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by um

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here you must acknowledge the owner of

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the uh the original creators so you need

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to say this person actually created uh

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this

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originally

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um Apachi is also kind of interesting

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because they will give you a license to

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use that code and any patents that are

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associated with that code unless you sue

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the company who released it so um just

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Give an example let's say uh Google

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released their code under the Apache

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license and that code implements

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Transformers and Google has a patent on

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Transformers so if you use uh kind of

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jacks or tensorflow a Jack or tensorflow

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implementation of Transformers uh that

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was created by Google you're okay you're

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safe to use that because they've

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released it under uh under that license

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but if you sue Google uh for anything

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related to intellectual property Google

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could say uh don't you can't use

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Transformers anymore um and so like if

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open AI ever sues Google for

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intellectual property infringement

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Google will say okay you can't use

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Transformers or word embeddings good

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luck uh open so um there's this

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interesting thing where all of these uh

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tech companies now are using patented um

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patented things a lot of it apachi

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license software and so none of them can

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sue each other for patents so patents

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have become basically mostly worthless

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uh in big

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te um moving on um there's also a g GPL

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in

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ccbsa these are licenses where if you

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use them you need to reshare under that

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license um and so like if you create

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some software it's GPL licensed and you

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build on it and build something new you

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need to release it under the GPL license

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so a lot of companies will not

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use um will not use GPL software because

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that would mean that if they incorporate

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into their system their whole system

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like for example Google uh like all of

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Google would have to be GPL licensed in

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Rel EAS uh so um and I'm kind of

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simplifying these licenses I'm just

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giving you the gist CC BSA and sorry CC

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licenses are more for data so MIT BSC

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Apachi and GPL are more for software CC

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Creative Commons licenses are for data

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so um for example Wikipedia is CC by SAA

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I believe

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let me make sure that I'm not lying

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there yeah CC bys and so that means that

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if you make any derivative work of

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Wikipedia you need to share it um the

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same way that Wikipedia is uh so you

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need to give it the same

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license there's also um cre of Commons

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non-commercial licenses or software

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non-commercial licenses you say you

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can't use them for commercial purposes

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all the ones above you can use for

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commercial purposes once you start

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getting down here this is no often no

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longer called open source so the open

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source initiative says anything with a

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restriction on the way that you can use

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it is no longer open source and so that

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means if you say you can't use this for

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commercial purposes or you can't use

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this in military systems for example

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which some language models say that

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nowadays those are no longer called open

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source according to the open source

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initiative so that's a thing to know

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about then separately uh there are these

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licenses that a lot of people like meta

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or hugging face come up with for their

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um for their models recently so the

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Llama license um how many people are

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using

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llama in your projects how many people

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read the

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license so um are you sure you can use

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it in your

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project uh so you're you're probably in

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luck in your project if you're using it

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the Lama license you can read into it to

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see what it actually allows but it has

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um the original llama license has some

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interesting uh things number one you

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cannot use llama to train any language

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model that is not derived from llama so

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you can't generate data from llama in

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train M that's not allowed according to

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the r Li um another thing is uh you

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can't use it for military purposes so

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you can't use it um in building a

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missile system or something like that

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hopefully none of you are doing that for

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your project um and you also need to get

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a license from meta if you have

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something more than 300 million active

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user asign your social network service

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so if you're Google or um you know X or

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Twitter or you know whatever else you

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need to get a license for meta before

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you can start using one so

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basically they created that license so

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their competitors don't take their

00:13:03.240 --> 00:13:08.959
language model and just use it for free

00:13:06.720 --> 00:13:11.000
um and then the final thing is no

00:13:08.959 --> 00:13:13.240
license so like let's say you have some

00:13:11.000 --> 00:13:15.560
code that you upload to GitHub and you

00:13:13.240 --> 00:13:17.839
don't put a license on your code this

00:13:15.560 --> 00:13:20.880
means that you have only agreed to the

00:13:17.839 --> 00:13:23.360
GitHub licensing terms which means that

00:13:20.880 --> 00:13:26.199
actually nobody can use their code they

00:13:23.360 --> 00:13:30.079
can view it possibly but they can't you

00:13:26.199 --> 00:13:31.720
download it use it they can't like um

00:13:30.079 --> 00:13:34.160
they can't incorporate it into their own

00:13:31.720 --> 00:13:36.000
system so actually if you release

00:13:34.160 --> 00:13:39.120
research code I would highly encourage

00:13:36.000 --> 00:13:41.120
you to use MIT or BSD um or one of these

00:13:39.120 --> 00:13:43.040
permissive licenses so other people can

00:13:41.120 --> 00:13:45.720
use it and follow up and your code can

00:13:43.040 --> 00:13:46.920
be effectful so um this is an important

00:13:45.720 --> 00:13:49.040
thing to know about there's obviously

00:13:46.920 --> 00:13:52.959
lots more to know

00:13:49.040 --> 00:13:56.440
about um so then my question my next

00:13:52.959 --> 00:13:57.360
question is uh what is most of the text

00:13:56.440 --> 00:13:59.560
on the

00:13:57.360 --> 00:14:01.160
internet the majority of the text on the

00:13:59.560 --> 00:14:04.839
internet falls into one of these

00:14:01.160 --> 00:14:04.839
categories any idea which

00:14:05.120 --> 00:14:12.759
one so Wikipedia is CC bya what what

00:14:09.040 --> 00:14:12.759
about uh Mo most of the text

00:14:14.199 --> 00:14:18.959
on yeah it's not maybe not no license

00:14:16.880 --> 00:14:21.680
but all rights reserved so basically you

00:14:18.959 --> 00:14:23.079
can't use it without having permission

00:14:21.680 --> 00:14:27.639
from the copyright

00:14:23.079 --> 00:14:30.639
holders and so because of that

00:14:27.639 --> 00:14:33.800
um the idea of fair use becomes very

00:14:30.639 --> 00:14:35.320
important this is a us specific thing

00:14:33.800 --> 00:14:36.880
and the rules in other countries are

00:14:35.320 --> 00:14:39.199
different they're not the same as the us

00:14:36.880 --> 00:14:41.680
but in the US uh we have rules about

00:14:39.199 --> 00:14:44.600
where you can use particular types of

00:14:41.680 --> 00:14:46.279
data so the US fair use Doctrine is

00:14:44.600 --> 00:14:50.240
basically that you can use copyrighted

00:14:46.279 --> 00:14:52.920
material in some cases so

00:14:50.240 --> 00:14:56.279
um as a gross

00:14:52.920 --> 00:15:01.800
simplification um quoting a small amount

00:14:56.279 --> 00:15:04.320
of material in like a textbook or slides

00:15:01.800 --> 00:15:07.079
or something like this this is likely

00:15:04.320 --> 00:15:10.040
okay um there are going to be very few

00:15:07.079 --> 00:15:11.399
cases where this is not going to um you

00:15:10.040 --> 00:15:12.720
know where you're going to get in

00:15:11.399 --> 00:15:15.600
trouble for

00:15:12.720 --> 00:15:18.000
this another important uh judgment

00:15:15.600 --> 00:15:19.600
criteria for whether this is fair use is

00:15:18.000 --> 00:15:22.440
that it doesn't diminish the value of

00:15:19.600 --> 00:15:25.120
the original work so if I quote

00:15:22.440 --> 00:15:27.759
something in my like let's say I quoted

00:15:25.120 --> 00:15:30.839
all of Harry Potter in a textbook and

00:15:27.759 --> 00:15:32.600
then I sold my textbook for $3 anybody

00:15:30.839 --> 00:15:34.279
could take my textbook and read all of

00:15:32.600 --> 00:15:35.800
Harry Potter for $3 and the money

00:15:34.279 --> 00:15:37.480
wouldn't go to JK rolling and that would

00:15:35.800 --> 00:15:41.040
not be fair use because it's diminishing

00:15:37.480 --> 00:15:42.920
the value of similarly if I create a big

00:15:41.040 --> 00:15:44.319
Corpus of books and I upload them to a

00:15:42.920 --> 00:15:46.079
site where anyone can browse them that

00:15:44.319 --> 00:15:48.319
would also probably not be for use

00:15:46.079 --> 00:15:49.160
because the authors would not get paid

00:15:48.319 --> 00:15:52.319
for

00:15:49.160 --> 00:15:54.480
it another judgment Criterion is whether

00:15:52.319 --> 00:15:57.399
it's for non commercial purposes or not

00:15:54.480 --> 00:15:59.639
so like in universities we're actually

00:15:57.399 --> 00:16:01.120
held to a probably held to a more

00:15:59.639 --> 00:16:03.000
lenient standard of fa use if we're

00:16:01.120 --> 00:16:06.120
doing non-commercial research compared

00:16:03.000 --> 00:16:08.519
to a company that's doing it

00:16:06.120 --> 00:16:11.480
so um most data on the Internet is

00:16:08.519 --> 00:16:13.279
copyrighted so right now most model

00:16:11.480 --> 00:16:16.240
training not all model training but most

00:16:13.279 --> 00:16:18.680
model training is done um assuming fair

00:16:16.240 --> 00:16:21.800
use which means that training an AI

00:16:18.680 --> 00:16:25.800
model on copyrighted

00:16:21.800 --> 00:16:29.480
data is number one it cannot reproduce

00:16:25.800 --> 00:16:32.240
the material easily so it's instead of

00:16:29.480 --> 00:16:33.600
quoting material directly it's kind of

00:16:32.240 --> 00:16:35.880
combining the material together to

00:16:33.600 --> 00:16:37.519
create a new thing they're saying it

00:16:35.880 --> 00:16:40.639
doesn't diminish the commercial value of

00:16:37.519 --> 00:16:42.360
the original uh data um and then the

00:16:40.639 --> 00:16:44.839
non-commercial purposes is maybe a

00:16:42.360 --> 00:16:47.240
secondary concern since the first two

00:16:44.839 --> 00:16:50.600
hold um but there are lawsuits about

00:16:47.240 --> 00:16:52.360
this and so um this is a clip from The

00:16:50.600 --> 00:16:55.560
New York Times where the New York Times

00:16:52.360 --> 00:16:58.279
is suing open AI in Microsoft over uh

00:16:55.560 --> 00:16:59.759
them training on New York Times articles

00:16:58.279 --> 00:17:02.040
and they did do a lot of things like

00:16:59.759 --> 00:17:05.799
they demonstrate that you can get uh gp4

00:17:02.040 --> 00:17:08.319
to reproduce uh like um New York Times

00:17:05.799 --> 00:17:11.480
articles and they also argue that people

00:17:08.319 --> 00:17:12.880
are using this gp4 as a source of news

00:17:11.480 --> 00:17:14.079
instead of going to the New York Times

00:17:12.880 --> 00:17:15.959
site so they're losing money from

00:17:14.079 --> 00:17:19.199
advertising and like other other things

00:17:15.959 --> 00:17:21.679
like that um another example is GitHub

00:17:19.199 --> 00:17:24.000
co-pilot was sued by people who uh

00:17:21.679 --> 00:17:26.439
uploaded software to GitHub and said

00:17:24.000 --> 00:17:29.039
that uh basically GitHub didn't have the

00:17:26.439 --> 00:17:32.400
right to use it to profit from it and

00:17:29.039 --> 00:17:34.799
diminish their uh you know their money

00:17:32.400 --> 00:17:37.520
so notably uh on this slide I'm using

00:17:34.799 --> 00:17:42.039
fair use I don't know if you've noticed

00:17:37.520 --> 00:17:44.679
like I copy I copy pasted an image from

00:17:42.039 --> 00:17:46.360
somebody's uh you know website and used

00:17:44.679 --> 00:17:48.520
it here that's copyrighted material but

00:17:46.360 --> 00:17:49.640
I'm using it because I'm quoting a small

00:17:48.520 --> 00:17:52.440
amount of material and I'm not

00:17:49.640 --> 00:17:54.360
diminishing the ostial values so um like

00:17:52.440 --> 00:17:56.320
fair use is very ubiquitous it's very

00:17:54.360 --> 00:17:58.480
important so we can do things like this

00:17:56.320 --> 00:18:00.840
but also um it's currently under thep

00:17:58.480 --> 00:18:00.840
with this

00:18:01.280 --> 00:18:07.799
models so then another question is why

00:18:04.360 --> 00:18:12.520
restrict model access why do we number

00:18:07.799 --> 00:18:14.320
one make models closed number two um you

00:18:12.520 --> 00:18:16.159
know maybe not even describe what we did

00:18:14.320 --> 00:18:18.880
in our models and I think there's three

00:18:16.159 --> 00:18:21.360
main reasons the first reason is

00:18:18.880 --> 00:18:23.480
commercial concerns and so they want to

00:18:21.360 --> 00:18:25.760
make money from the models so open AI

00:18:23.480 --> 00:18:27.520
makes money from the open AI API Gemini

00:18:25.760 --> 00:18:29.480
makes uh sorry Google makes money from

00:18:27.520 --> 00:18:31.799
the Gemini API

00:18:29.480 --> 00:18:33.720
um and anthropic makes money from the

00:18:31.799 --> 00:18:34.760
CLA API these are all models that I'm

00:18:33.720 --> 00:18:37.640
going to talk

00:18:34.760 --> 00:18:39.440
about number two safety I I think there

00:18:37.640 --> 00:18:41.640
are very legitimate concerns where if

00:18:39.440 --> 00:18:43.840
you release strong models people might

00:18:41.640 --> 00:18:47.200
use them for bad things so you know

00:18:43.840 --> 00:18:49.120
creating fake content online or uh doing

00:18:47.200 --> 00:18:50.720
spear fishing attacks against people and

00:18:49.120 --> 00:18:52.600
trying to you know scam them out of

00:18:50.720 --> 00:18:55.600
money or things like that so I think

00:18:52.600 --> 00:18:57.240
there are legitimate concerns about this

00:18:55.600 --> 00:18:58.880
and then the final one is legal

00:18:57.240 --> 00:19:01.520
liability so training models on

00:18:58.880 --> 00:19:03.640
copyrighted data is a legal gray area as

00:19:01.520 --> 00:19:05.159
I just mentioned so they don't want to

00:19:03.640 --> 00:19:07.159
say what data they trained on because if

00:19:05.159 --> 00:19:10.240
they say what data they trained on then

00:19:07.159 --> 00:19:11.960
they might get sued so these are the

00:19:10.240 --> 00:19:14.960
three main

00:19:11.960 --> 00:19:17.960
concerns so

00:19:14.960 --> 00:19:19.480
um anyway this this is a preface and

00:19:17.960 --> 00:19:23.360
then I want to go into like the actual

00:19:19.480 --> 00:19:23.360
models but are there any questions about

00:19:24.679 --> 00:19:30.280
this so if any of you

00:19:27.280 --> 00:19:31.720
are working at a company or starting a

00:19:30.280 --> 00:19:33.120
company thinking about working at a

00:19:31.720 --> 00:19:35.440
company or starting a company this is

00:19:33.120 --> 00:19:37.320
something you should be aware of um you

00:19:35.440 --> 00:19:39.720
should also be aware of the fact that

00:19:37.320 --> 00:19:42.360
you know open AI has been doing sketchy

00:19:39.720 --> 00:19:46.640
things for a long time and look where

00:19:42.360 --> 00:19:48.440
they are so you know it it's uh like

00:19:46.640 --> 00:19:51.400
this is very much a legal gray area and

00:19:48.440 --> 00:19:53.880
people are are uh moving through that

00:19:51.400 --> 00:19:55.640
gray area but anyway it's worth knowing

00:19:53.880 --> 00:19:59.480
that so next I'm going to talk about

00:19:55.640 --> 00:20:00.679
open models um so first bird's eye view

00:19:59.480 --> 00:20:02.600
I'm going to talk about five different

00:20:00.679 --> 00:20:04.080
models and I picked them for a reason

00:20:02.600 --> 00:20:06.440
the first two are because they're open

00:20:04.080 --> 00:20:08.159
source and fully reproducible namely

00:20:06.440 --> 00:20:10.360
pipia

00:20:08.159 --> 00:20:11.919
Ino and the reason why I want to talk

00:20:10.360 --> 00:20:13.120
about these is we know everything about

00:20:11.919 --> 00:20:14.679
them including what data they were

00:20:13.120 --> 00:20:16.799
trained on um what their training

00:20:14.679 --> 00:20:19.080
procedures are you can download all the

00:20:16.799 --> 00:20:21.000
the stuff so you can kind of know uh

00:20:19.080 --> 00:20:24.840
exactly what goes into making a strong

00:20:21.000 --> 00:20:26.520
model um Pia is uh actually has many

00:20:24.840 --> 00:20:28.159
sizes in checkpoints which is pretty

00:20:26.520 --> 00:20:30.919
interesting Ando is maybe the strongest

00:20:28.159 --> 00:20:32.559
reproduced model at the moment um then

00:20:30.919 --> 00:20:34.120
we have open weights models and these

00:20:32.559 --> 00:20:35.520
are models that aren't fully open they

00:20:34.120 --> 00:20:38.679
don't disclose everything they don't

00:20:35.520 --> 00:20:40.760
release their training data uh or

00:20:38.679 --> 00:20:43.799
code um but I'm going to talk about

00:20:40.760 --> 00:20:46.520
llama 2 which is the most popular um

00:20:43.799 --> 00:20:48.280
it's also heavily safety tuned mistol

00:20:46.520 --> 00:20:50.840
and mixol which is a strong and fast

00:20:48.280 --> 00:20:53.200
model um it's somewhat multilingual and

00:20:50.840 --> 00:20:55.200
also quen which is a very uh strong

00:20:53.200 --> 00:20:57.520
model it's more multilingual and

00:20:55.200 --> 00:21:00.600
specifically it's good in English and

00:20:57.520 --> 00:21:03.440
Chinese because it was train down of

00:21:00.600 --> 00:21:04.720
that so first going into Pia for each of

00:21:03.440 --> 00:21:06.159
them I'm going to give an overview and

00:21:04.720 --> 00:21:08.880
then talk about some interesting points

00:21:06.159 --> 00:21:12.320
about them so pythia was created by

00:21:08.880 --> 00:21:14.799
alther ai alther ai is one of the first

00:21:12.320 --> 00:21:16.279
um kind of open- source AI organizations

00:21:14.799 --> 00:21:18.720
they've created a huge number of really

00:21:16.279 --> 00:21:21.480
useful things including training code

00:21:18.720 --> 00:21:25.279
models training data sets and also

00:21:21.480 --> 00:21:28.080
evaluation that's used pretty widely um

00:21:25.279 --> 00:21:29.760
the goal of pythia was basically joint

00:21:28.080 --> 00:21:32.159
understanding model training Dynamics

00:21:29.760 --> 00:21:36.320
and scaling and so from that point of

00:21:32.159 --> 00:21:39.120
view um they released eight model sizes

00:21:36.320 --> 00:21:41.880
from 70 million parameters to 12 billion

00:21:39.120 --> 00:21:44.960
parameters for each model size they have

00:21:41.880 --> 00:21:47.440
154 checkpoints throughout the training

00:21:44.960 --> 00:21:52.880
process um so they basically trained on

00:21:47.440 --> 00:21:55.960
uh 3300 billion uh parameter uh tokens

00:21:52.880 --> 00:21:57.400
and uh did checkpoints you know

00:21:55.960 --> 00:21:59.000
periodically during that training

00:21:57.400 --> 00:22:02.400
process so you can do interest things

00:21:59.000 --> 00:22:04.400
like say uh how quickly do small models

00:22:02.400 --> 00:22:06.919
learn things how quickly do large models

00:22:04.400 --> 00:22:09.480
learn things and other stuff like

00:22:06.919 --> 00:22:10.760
that in terms of the architecture as I

00:22:09.480 --> 00:22:12.760
mentioned at the very beginning the

00:22:10.760 --> 00:22:14.799
architectures are actually very similar

00:22:12.760 --> 00:22:17.840
between them so it's almost easier to

00:22:14.799 --> 00:22:21.080
point out their differences than uh

00:22:17.840 --> 00:22:22.559
their like their similarities um

00:22:21.080 --> 00:22:25.400
actually one thing that's not on the

00:22:22.559 --> 00:22:27.159
slide is um I mainly focused on the

00:22:25.400 --> 00:22:29.080
seven billion models because almost

00:22:27.159 --> 00:22:30.320
everybody trains a seven billi model

00:22:29.080 --> 00:22:32.720
it's just kind of like one of the

00:22:30.320 --> 00:22:34.640
standard sizes it's the smallest size of

00:22:32.720 --> 00:22:36.559
llama it's one of the largest it's the

00:22:34.640 --> 00:22:40.240
largest size ofo and one of the largest

00:22:36.559 --> 00:22:46.880
sizes of pipon 7 billion models are

00:22:40.240 --> 00:22:52.880
generally um 4096 wide 32 uh

00:22:46.880 --> 00:22:52.880
deep uh 32 attention heads and they're

00:22:54.200 --> 00:23:01.159
um and their um hidden layer size is

00:22:57.400 --> 00:23:04.400
about like eight3 of the size of this

00:23:01.159 --> 00:23:07.360
and this is kind of a standard llama 7B

00:23:04.400 --> 00:23:09.240
architecture um as you scale up to

00:23:07.360 --> 00:23:11.520
larger sizes you just increase the

00:23:09.240 --> 00:23:13.880
number of layers you increase the the

00:23:11.520 --> 00:23:16.080
width and other things like that so

00:23:13.880 --> 00:23:19.039
that's very standard um the other

00:23:16.080 --> 00:23:21.320
standard is everybody uses a Transformer

00:23:19.039 --> 00:23:24.440
um everybody uses pre-layer Norm like I

00:23:21.320 --> 00:23:27.120
talked about before everybody uses rope

00:23:24.440 --> 00:23:29.520
eddings um almost everybody uses a swig

00:23:27.120 --> 00:23:30.919
glue activation so this is just kind of

00:23:29.520 --> 00:23:31.880
the standard recipe that almost

00:23:30.919 --> 00:23:35.120
everybody

00:23:31.880 --> 00:23:37.000
uses um where things start to change a

00:23:35.120 --> 00:23:38.559
little bit between the architectures

00:23:37.000 --> 00:23:40.559
which arguably might not be very

00:23:38.559 --> 00:23:44.679
important is how long is the context

00:23:40.559 --> 00:23:48.320
length so um pythia is 2K context

00:23:44.679 --> 00:23:51.360
compared to llama llama 2's 4K context

00:23:48.320 --> 00:23:55.000
um actually llama 1 is 1K context so

00:23:51.360 --> 00:24:00.000
Llama Llama Or sorry llama one is 2K

00:23:55.000 --> 00:24:02.120
context and llama 2 is 4K context um

00:24:00.000 --> 00:24:03.880
another thing is where do they put

00:24:02.120 --> 00:24:06.240
biases in the model most people don't

00:24:03.880 --> 00:24:08.200
use biases uh anywhere but sometimes

00:24:06.240 --> 00:24:09.840
they put them in various places the

00:24:08.200 --> 00:24:11.919
other thing is a variety of layer Norm

00:24:09.840 --> 00:24:13.559
that people use and Pia was using

00:24:11.919 --> 00:24:16.240
standard parametric layer Norm but

00:24:13.559 --> 00:24:18.000
gradually people are stepping back from

00:24:16.240 --> 00:24:21.360
that and they're using like RMS Norm or

00:24:18.000 --> 00:24:22.880
even nonparametric LMS so um small

00:24:21.360 --> 00:24:25.559
architecture differences but almost

00:24:22.880 --> 00:24:29.240
everybody uses something pretty

00:24:25.559 --> 00:24:31.960
similar um the data this was trained on

00:24:29.240 --> 00:24:34.600
300 billion tokens of the pile uh which

00:24:31.960 --> 00:24:37.440
is on the next slide but one interesting

00:24:34.600 --> 00:24:39.000
thing is that they also did a duplicated

00:24:37.440 --> 00:24:43.320
training run on

00:24:39.000 --> 00:24:47.679
270 s billions of the token ah sorry 207

00:24:43.320 --> 00:24:50.559
billion tokens and um the idea is that

00:24:47.679 --> 00:24:53.039
they um they wanted to test how

00:24:50.559 --> 00:24:54.919
important it is to duplicate how much do

00:24:53.039 --> 00:24:56.279
you gain by D duplicating in terms of

00:24:54.919 --> 00:24:59.559
training

00:24:56.279 --> 00:25:01.520
efficiency and um

00:24:59.559 --> 00:25:04.760
they have different learning rates for

00:25:01.520 --> 00:25:08.640
different model sizes the 7B model is uh

00:25:04.760 --> 00:25:11.760
1.2 * e to Theus 4 in contrast llama is

00:25:08.640 --> 00:25:13.120
3 * eus 4 so this is a potentially big

00:25:11.760 --> 00:25:16.840
change because the learning rate is

00:25:13.120 --> 00:25:18.880
actually half the size here um is the

00:25:16.840 --> 00:25:20.559
batch size they use 2 million tokens and

00:25:18.880 --> 00:25:23.600
actually llama 2 uses four million

00:25:20.559 --> 00:25:26.520
tokens for the batch size so um there

00:25:23.600 --> 00:25:29.000
are some small differences

00:25:26.520 --> 00:25:31.480
there so next next I'd like to talk

00:25:29.000 --> 00:25:33.760
about the pile um this is kind of the

00:25:31.480 --> 00:25:36.279
original open data set for training

00:25:33.760 --> 00:25:37.960
large language models um that being said

00:25:36.279 --> 00:25:42.159
it's a really nice data set made out of

00:25:37.960 --> 00:25:47.039
lots of uh different types of data and

00:25:42.159 --> 00:25:49.960
namely it's trained on academic data so

00:25:47.039 --> 00:25:52.559
that includes things like PubMed archive

00:25:49.960 --> 00:25:55.240
free law the US patent office other

00:25:52.559 --> 00:25:57.000
stuff like that it's also trained on

00:25:55.240 --> 00:26:00.080
internet data so this is data that's

00:25:57.000 --> 00:26:02.840
just scraped from parts of the internet

00:26:00.080 --> 00:26:05.799
but also stack Exchange in

00:26:02.840 --> 00:26:09.480
Wikipedia um it also has some pros so

00:26:05.799 --> 00:26:12.200
these are um like book data sets it has

00:26:09.480 --> 00:26:15.640
some code data sets and it has some like

00:26:12.200 --> 00:26:18.799
subtitle dialog data sets in it so this

00:26:15.640 --> 00:26:22.399
overall is 800 gigabytes or about 300

00:26:18.799 --> 00:26:22.399
billion tokens according to

00:26:23.360 --> 00:26:28.080
Tok so some of the findings from the

00:26:25.760 --> 00:26:30.919
pipia paper in addition to just being

00:26:28.080 --> 00:26:33.399
like one of the original strong uh open

00:26:30.919 --> 00:26:36.279
language models is they have some

00:26:33.399 --> 00:26:38.600
interesting analysis into um model

00:26:36.279 --> 00:26:40.960
memorization and how quickly models

00:26:38.600 --> 00:26:44.080
learn uh based on the number of tokens

00:26:40.960 --> 00:26:45.520
that you show them and this graph is

00:26:44.080 --> 00:26:47.520
maybe a little bit hard to see from the

00:26:45.520 --> 00:26:49.440
back so I'll interpret it the left side

00:26:47.520 --> 00:26:50.840
is one of their smaller models 160

00:26:49.440 --> 00:26:54.880
million the right side is their biggest

00:26:50.840 --> 00:26:57.799
Model 12 billion um the different lines

00:26:54.880 --> 00:26:58.840
here are different steps of the training

00:26:57.799 --> 00:27:03.120
process

00:26:58.840 --> 00:27:09.640
so like uh 13,000 steps uh

00:27:03.120 --> 00:27:13.840
30 sorry 39,000 steps and uh etc etc and

00:27:09.640 --> 00:27:18.240
the xaxis here is the frequency of a

00:27:13.840 --> 00:27:21.679
fact in or a frequency of a fact in the

00:27:18.240 --> 00:27:24.640
training data and the y axis is question

00:27:21.679 --> 00:27:29.159
answering accuracy about that fact and

00:27:24.640 --> 00:27:30.919
so what this is basically showing is

00:27:29.159 --> 00:27:35.679
as you scale up the

00:27:30.919 --> 00:27:38.520
model um the larger models learn faster

00:27:35.679 --> 00:27:41.120
um up to a point so like right here you

00:27:38.520 --> 00:27:44.519
see the 2.8 billion model is about the

00:27:41.120 --> 00:27:46.080
same as the 12 billion model at earlier

00:27:44.519 --> 00:27:48.080
parts of the training

00:27:46.080 --> 00:27:51.000
process but as you get later in the

00:27:48.080 --> 00:27:54.200
training process the 12 billion model is

00:27:51.000 --> 00:27:57.279
like memorizing and being able to recall

00:27:54.200 --> 00:27:58.840
more facts uh so like right at the very

00:27:57.279 --> 00:28:02.519
beginning you need to scale up to about

00:27:58.840 --> 00:28:05.840
2.8 billion to learn efficiently uh but

00:28:02.519 --> 00:28:07.799
at the end this model is like better uh

00:28:05.840 --> 00:28:10.399
further on

00:28:07.799 --> 00:28:12.000
so this is really nice all of this all

00:28:10.399 --> 00:28:14.240
of these checkpoints all this data is

00:28:12.000 --> 00:28:15.840
open they even made the data loaders so

00:28:14.240 --> 00:28:17.360
it's reproducible so you can look at the

00:28:15.840 --> 00:28:19.559
actual data that the model was trained

00:28:17.360 --> 00:28:21.000
on um at each of the checkpoints so if

00:28:19.559 --> 00:28:24.320
you want to do this sort of analysis

00:28:21.000 --> 00:28:27.120
this is a good set of um models to look

00:28:24.320 --> 00:28:28.720
at um another thing that they did is

00:28:27.120 --> 00:28:31.120
they actually did interv itions on the

00:28:28.720 --> 00:28:35.640
data so they um tried to intervene on

00:28:31.120 --> 00:28:37.279
the data to modify it because uh male or

00:28:35.640 --> 00:28:38.840
masculine pronouns were much more

00:28:37.279 --> 00:28:42.000
frequent than feminine pronouns in the

00:28:38.840 --> 00:28:43.919
data so they intervened on the data um

00:28:42.000 --> 00:28:45.559
to try to balance out the distribution

00:28:43.919 --> 00:28:48.000
of masculine and feminine pronouns and

00:28:45.559 --> 00:28:49.559
demonstrated that the model became less

00:28:48.000 --> 00:28:52.080
biased towards generating masculine

00:28:49.559 --> 00:28:55.480
pronouns later so they also were able to

00:28:52.080 --> 00:28:55.480
do those sorts of intervention

00:28:55.919 --> 00:29:00.039
studies um any any questions about

00:29:00.519 --> 00:29:07.919
Pia okay um next I want to go too Soo is

00:29:04.720 --> 00:29:10.279
a more recent model um Pia I think came

00:29:07.919 --> 00:29:13.200
came out around a year agoo is very

00:29:10.279 --> 00:29:15.440
recent about a month ago and um this was

00:29:13.200 --> 00:29:18.360
created by ai2 the Ellen Institute for

00:29:15.440 --> 00:29:20.440
AI one thing you'll notice is the two um

00:29:18.360 --> 00:29:22.279
completely open models that I'm talking

00:29:20.440 --> 00:29:24.799
about both came from nonprofit

00:29:22.279 --> 00:29:28.640
organizations um so Al Luther is

00:29:24.799 --> 00:29:30.039
nonprofit uh ai2 is nonprofit so uh

00:29:28.640 --> 00:29:31.519
they're maybe a little bit less worried

00:29:30.039 --> 00:29:34.919
about people trying to sue them for lots

00:29:31.519 --> 00:29:36.720
of money for fair use violations uh so

00:29:34.919 --> 00:29:38.120
uh that's the cynical point of view the

00:29:36.720 --> 00:29:39.679
the non cynical point of view is they

00:29:38.120 --> 00:29:42.279
have nothing to profit by creating a

00:29:39.679 --> 00:29:44.240
better model uh by having other people

00:29:42.279 --> 00:29:47.039
create a better model so um they're

00:29:44.240 --> 00:29:50.840
willing to do this for open uh in good

00:29:47.039 --> 00:29:54.080
science um their goal is better science

00:29:50.840 --> 00:29:55.880
of State ofth art LMS and uh some of the

00:29:54.080 --> 00:29:57.600
unique features are top performance of a

00:29:55.880 --> 00:29:59.840
fully documented model and they also

00:29:57.600 --> 00:30:02.960
have in construction tun models

00:29:59.840 --> 00:30:04.960
Etc looking at the parameters um

00:30:02.960 --> 00:30:06.240
basically similar to llama the one big

00:30:04.960 --> 00:30:08.440
difference is they're using

00:30:06.240 --> 00:30:10.440
non-parametric layer Norm instead of RMS

00:30:08.440 --> 00:30:13.640
Norm so this is basically layer Norm

00:30:10.440 --> 00:30:15.960
with no parameters whatsoever um they

00:30:13.640 --> 00:30:18.880
they didn't super clearly justify why

00:30:15.960 --> 00:30:21.760
they decided to do this one difference

00:30:18.880 --> 00:30:25.519
from Pia uh this was actually trained on

00:30:21.760 --> 00:30:29.559
2.46 trillion tokens uh so compare this

00:30:25.519 --> 00:30:32.600
to uh to Pia which was trained on 300

00:30:29.559 --> 00:30:34.480
billion tokens and so they basically

00:30:32.600 --> 00:30:36.120
trained it for a lot longer they trained

00:30:34.480 --> 00:30:37.960
it on something called the dolma Corpus

00:30:36.120 --> 00:30:41.480
which they also created at

00:30:37.960 --> 00:30:44.279
ai2 um actually I think this might be

00:30:41.480 --> 00:30:47.279
wrong uh so just ignore that that was

00:30:44.279 --> 00:30:49.760
copy paste mistake from typ so um they

00:30:47.279 --> 00:30:52.039
always use 3E to the minus 4 is a

00:30:49.760 --> 00:30:53.679
learning rate which is the same as uh as

00:30:52.039 --> 00:30:56.039
llama and the batch size is 4 million

00:30:53.679 --> 00:30:59.960
tokens which is also the same as

00:30:56.039 --> 00:31:02.000
llama so the domma that they created is

00:30:59.960 --> 00:31:04.320
um actually pretty similar to the pile

00:31:02.000 --> 00:31:07.320
but it's a larger Corpus it's three

00:31:04.320 --> 00:31:09.240
trillion tokens this is also fully open

00:31:07.320 --> 00:31:11.480
so you can download it from hugging face

00:31:09.240 --> 00:31:15.399
uh if you could find some dis to put

00:31:11.480 --> 00:31:19.200
three trillion tokens on um

00:31:15.399 --> 00:31:21.080
so uh another thing is that they have a

00:31:19.200 --> 00:31:23.360
data processing pipeline of language

00:31:21.080 --> 00:31:26.240
filtering quality filtering content

00:31:23.360 --> 00:31:28.399
filtering D duplication uh multisource

00:31:26.240 --> 00:31:31.440
mixing and tokenization

00:31:28.399 --> 00:31:33.279
and so the nice thing about this is a

00:31:31.440 --> 00:31:35.639
lot of this stuff is usually proprietary

00:31:33.279 --> 00:31:38.240
for most language modeling creators so

00:31:35.639 --> 00:31:39.600
if you want to see all of the like data

00:31:38.240 --> 00:31:41.039
processing pipeline that goes into

00:31:39.600 --> 00:31:42.799
training a model this is a pretty good

00:31:41.039 --> 00:31:45.320
example of

00:31:42.799 --> 00:31:48.120
that um the document types that are

00:31:45.320 --> 00:31:51.080
included are the common crawl and so the

00:31:48.120 --> 00:31:53.919
common crawl is just um data crawled

00:31:51.080 --> 00:31:56.760
from the Internet it's uh about 2.2

00:31:53.919 --> 00:32:00.039
trillion tokens uh they also have the

00:31:56.760 --> 00:32:03.399
stack which is um lots of code about 400

00:32:00.039 --> 00:32:09.120
billion tokens of code um C4 which is

00:32:03.399 --> 00:32:13.039
also uh web data uh Reddit um stem

00:32:09.120 --> 00:32:16.960
papers books and uh Wikipedia

00:32:13.039 --> 00:32:19.039
encyclopedia T so um you can see that it

00:32:16.960 --> 00:32:21.440
has a fairly large amount of coverage

00:32:19.039 --> 00:32:24.480
although mostly in

00:32:21.440 --> 00:32:26.799
English um so some findings from omo

00:32:24.480 --> 00:32:29.440
that I found interesting um number one

00:32:26.799 --> 00:32:31.279
it has competitive average performance

00:32:29.440 --> 00:32:34.320
so as I mentioned I think this is the

00:32:31.279 --> 00:32:38.519
first fully open and documented language

00:32:34.320 --> 00:32:40.639
model on the 7 billion range that is

00:32:38.519 --> 00:32:43.360
competitive with all the other uh kind

00:32:40.639 --> 00:32:47.080
of like Less open models in this range

00:32:43.360 --> 00:32:49.200
so uh for example uh llama 2 is 70.5

00:32:47.080 --> 00:32:51.840
average on on all of the data sets that

00:32:49.200 --> 00:32:53.960
they're evaluating on Falcon is

00:32:51.840 --> 00:32:58.000
70.3 MPT is

00:32:53.960 --> 00:33:00.000
69.8 and almost 69.3 so it's not a

00:32:58.000 --> 00:33:04.639
slouch with respect to accuracy compared

00:33:00.000 --> 00:33:06.399
to pipia which had 63 um much of the

00:33:04.639 --> 00:33:09.120
issue with pipia could just be that they

00:33:06.399 --> 00:33:12.080
didn't train for long enough and some

00:33:09.120 --> 00:33:15.039
evidence of this is this is

00:33:12.080 --> 00:33:17.000
um where they measured performance

00:33:15.039 --> 00:33:18.880
constantly as they train for longer so

00:33:17.000 --> 00:33:21.440
the left side is training on 500 billion

00:33:18.880 --> 00:33:24.080
tokens which is already more than what

00:33:21.440 --> 00:33:25.840
pipia trained on the right side is uh

00:33:24.080 --> 00:33:30.360
two uh

00:33:25.840 --> 00:33:32.679
2.4 or 2.5 TR I tokens and you can see

00:33:30.360 --> 00:33:34.440
interestingly that the numbers are just

00:33:32.679 --> 00:33:36.760
continuing to increase as they train for

00:33:34.440 --> 00:33:39.480
longer so it seems that training for

00:33:36.760 --> 00:33:43.679
longer and longer just kind of

00:33:39.480 --> 00:33:47.000
helps um one question is whether they're

00:33:43.679 --> 00:33:48.679
like overfitting to uh the data set like

00:33:47.000 --> 00:33:52.000
is any of the test data included in

00:33:48.679 --> 00:33:53.799
their training data here um they did do

00:33:52.000 --> 00:33:57.440
D duplication to some extent to try to

00:33:53.799 --> 00:33:59.320
remove the test data so um I I think

00:33:57.440 --> 00:34:00.919
it's quite probable that this these are

00:33:59.320 --> 00:34:02.720
real gains and if they train for longer

00:34:00.919 --> 00:34:07.559
they might get an even better model but

00:34:02.720 --> 00:34:07.559
um I'm not you know 100% sure about

00:34:07.679 --> 00:34:12.639
that cool

00:34:10.480 --> 00:34:14.359
um yeah one one other thing that I

00:34:12.639 --> 00:34:16.119
noticed which might be uh might be a

00:34:14.359 --> 00:34:18.119
little bit interesting is um all of

00:34:16.119 --> 00:34:20.240
these that I didn't mention here is all

00:34:18.119 --> 00:34:21.760
of these have a learning rate schedule

00:34:20.240 --> 00:34:23.679
and typically they have a learning rate

00:34:21.760 --> 00:34:25.760
schedule where they do this standard

00:34:23.679 --> 00:34:29.159
warmup where they increase and then they

00:34:25.760 --> 00:34:30.960
decrease but they St decreasing at a a

00:34:29.159 --> 00:34:34.040
floor and usually that floor is about

00:34:30.960 --> 00:34:36.720
one1 the size of the um of the original

00:34:34.040 --> 00:34:38.520
learning rate so the if they start out 3

00:34:36.720 --> 00:34:41.919
e to Theus 4 they'll decrease it but

00:34:38.520 --> 00:34:43.960
only to 3 eus2 and then they're can so

00:34:41.919 --> 00:34:46.079
that might be another good thing to put

00:34:43.960 --> 00:34:46.079
it

00:34:46.480 --> 00:34:51.240
out cool any questions about

00:34:51.320 --> 00:34:58.599
this okay um so now I'll get into L 2 um

00:34:56.560 --> 00:35:00.200
in Lama 2 you know is a model that

00:34:58.599 --> 00:35:04.400
probably most people have heard about it

00:35:00.200 --> 00:35:07.599
was created by meta um it's one of the

00:35:04.400 --> 00:35:09.480
uh strongest open language models now

00:35:07.599 --> 00:35:10.839
although arguably there might be

00:35:09.480 --> 00:35:15.000
stronger open language

00:35:10.839 --> 00:35:18.400
models and the goal is a strong and safe

00:35:15.000 --> 00:35:21.320
open LM and they have base and chat

00:35:18.400 --> 00:35:23.400
versions of it and some unique features

00:35:21.320 --> 00:35:24.680
are I think this is the open model with

00:35:23.400 --> 00:35:30.119
the strongest

00:35:24.680 --> 00:35:30.119
safety uh safeguards so it

00:35:30.200 --> 00:35:35.079
is if I were to pick one model that I

00:35:33.079 --> 00:35:37.200
wanted to use in an actual system that

00:35:35.079 --> 00:35:39.599
was directly conversing with users I

00:35:37.200 --> 00:35:41.920
would probably pick this one over

00:35:39.599 --> 00:35:43.760
something like uh mistol even though

00:35:41.920 --> 00:35:46.599
mistol shows Superior performance some

00:35:43.760 --> 00:35:48.680
of the time um it might say things that

00:35:46.599 --> 00:35:52.000
you don't want it to be saying to like

00:35:48.680 --> 00:35:55.520
users so I think that's one of the uh

00:35:52.000 --> 00:35:56.880
the nice things about M so I've been

00:35:55.520 --> 00:35:58.280
comparing everything else to it so

00:35:56.880 --> 00:36:00.560
that's pretty normal

00:35:58.280 --> 00:36:03.160
um one thing about the data is the data

00:36:00.560 --> 00:36:04.520
is not open they didn't say what data

00:36:03.160 --> 00:36:06.960
they trained on for reasons that I

00:36:04.520 --> 00:36:08.960
talked about before um what they did say

00:36:06.960 --> 00:36:12.400
is it was trained on public sources

00:36:08.960 --> 00:36:14.240
upsampling the most factual sources so

00:36:12.400 --> 00:36:17.640
um that's what they

00:36:14.240 --> 00:36:19.240
said the Llama one paper has more

00:36:17.640 --> 00:36:20.760
information and so I'll talk about what

00:36:19.240 --> 00:36:22.400
they did in the Llama one paper and we

00:36:20.760 --> 00:36:24.920
can maybe extrapolate that they did

00:36:22.400 --> 00:36:26.560
something similar in the LL tube paper

00:36:24.920 --> 00:36:28.200
um and then the total training amount is

00:36:26.560 --> 00:36:30.079
2 trillion tokens so that's actually

00:36:28.200 --> 00:36:32.680
less

00:36:30.079 --> 00:36:34.520
than um so if we look at the Llama 1

00:36:32.680 --> 00:36:36.319
training data it looks a little bit like

00:36:34.520 --> 00:36:38.839
it looks very much like Theo training

00:36:36.319 --> 00:36:41.200
data it's common crawl C4 GitHub

00:36:38.839 --> 00:36:45.160
Wikipedia books archives stack

00:36:41.200 --> 00:36:46.400
exchange um and one thing you'll notice

00:36:45.160 --> 00:36:49.200
is that they

00:36:46.400 --> 00:36:51.599
upsampled uh Wikipedia and books and

00:36:49.200 --> 00:36:53.319
down sampled GitHub according compared

00:36:51.599 --> 00:36:57.000
to the amount of data that they actually

00:36:53.319 --> 00:37:00.760
had and so they did 2.4 EPO over

00:36:57.000 --> 00:37:03.040
Wikipedia 2.2 epochs over books and only

00:37:00.760 --> 00:37:05.880
one Epoch over like the standard web

00:37:03.040 --> 00:37:08.240
data and archive and stack exchange and

00:37:05.880 --> 00:37:09.760
0.6 epx over the GitHub data that they

00:37:08.240 --> 00:37:11.520
had so

00:37:09.760 --> 00:37:13.800
obviously

00:37:11.520 --> 00:37:15.520
they thought that this Wikipedia and

00:37:13.800 --> 00:37:17.040
books data was more valuable for some

00:37:15.520 --> 00:37:20.560
reason and they really wanted the model

00:37:17.040 --> 00:37:22.319
to to learn well out it so I think um

00:37:20.560 --> 00:37:24.240
when they say that they upsampled

00:37:22.319 --> 00:37:27.960
factual data I'm assuming that that's

00:37:24.240 --> 00:37:27.960
also what they did in mud

00:37:29.440 --> 00:37:33.640
so the next thing um that's

00:37:35.960 --> 00:37:43.160
yeah uh what does it need to have

00:37:40.280 --> 00:37:45.400
like oh um yeah actually that's a really

00:37:43.160 --> 00:37:47.960
good question so why are EPO not integer

00:37:45.400 --> 00:37:50.240
values there's actually no reason at all

00:37:47.960 --> 00:37:52.040
that you should do you know an integer

00:37:50.240 --> 00:37:54.760
value of epo you can always save out a

00:37:52.040 --> 00:37:57.560
checkpoint every you know 10,000 steps

00:37:54.760 --> 00:37:59.200
or something so I'd actually encourage

00:37:57.560 --> 00:38:02.040
people to get away from saving out

00:37:59.200 --> 00:38:03.640
checkpoints every Epoch because that

00:38:02.040 --> 00:38:05.319
kind of discourages you from making your

00:38:03.640 --> 00:38:07.160
training data larger because if you make

00:38:05.319 --> 00:38:09.359
your training data larger it will take

00:38:07.160 --> 00:38:11.760
you'll think oh training takes forever

00:38:09.359 --> 00:38:13.480
um because it takes forever to use an

00:38:11.760 --> 00:38:16.599
Epoch but in reality you can just save

00:38:13.480 --> 00:38:18.760
out you know periodically and um and

00:38:16.599 --> 00:38:21.319
keep the checkpoints from earlier

00:38:18.760 --> 00:38:22.680
so many language models don't train on

00:38:21.319 --> 00:38:24.480
all the data on the web because it would

00:38:22.680 --> 00:38:25.800
just be too expensive to do so despite

00:38:24.480 --> 00:38:27.640
the fact that they have all the data on

00:38:25.800 --> 00:38:29.079
the web

00:38:27.640 --> 00:38:31.000
but very good question though it's

00:38:29.079 --> 00:38:34.560
that's an important

00:38:31.000 --> 00:38:36.280
Point um okay so now I'd like to talk a

00:38:34.560 --> 00:38:39.440
little bit about the safety tuning that

00:38:36.280 --> 00:38:42.359
goes into uh the Llama models I might

00:38:39.440 --> 00:38:45.640
talk a little bit more about this um

00:38:42.359 --> 00:38:48.960
later but I I think uh I'll I'll talk

00:38:45.640 --> 00:38:51.480
about it now um basically the Llama 2

00:38:48.960 --> 00:38:54.200
developers put a lot of effort into

00:38:51.480 --> 00:38:56.400
training the model to be safe because um

00:38:54.200 --> 00:38:59.599
you know they're a big company and they

00:38:56.400 --> 00:39:01.200
don't want any PR design disasters um uh

00:38:59.599 --> 00:39:02.680
and also you know they want an actual

00:39:01.200 --> 00:39:04.960
safe model that they can use and to BL

00:39:02.680 --> 00:39:08.240
their products so I think they have the

00:39:04.960 --> 00:39:10.880
Dual uh you know dual motivation

00:39:08.240 --> 00:39:13.200
there the first thing that they did was

00:39:10.880 --> 00:39:15.960
they collected lots of data for reward

00:39:13.200 --> 00:39:17.520
modeling and reward modeling what they

00:39:15.960 --> 00:39:19.720
say what they're calling reward modeling

00:39:17.520 --> 00:39:23.720
is basically preference modeling so they

00:39:19.720 --> 00:39:26.359
have you know multiple outputs where the

00:39:23.720 --> 00:39:28.359
two outputs are somehow ranked for

00:39:26.359 --> 00:39:29.960
preferences and I talked about this when

00:39:28.359 --> 00:39:31.839
I was talking about DPO in the

00:39:29.960 --> 00:39:35.720
reinforcement learning class for

00:39:31.839 --> 00:39:38.480
example um a lot of these actually exist

00:39:35.720 --> 00:39:41.920
so there's um like the anthropic helpful

00:39:38.480 --> 00:39:45.599
and harmless data sets uh these open AI

00:39:41.920 --> 00:39:48.200
data sets uh from web GPT stack exchange

00:39:45.599 --> 00:39:50.160
on stack exchange they have um helpful

00:39:48.200 --> 00:39:52.240
answers and not helpful answers so once

00:39:50.160 --> 00:39:57.720
that you give thumbs up and thumbs down

00:39:52.240 --> 00:39:59.839
to and um the Stanford uh human

00:39:57.720 --> 00:40:03.040
preferences data set I I forget what s

00:39:59.839 --> 00:40:05.800
stands for human preferences data set

00:40:03.040 --> 00:40:09.400
basically this is um where they tried to

00:40:05.800 --> 00:40:11.599
find Reddit posts I think Reddit posts

00:40:09.400 --> 00:40:13.720
that got more upvotes despite the fact

00:40:11.599 --> 00:40:16.400
that they were posted later than a a

00:40:13.720 --> 00:40:18.720
previous one so the idea is like usually

00:40:16.400 --> 00:40:21.359
the first post posts get more up votes

00:40:18.720 --> 00:40:22.880
so if you get more up votes for a later

00:40:21.359 --> 00:40:25.240
post that indicates that you're probably

00:40:22.880 --> 00:40:27.640
more valuable than the earlier post so

00:40:25.240 --> 00:40:30.880
kind of clever uh clever way of creating

00:40:27.640 --> 00:40:33.680
data um I'm actually not sure what the

00:40:30.880 --> 00:40:36.240
synthetic jpj was I didn't look at that

00:40:33.680 --> 00:40:37.640
and then separately from that um meta

00:40:36.240 --> 00:40:39.599
collected a very large amount of

00:40:37.640 --> 00:40:42.400
internal data that they didn't release

00:40:39.599 --> 00:40:44.319
uh for tuning llama and they did this

00:40:42.400 --> 00:40:46.760
through various iterations so basically

00:40:44.319 --> 00:40:49.839
what they did is they created a first

00:40:46.760 --> 00:40:53.240
version of the model um they let it you

00:40:49.839 --> 00:40:55.599
loose on users they also did some uh

00:40:53.240 --> 00:40:56.960
some data collection with uh people who

00:40:55.599 --> 00:40:59.720
were actually trying to break the model

00:40:56.960 --> 00:41:01.200
and get getting it to say bad things

00:40:59.720 --> 00:41:02.760
they collected preference data from

00:41:01.200 --> 00:41:04.599
these people and then they iterated over

00:41:02.760 --> 00:41:06.960
and over again to collect more and more

00:41:04.599 --> 00:41:09.720
of this data on various uh versions of

00:41:06.960 --> 00:41:11.280
the model so as the model got gets

00:41:09.720 --> 00:41:14.079
better you know it's going to be harder

00:41:11.280 --> 00:41:16.240
to collect this data but um they want to

00:41:14.079 --> 00:41:17.920
try to improve the current model that

00:41:16.240 --> 00:41:20.599
they

00:41:17.920 --> 00:41:22.680
have so the next step that they did was

00:41:20.599 --> 00:41:26.079
they trained a model to follow these

00:41:22.680 --> 00:41:27.920
preferences and so they trained a model

00:41:26.079 --> 00:41:32.560
that basically can predict human

00:41:27.920 --> 00:41:35.119
preference given um given to uh language

00:41:32.560 --> 00:41:37.680
model outputs and this is a hard problem

00:41:35.119 --> 00:41:40.440
right because these are language model

00:41:37.680 --> 00:41:42.760
outputs and the language model thought

00:41:40.440 --> 00:41:45.480
it was a good output regardless because

00:41:42.760 --> 00:41:47.319
otherwise it wouldn't be sampling and so

00:41:45.480 --> 00:41:49.720
you need to distinguish between two very

00:41:47.319 --> 00:41:52.240
fluent looking outputs where one is

00:41:49.720 --> 00:41:56.880
preferred and one is not preferred so

00:41:52.240 --> 00:41:58.359
even kind of strong models like um oh by

00:41:56.880 --> 00:42:00.319
the way there are some open reward

00:41:58.359 --> 00:42:02.119
models like this open Assistant reward

00:42:00.319 --> 00:42:03.839
model is publicly available and you can

00:42:02.119 --> 00:42:08.520
just go and download it if you want if

00:42:03.839 --> 00:42:10.920
you want it um but this if you evaluate

00:42:08.520 --> 00:42:14.720
it on this anthropic uh helpful and

00:42:10.920 --> 00:42:16.160
harmless data set um this gets about 67

00:42:14.720 --> 00:42:18.760
or 68

00:42:16.160 --> 00:42:24.680
accuracy

00:42:18.760 --> 00:42:27.200
um but if you evaluate it on um this

00:42:24.680 --> 00:42:29.480
like open Assistant data set or sorry if

00:42:27.200 --> 00:42:33.359
you evaluate the public models including

00:42:29.480 --> 00:42:36.079
gp4 on The Meta data set actually it's

00:42:33.359 --> 00:42:38.720
pretty hard for um to distinguish

00:42:36.079 --> 00:42:41.319
between the things and here they're

00:42:38.720 --> 00:42:44.720
evaluating both helpful and harmless or

00:42:41.319 --> 00:42:47.400
helpful and safety and the reason why is

00:42:44.720 --> 00:42:49.119
because like it's very easy to create a

00:42:47.400 --> 00:42:51.119
very safe but not helpful at all model

00:42:49.119 --> 00:42:53.640
by saying I don't know all the time it's

00:42:51.119 --> 00:42:55.480
very it's relatively easy to create a

00:42:53.640 --> 00:42:57.880
helpful model that's very unsafe like it

00:42:55.480 --> 00:42:59.480
will do anything you want and so they

00:42:57.880 --> 00:43:01.599
want a balance between the two and they

00:42:59.480 --> 00:43:03.480
evaluate them separately they also

00:43:01.599 --> 00:43:05.280
created two different separate reward

00:43:03.480 --> 00:43:07.880
models so they created one reward model

00:43:05.280 --> 00:43:10.079
to distinguish safety and another reward

00:43:07.880 --> 00:43:13.440
model to distinguish helpfulness and

00:43:10.079 --> 00:43:14.760
they Ed these separately to uh to train

00:43:13.440 --> 00:43:17.359
the model and you can see that the

00:43:14.760 --> 00:43:18.920
helpfulness model does a lot better on

00:43:17.359 --> 00:43:20.640
discriminating between helpful things

00:43:18.920 --> 00:43:22.319
and the safety model does a lot better

00:43:20.640 --> 00:43:23.760
on discriminate or does a little better

00:43:22.319 --> 00:43:25.960
on discriminating between safe and

00:43:23.760 --> 00:43:28.480
unsafe

00:43:25.960 --> 00:43:29.920
things um

00:43:28.480 --> 00:43:33.640
actually I didn't include this in the

00:43:29.920 --> 00:43:35.400
slides but they also have an interesting

00:43:33.640 --> 00:43:38.920
graph that

00:43:35.400 --> 00:43:41.119
demonstrates um how good the reward

00:43:38.920 --> 00:43:42.640
models are based on their size and it

00:43:41.119 --> 00:43:44.359
turns out that this is a place where

00:43:42.640 --> 00:43:47.559
it's really really important to use a

00:43:44.359 --> 00:43:49.760
large and Powerful language model to

00:43:47.559 --> 00:43:51.319
determine your reward because they

00:43:49.760 --> 00:43:52.680
demonstrate that the 70 billion

00:43:51.319 --> 00:43:55.280
parameter model that they used is

00:43:52.680 --> 00:43:57.359
actually far better than the um than the

00:43:55.280 --> 00:44:00.079
smaller models that they used it

00:43:57.359 --> 00:44:00.079
predicting this

00:44:01.359 --> 00:44:07.760
reward so this is um a graph of their

00:44:05.200 --> 00:44:10.480
incremental training process for safety

00:44:07.760 --> 00:44:12.640
tuning and um you can see they have

00:44:10.480 --> 00:44:15.920
their first supervised fine tuned model

00:44:12.640 --> 00:44:19.440
this is with no um like RL or anything

00:44:15.920 --> 00:44:22.240
like this this is a second model

00:44:19.440 --> 00:44:24.760
um and uh it improves a lot with respect

00:44:22.240 --> 00:44:28.119
to helpfulness and then they do more and

00:44:24.760 --> 00:44:30.400
more rhf uh where they start with the

00:44:28.119 --> 00:44:33.200
like supervised fine tune model and and

00:44:30.400 --> 00:44:36.079
gradually do um add more reward data

00:44:33.200 --> 00:44:38.200
train with a better reward model and get

00:44:36.079 --> 00:44:39.800
to the end where they finally have the

00:44:38.200 --> 00:44:41.359
best model that and I believe this is

00:44:39.800 --> 00:44:43.200
the one that they actually released so

00:44:41.359 --> 00:44:45.000
you can see that they really put a lot

00:44:43.200 --> 00:44:46.520
of effort into making this model you

00:44:45.000 --> 00:44:49.800
know safe and that's one of the main

00:44:46.520 --> 00:44:49.800
points of the paper that they had

00:44:51.319 --> 00:44:57.920
here um another interesting part of the

00:44:55.119 --> 00:45:02.319
Llama 2 paper is how how they got it to

00:44:57.920 --> 00:45:05.280
follow chat instructions and so um I I

00:45:02.319 --> 00:45:06.640
think you're all familiar from the class

00:45:05.280 --> 00:45:10.040
where I talked about

00:45:06.640 --> 00:45:13.000
prompting B where basically they um

00:45:10.040 --> 00:45:16.119
prompt the language model using a system

00:45:13.000 --> 00:45:20.359
message and um a user message and an

00:45:16.119 --> 00:45:23.160
assistant message and so um the

00:45:20.359 --> 00:45:25.000
characteristic of the system message is

00:45:23.160 --> 00:45:28.240
this is something that you want to be

00:45:25.000 --> 00:45:32.319
obeyed throughout the um entire

00:45:28.240 --> 00:45:34.599
conversation right and

00:45:32.319 --> 00:45:36.760
so in order to get this obeyed

00:45:34.599 --> 00:45:38.079
throughout the entire conversation you

00:45:36.760 --> 00:45:39.760
need a model that's good at paying

00:45:38.079 --> 00:45:40.760
attent paying particular attention to

00:45:39.760 --> 00:45:43.160
the system

00:45:40.760 --> 00:45:45.319
message um in this example I'm saying

00:45:43.160 --> 00:45:46.880
write in only emojis so you no matter

00:45:45.319 --> 00:45:48.720
how long this conversation gets you want

00:45:46.880 --> 00:45:50.599
your model to continue writing in emojis

00:45:48.720 --> 00:45:53.440
and models don't do this

00:45:50.599 --> 00:45:56.559
spontaneously so what they did here and

00:45:53.440 --> 00:45:58.359
I'm I'm 90% 95% certain that my

00:45:56.559 --> 00:45:59.800
interpret of the paper is correct the

00:45:58.359 --> 00:46:03.319
paper is a little bit hard to understand

00:45:59.800 --> 00:46:06.720
with respect to this but um the uh what

00:46:03.319 --> 00:46:10.480
they I think they do is they take the

00:46:06.720 --> 00:46:13.200
system message and then they have a data

00:46:10.480 --> 00:46:16.160
generation step where they

00:46:13.200 --> 00:46:19.079
basically ask an existing model to write

00:46:16.160 --> 00:46:21.400
and only emojis and then say hello and

00:46:19.079 --> 00:46:23.640
then the model generates something and

00:46:21.400 --> 00:46:26.599
then they say again write in only emojis

00:46:23.640 --> 00:46:28.440
how are you doing and then they uh they

00:46:26.599 --> 00:46:29.599
generate it again and because this is so

00:46:28.440 --> 00:46:32.680
close in the

00:46:29.599 --> 00:46:35.440
context um the assistant basically will

00:46:32.680 --> 00:46:36.760
be will you know continue paying

00:46:35.440 --> 00:46:39.119
attention to these

00:46:36.760 --> 00:46:40.599
directions um and then after that now

00:46:39.119 --> 00:46:42.640
you have a data set that you can train

00:46:40.599 --> 00:46:44.280
your model on you can train your model

00:46:42.640 --> 00:46:46.880
on this generated data set that looks

00:46:44.280 --> 00:46:49.079
like write an only emojis say hello uh

00:46:46.880 --> 00:46:50.480
how are you doing and stuff like this

00:46:49.079 --> 00:46:54.040
and they try this with a whole bunch of

00:46:50.480 --> 00:46:57.880
rules it's like right um right as if

00:46:54.040 --> 00:47:00.559
you're explaining to a 5-year-old or um

00:46:57.880 --> 00:47:02.720
write in a very polite manner write in a

00:47:00.559 --> 00:47:03.960
very informal Manner and stuff like that

00:47:02.720 --> 00:47:06.480
so they generate a whole bunch of the

00:47:03.960 --> 00:47:08.480
synthetic data and in doing this they

00:47:06.480 --> 00:47:09.960
basically are able to train the model to

00:47:08.480 --> 00:47:11.559
pay very close attention to the system

00:47:09.960 --> 00:47:13.480
message because it needs to do so in

00:47:11.559 --> 00:47:17.319
order to do

00:47:13.480 --> 00:47:19.160
better so um yeah these are kind of the

00:47:17.319 --> 00:47:20.599
unique characteristics from lava 2 I'd

00:47:19.160 --> 00:47:21.960
love to tell you more about its training

00:47:20.599 --> 00:47:24.520
data and all that other stuff but they

00:47:21.960 --> 00:47:26.240
didn't tell us uh like what they did

00:47:24.520 --> 00:47:28.839
with respect to that so we'll just have

00:47:26.240 --> 00:47:28.839
to infer

00:47:28.960 --> 00:47:33.559
on cool uh any questions about

00:47:33.800 --> 00:47:39.160
this okay

00:47:36.640 --> 00:47:40.839
go so next I want to go into mistol and

00:47:39.160 --> 00:47:42.599
mixol this is going to be a little bit

00:47:40.839 --> 00:47:44.200
short because I've kind of covered some

00:47:42.599 --> 00:47:45.720
of the stuff already and also they

00:47:44.200 --> 00:47:48.240
didn't tell you very much about the

00:47:45.720 --> 00:47:52.240
training process um basically it was

00:47:48.240 --> 00:47:54.079
created by mistol um AI the company and

00:47:52.240 --> 00:47:56.839
it's a strong and somewhat multilingual

00:47:54.079 --> 00:47:59.400
open language model um it has some

00:47:56.839 --> 00:48:01.760
unique features like speed optimizations

00:47:59.400 --> 00:48:03.200
in um including grouped query attention

00:48:01.760 --> 00:48:06.200
and mixture of

00:48:03.200 --> 00:48:06.200
experts

00:48:06.599 --> 00:48:12.359
um it makes unlike the other ones it

00:48:10.599 --> 00:48:14.599
makes some actual architectural

00:48:12.359 --> 00:48:17.599
modifications including sliding window

00:48:14.599 --> 00:48:19.160
attention and um mixture of experts and

00:48:17.599 --> 00:48:21.079
I I have actually talked about both of

00:48:19.160 --> 00:48:23.640
them so I'll just very briefly go

00:48:21.079 --> 00:48:26.040
through them here um the data as far as

00:48:23.640 --> 00:48:28.559
I could tell was not disclosed uh very

00:48:26.040 --> 00:48:30.480
completely but one important thing is it

00:48:28.559 --> 00:48:32.160
includes English and European languages

00:48:30.480 --> 00:48:35.520
so at least theoretically it should be

00:48:32.160 --> 00:48:38.040
better than llama at this um one

00:48:35.520 --> 00:48:39.559
interesting thing about llama is llama

00:48:38.040 --> 00:48:40.680
if I remember correctly the actual

00:48:39.559 --> 00:48:42.880
numbers are in the paper but it's

00:48:40.680 --> 00:48:47.920
something like 85%

00:48:42.880 --> 00:48:52.400
English um 8% code and then like

00:48:47.920 --> 00:48:54.559
0.3% other languages like um starting at

00:48:52.400 --> 00:48:57.280
all the other languages it's like 0.3%

00:48:54.559 --> 00:48:59.680
so it's not very multilingual at all

00:48:57.280 --> 00:49:01.319
um and they were really only aiming to

00:48:59.680 --> 00:49:04.799
create a good uh English

00:49:01.319 --> 00:49:06.200
model um also the training uh details

00:49:04.799 --> 00:49:08.280
were not disclosed here like I wasn't

00:49:06.200 --> 00:49:12.400
able to find the back sides as far as I

00:49:08.280 --> 00:49:15.119
know um so mistol uses sliding window

00:49:12.400 --> 00:49:18.200
attention uh vanilla attention basically

00:49:15.119 --> 00:49:21.440
you always attend to all of the previous

00:49:18.200 --> 00:49:24.880
things in the sequence what mistol does

00:49:21.440 --> 00:49:28.119
is it attends to the previous n um

00:49:24.880 --> 00:49:30.559
examples where n is equal to 4090 6 and

00:49:28.119 --> 00:49:34.839
because of this uh what this means is

00:49:30.559 --> 00:49:37.200
you can attend uh 4096 back and then in

00:49:34.839 --> 00:49:39.280
the next layer you can attend 4096 back

00:49:37.200 --> 00:49:41.599
then you can attend 4096 back so

00:49:39.280 --> 00:49:44.400
basically as many layers as you have

00:49:41.599 --> 00:49:47.240
times 4096 you can attend that many

00:49:44.400 --> 00:49:49.000
tokens back for a minimal training

00:49:47.240 --> 00:49:50.760
penalty because still the length of

00:49:49.000 --> 00:49:55.079
attention for any particular token is

00:49:50.760 --> 00:49:57.440
the same uh so that's one

00:49:55.079 --> 00:50:00.400
feature oh and then yeah sorry the other

00:49:57.440 --> 00:50:01.920
feature is mixol is using um is using a

00:50:00.400 --> 00:50:05.920
mixture of experts like we talked about

00:50:01.920 --> 00:50:07.720
in the previous time so um despite these

00:50:05.920 --> 00:50:09.520
uh these are very strong models they're

00:50:07.720 --> 00:50:12.960
generally stronger than llama at a lot

00:50:09.520 --> 00:50:15.480
of things um and mixol is actually a lot

00:50:12.960 --> 00:50:18.200
faster and easier to deploy than llama

00:50:15.480 --> 00:50:20.680
70b uh it's smaller it only has 45

00:50:18.200 --> 00:50:23.680
billion parameters so it's definitely a

00:50:20.680 --> 00:50:26.680
good choice if you want to use it yeah

00:50:23.680 --> 00:50:26.680
makinging

00:50:28.720 --> 00:50:33.000
yeah so it's attending to 496

00:50:33.520 --> 00:50:39.559
C so the contact size

00:50:37.720 --> 00:50:43.240
typically like let's say you have a

00:50:39.559 --> 00:50:45.240
block of 4096 tokens here typically that

00:50:43.240 --> 00:50:48.079
means that the first token attends to

00:50:45.240 --> 00:50:51.200
zero tokens the second token attends to

00:50:48.079 --> 00:50:54.640
one token and the third token attends to

00:50:51.200 --> 00:50:58.920
two tokens here this is maybe a little

00:50:54.640 --> 00:51:01.680
bit uh Mis mislead I guess but if your

00:50:58.920 --> 00:51:04.079
context length is 4096 you actually get

00:51:01.680 --> 00:51:07.760
a block of twice that size you get a

00:51:04.079 --> 00:51:10.960
block of 8192 tokens and so the first

00:51:07.760 --> 00:51:15.839
one attends to all of the previous

00:51:10.960 --> 00:51:17.760
ones so the first uh sorry so

00:51:15.839 --> 00:51:19.960
the

00:51:17.760 --> 00:51:22.280
um so the

00:51:19.960 --> 00:51:26.760
40

00:51:22.280 --> 00:51:29.280
9 7 token

00:51:26.760 --> 00:51:32.280
back to um all from

00:51:29.280 --> 00:51:36.319
[Music]

00:51:32.280 --> 00:51:36.319
to sorry either

00:51:41.160 --> 00:51:46.880
one96 and

00:51:43.839 --> 00:51:50.520
so because of that you moan to the very

00:51:46.880 --> 00:51:50.520
end then you have the 8198

00:51:50.880 --> 00:51:55.359
seconding from like9

00:51:58.480 --> 00:52:01.920
and so like every token is always

00:52:00.319 --> 00:52:05.280
attending to the previous one and that

00:52:01.920 --> 00:52:08.200
allows you to um to kind of attend to

00:52:05.280 --> 00:52:08.200
things in the previous

00:52:11.760 --> 00:52:18.520
BL uh no it's big so that allows them to

00:52:15.000 --> 00:52:22.000
attend a very large

00:52:18.520 --> 00:52:24.599
am cool um so the next one I'd like to

00:52:22.000 --> 00:52:26.559
talk about is quen this is one that in

00:52:24.599 --> 00:52:29.040
the US at least people maybe pay a a

00:52:26.559 --> 00:52:33.000
little bit less attention to um but it

00:52:29.040 --> 00:52:35.680
was created by Alibaba and it's a strong

00:52:33.000 --> 00:52:37.559
um multilingual model especially English

00:52:35.680 --> 00:52:39.119
and Chinese but even uh in other

00:52:37.559 --> 00:52:41.000
languages as

00:52:39.119 --> 00:52:43.480
well

00:52:41.000 --> 00:52:45.160
and uh one of its defining

00:52:43.480 --> 00:52:48.240
characteristics other than just being a

00:52:45.160 --> 00:52:50.160
strong model overall is that it's has a

00:52:48.240 --> 00:52:51.799
large vocabulary for multilingual

00:52:50.160 --> 00:52:56.000
support and strong

00:52:51.799 --> 00:52:58.760
performance um it comes in several sizes

00:52:56.000 --> 00:53:01.880
um I

00:52:58.760 --> 00:53:04.799
believe uh there's a 7B version and then

00:53:01.880 --> 00:53:10.119
there's also like a large like 70b

00:53:04.799 --> 00:53:13.480
version 72b I think and it's using very

00:53:10.119 --> 00:53:15.319
standard uh architecture things the only

00:53:13.480 --> 00:53:18.119
small difference it has is it has a bias

00:53:15.319 --> 00:53:19.920
in the attention layer which is doesn't

00:53:18.119 --> 00:53:23.559
uh exist in

00:53:19.920 --> 00:53:25.880
llama um an important thing is it's

00:53:23.559 --> 00:53:28.920
actually trained on multilingual data

00:53:25.880 --> 00:53:32.720
and they use a large vocabulary um they

00:53:28.920 --> 00:53:33.839
use a vocabulary of 150k in contrast to

00:53:32.720 --> 00:53:36.599
llama's

00:53:33.839 --> 00:53:39.839
32k and that allows it to handle

00:53:36.599 --> 00:53:41.720
multilingual uh data relatively

00:53:39.839 --> 00:53:47.079
well

00:53:41.720 --> 00:53:49.359
and um we have the three uh similar you

00:53:47.079 --> 00:53:52.760
know training regimes so overall it's

00:53:49.359 --> 00:53:55.559
not very diff different from uh

00:53:52.760 --> 00:53:57.040
llama what might be different is data

00:53:55.559 --> 00:53:59.319
engineering

00:53:57.040 --> 00:54:00.680
uh and actually I I expect the data

00:53:59.319 --> 00:54:02.760
engineering part is a bit different

00:54:00.680 --> 00:54:06.400
because overall it's a bit stronger than

00:54:02.760 --> 00:54:09.920
llama 2 um and I I think uh that has to

00:54:06.400 --> 00:54:12.119
do with data in in various areas one

00:54:09.920 --> 00:54:16.920
interesting piece from the paper that

00:54:12.119 --> 00:54:18.280
they have is uh if we think all the way

00:54:16.920 --> 00:54:21.720
back to when we talked about word

00:54:18.280 --> 00:54:23.839
subword models and word tokenization we

00:54:21.720 --> 00:54:27.760
remember that subword models split up

00:54:23.839 --> 00:54:29.920
the input and they split up the input uh

00:54:27.760 --> 00:54:31.799
so that frequent tokens get longer

00:54:29.920 --> 00:54:34.520
outputs and infrequent tokens get

00:54:31.799 --> 00:54:36.359
shorter outputs so one of the problems

00:54:34.520 --> 00:54:40.559
as I mentioned a long time ago when we

00:54:36.359 --> 00:54:42.040
covered this topic is this causes issues

00:54:40.559 --> 00:54:43.000
if you're doing multilingual things

00:54:42.040 --> 00:54:44.880
because if you have very little

00:54:43.000 --> 00:54:47.520
multilingual data in your training data

00:54:44.880 --> 00:54:49.040
for the subword tokenization model um it

00:54:47.520 --> 00:54:51.559
will end up splitting all of the words

00:54:49.040 --> 00:54:55.680
into basically characters or even bytes

00:54:51.559 --> 00:54:59.040
so what this shows here is this is

00:54:55.680 --> 00:55:00.960
comparing the amount of subord

00:54:59.040 --> 00:55:03.040
tokenization that happens according to

00:55:00.960 --> 00:55:05.520
each of the llms

00:55:03.040 --> 00:55:08.599
tokenizers with another explicitly

00:55:05.520 --> 00:55:10.799
multilingual model xlmr so xlmr is kind

00:55:08.599 --> 00:55:12.760
of their Baseline here with respect to

00:55:10.799 --> 00:55:16.319
how much it tokenizes each

00:55:12.760 --> 00:55:19.079
language and on the very left we have

00:55:16.319 --> 00:55:22.839
llama and so what we can see is that

00:55:19.079 --> 00:55:26.599
llama tokenizes TI

00:55:22.839 --> 00:55:28.640
3.7 times as much as it as xlmr does so

00:55:26.599 --> 00:55:30.359
it's basically splitting tie into tie up

00:55:28.640 --> 00:55:32.480
into little tiny bits which makes it

00:55:30.359 --> 00:55:35.440
very expensive and ineffective to

00:55:32.480 --> 00:55:38.039
process uh let's let's find some other

00:55:35.440 --> 00:55:41.599
languages that we care about we have he

00:55:38.039 --> 00:55:43.760
Hebrew Arabic

00:55:41.599 --> 00:55:47.079
Korean uh

00:55:43.760 --> 00:55:49.559
Japanese uh Chinese so all of these you

00:55:47.079 --> 00:55:52.319
can see are split up pretty into many

00:55:49.559 --> 00:55:55.440
many different chunks by

00:55:52.319 --> 00:55:56.799
Lama and then we we have a few other

00:55:55.440 --> 00:55:58.359
language models in the middle and then

00:55:56.799 --> 00:56:01.440
we have quen on the right side and what

00:55:58.359 --> 00:56:04.039
we can see is basically it's pretty

00:56:01.440 --> 00:56:06.400
comparable to xlmr maybe a little bit

00:56:04.039 --> 00:56:09.520
more than xlmr but pretty comparable to

00:56:06.400 --> 00:56:12.839
xlmr on many languages and then on code

00:56:09.520 --> 00:56:15.000
it actually um splits up code much less

00:56:12.839 --> 00:56:17.039
so we can see that you know its

00:56:15.000 --> 00:56:18.960
tokenizer is heavily

00:56:17.039 --> 00:56:22.640
multilingual um another thing I'd like

00:56:18.960 --> 00:56:24.640
to point out is um I I let I'm focusing

00:56:22.640 --> 00:56:27.000
on this particular language model for a

00:56:24.640 --> 00:56:29.799
number of reasons

00:56:27.000 --> 00:56:32.440
um the first one is multilinguality and

00:56:29.799 --> 00:56:36.599
I I like multilinguality I hope other

00:56:32.440 --> 00:56:39.039
people like multilinguality too um but

00:56:36.599 --> 00:56:43.799
another motivation is just it has quite

00:56:39.039 --> 00:56:45.680
strong performance and it's uh topping

00:56:43.799 --> 00:56:47.960
topping the leaderboards in in several

00:56:45.680 --> 00:56:52.160
different uh

00:56:47.960 --> 00:56:57.640
places so if we look at the open llm

00:56:52.160 --> 00:56:57.640
leaderboard um at least recently

00:56:59.480 --> 00:57:07.440
this was a fine-tuned model by Abus

00:57:04.240 --> 00:57:09.440
AI which was uh originally based on quen

00:57:07.440 --> 00:57:11.079
so you can see that this is like a

00:57:09.440 --> 00:57:13.920
strong found Foundation model that lots

00:57:11.079 --> 00:57:16.440
of people are using for fing things so

00:57:13.920 --> 00:57:18.960
um I would definitely uh encourage you

00:57:16.440 --> 00:57:20.240
to take a look at that too of course

00:57:18.960 --> 00:57:22.520
there's many many different models that

00:57:20.240 --> 00:57:24.880
I didn't cover because if I covered all

00:57:22.520 --> 00:57:26.839
of the general purpose models then we'd

00:57:24.880 --> 00:57:29.599
be here all day but um

00:57:26.839 --> 00:57:31.200
that's uh first start so next I want to

00:57:29.599 --> 00:57:33.200
go into other kind of special purpose

00:57:31.200 --> 00:57:36.839
models but are there any questions about

00:57:33.200 --> 00:57:36.839
um about the things I covered so

00:57:38.000 --> 00:57:44.079
far cool okay

00:57:41.440 --> 00:57:47.960
um so next I'd like to go into other

00:57:44.079 --> 00:57:49.760
models um first is code models so code

00:57:47.960 --> 00:57:52.680
models are models that were specifically

00:57:49.760 --> 00:57:55.280
trained on code actually right now every

00:57:52.680 --> 00:57:56.960
model is a code model um like nobody

00:57:55.280 --> 00:57:58.799
pre-train a large language model and is

00:57:56.960 --> 00:58:01.720
serious about it and doesn't train on

00:57:58.799 --> 00:58:04.680
code because um generating code is a

00:58:01.720 --> 00:58:06.680
huge use case and also um some work has

00:58:04.680 --> 00:58:08.880
demonstrated that gen training on code

00:58:06.680 --> 00:58:13.720
seems to improve reasoning abilities of

00:58:08.880 --> 00:58:16.160
language models as well um but uh these

00:58:13.720 --> 00:58:19.319
models were very heavily trained on code

00:58:16.160 --> 00:58:22.400
so um we have star coder 2 this is a

00:58:19.319 --> 00:58:24.079
very recent uh entry this is a fully

00:58:22.400 --> 00:58:26.720
open model so you can see the data it

00:58:24.079 --> 00:58:29.039
was trained on um all the training

00:58:26.720 --> 00:58:31.640
details are released and other stuff

00:58:29.039 --> 00:58:36.760
like that so this is kind of in the

00:58:31.640 --> 00:58:38.599
pythia you know piao category but it's

00:58:36.760 --> 00:58:41.240
very uh it's actually a very strong

00:58:38.599 --> 00:58:42.839
model very good model so it's uh a good

00:58:41.240 --> 00:58:46.480
one to know

00:58:42.839 --> 00:58:48.680
about um separately there's code llama

00:58:46.480 --> 00:58:52.520
by meta which is a code adaptation of

00:58:48.680 --> 00:58:54.799
llama and uh it also gets quite a quite

00:58:52.520 --> 00:58:57.720
good performance there's also another

00:58:54.799 --> 00:58:59.760
model uh called seek coder I would say

00:58:57.720 --> 00:59:01.720
all three of these are topping some

00:58:59.760 --> 00:59:03.119
variety of leaderboard where deep seek

00:59:01.720 --> 00:59:04.640
maybe is topping a few more leader

00:59:03.119 --> 00:59:06.319
boards than the other ones are but all

00:59:04.640 --> 00:59:09.960
of them are very competitive and might

00:59:06.319 --> 00:59:11.680
be the best in class for code things um

00:59:09.960 --> 00:59:13.119
I'm not talking very much about these

00:59:11.680 --> 00:59:15.119
because we're going to have a a class on

00:59:13.119 --> 00:59:18.280
code generation and code related things

00:59:15.119 --> 00:59:21.000
later so um I'm not going to go into a

00:59:18.280 --> 00:59:21.000
lot of detail

00:59:21.319 --> 00:59:27.839
here another thing is about math models

00:59:24.680 --> 00:59:31.960
and so like one thing is large language

00:59:27.839 --> 00:59:35.480
models are not particularly good at math

00:59:31.960 --> 00:59:38.839
um so there are quite a few models that

00:59:35.480 --> 00:59:40.200
were trained specifically for math um

00:59:38.839 --> 00:59:45.160
the first one is

00:59:40.200 --> 00:59:47.280
Lemma um yes that is a pun um for like

00:59:45.160 --> 00:59:49.920
LMA from

00:59:47.280 --> 00:59:51.160
maap I I'm I'm not responsible for it

00:59:49.920 --> 00:59:55.240
but I I thought it was kind of funny

00:59:51.160 --> 00:59:56.920
anyway um so uh this was by alther AI so

00:59:55.240 --> 01:00:00.359
because this was by Luther again this is

00:59:56.920 --> 01:00:03.640
a fully open model all the data is open

01:00:00.359 --> 01:00:05.960
um everything is known about it um also

01:00:03.640 --> 01:00:08.480
uh our our very own Shan wck was one of

01:00:05.960 --> 01:00:10.559
the contributors to it uh so if you want

01:00:08.480 --> 01:00:13.839
to know more about LMA you can go bother

01:00:10.559 --> 01:00:17.440
Sean so uh that's another thing that I

01:00:13.839 --> 01:00:19.240
should mention um another thing is deep

01:00:17.440 --> 01:00:20.839
seek who made the Deep seek Cod model

01:00:19.240 --> 01:00:23.480
has also created a very strong math

01:00:20.839 --> 01:00:26.200
model uh that's competitive with gp4 on

01:00:23.480 --> 01:00:28.160
a lot of math things uh basically the

01:00:26.200 --> 01:00:30.480
way they did this was they did this by

01:00:28.160 --> 01:00:32.559
um training a classifier to try to

01:00:30.480 --> 01:00:34.640
identify data on the web that is related

01:00:32.559 --> 01:00:37.599
to math and scraping all of that data

01:00:34.640 --> 01:00:39.960
and fine tuning on it so um you can get

01:00:37.599 --> 01:00:42.280
gold standard data from like proof pile

01:00:39.960 --> 01:00:44.359
and a whole bunch of other sources and

01:00:42.280 --> 01:00:46.200
so they trained a like math or not maath

01:00:44.359 --> 01:00:48.400
classifier and and harvested a lot of

01:00:46.200 --> 01:00:52.400
math related

01:00:48.400 --> 01:00:52.400
dat yeah

01:00:59.880 --> 01:01:04.920
it's mostly mostly data sets um I

01:01:03.599 --> 01:01:07.119
actually might be talking a little bit

01:01:04.920 --> 01:01:10.039
more about these in the reasoning class

01:01:07.119 --> 01:01:11.799
and I did a lot of uh I did a lot of

01:01:10.039 --> 01:01:13.599
prep to create these slides and actually

01:01:11.799 --> 01:01:15.680
ran out of time to do the math stuff so

01:01:13.599 --> 01:01:17.200
I might talk about it later um but I

01:01:15.680 --> 01:01:18.480
don't think they're really doing a lot

01:01:17.200 --> 01:01:21.799
of things like you could think of

01:01:18.480 --> 01:01:23.440
obvious things like doing RL rlf based

01:01:21.799 --> 01:01:26.799
on like whether it gets the answer right

01:01:23.440 --> 01:01:28.559
or not in the end um as far as I know

01:01:26.799 --> 01:01:30.359
that's not a big ingredient here but

01:01:28.559 --> 01:01:31.920
I'll be more sure of that when we talk

01:01:30.359 --> 01:01:37.599
about it

01:01:31.920 --> 01:01:39.559
later um cool and a final one uh it's

01:01:37.599 --> 01:01:43.200
not a Sy model it's a science model

01:01:39.559 --> 01:01:45.920
sorry for the typo um but uh this model

01:01:43.200 --> 01:01:49.160
Galactica um was a model for science

01:01:45.920 --> 01:01:51.799
that was trained by meta

01:01:49.160 --> 01:01:54.359
um does anyone remember this model or

01:01:51.799 --> 01:01:58.079
was anybody around when this model came

01:01:54.359 --> 01:01:59.640
out no there was a big uh a big PR

01:01:58.079 --> 01:02:01.160
disaster for meta when they released

01:01:59.640 --> 01:02:03.480
this model because they said this is a

01:02:01.160 --> 01:02:05.520
great model for math use it in your in

01:02:03.480 --> 01:02:08.599
writing your science paper sorry this is

01:02:05.520 --> 01:02:10.480
a great model for science try using it

01:02:08.599 --> 01:02:12.640
it in your science papers and this came

01:02:10.480 --> 01:02:14.839
out about two years ago and two years

01:02:12.640 --> 01:02:16.640
ago language models hallucinated all the

01:02:14.839 --> 01:02:19.279
time and came up with false scientific

01:02:16.640 --> 01:02:22.039
facts and stuff and so basically um a

01:02:19.279 --> 01:02:25.680
lot of people kind of bashed this model

01:02:22.039 --> 01:02:27.440
uh in my mind kind of unfairly because

01:02:25.680 --> 01:02:31.200
they actually have a lot of really

01:02:27.440 --> 01:02:32.960
interesting things in this paper um one

01:02:31.200 --> 01:02:34.720
interesting thing in this paper is they

01:02:32.960 --> 01:02:37.000
tried to create a general purpose model

01:02:34.720 --> 01:02:38.960
for science that's able to understand

01:02:37.000 --> 01:02:41.960
not only text but also various

01:02:38.960 --> 01:02:47.720
modalities of scientific data and so

01:02:41.960 --> 01:02:51.000
that includes text it includes latex um

01:02:47.720 --> 01:02:53.799
you know equations it includes code but

01:02:51.000 --> 01:02:58.559
it also included things like molecular

01:02:53.799 --> 01:03:01.799
structures and uh like collagens and DNA

01:02:58.559 --> 01:03:04.160
and stuff like this so they tried to

01:03:01.799 --> 01:03:06.160
like model biology and other things like

01:03:04.160 --> 01:03:08.079
this as well so I I think it's really

01:03:06.160 --> 01:03:10.640
kind of too bad that this model got a a

01:03:08.079 --> 01:03:12.400
bad WAP because I I really like the you

01:03:10.640 --> 01:03:14.839
know the work that went into it and I

01:03:12.400 --> 01:03:16.359
hope we'll see more of this um because

01:03:14.839 --> 01:03:17.640
language models for science is a really

01:03:16.359 --> 01:03:19.880
big topic that a lot of people are

01:03:17.640 --> 01:03:19.880
thinking

01:03:20.760 --> 01:03:24.240
about

01:03:22.400 --> 01:03:26.440
cool

01:03:24.240 --> 01:03:28.000
um one thing I didn't talk about is

01:03:26.440 --> 01:03:29.880
multimodal models but I hope to talk

01:03:28.000 --> 01:03:32.440
about multimodal models in a a future

01:03:29.880 --> 01:03:33.359
class so um I'll I'll talk more about

01:03:32.440 --> 01:03:38.680
that

01:03:33.359 --> 01:03:41.640
soon um the next thing is Clos models um

01:03:38.680 --> 01:03:44.480
so Clos models we don't know a whole lot

01:03:41.640 --> 01:03:46.880
about them uh most of what we know about

01:03:44.480 --> 01:03:49.480
them in their training data and other

01:03:46.880 --> 01:03:52.359
things like that is their uh is

01:03:49.480 --> 01:03:54.720
conjecture so the

01:03:52.359 --> 01:03:57.839
standard the standard format for

01:03:54.720 --> 01:03:59.599
releasing in a closed model or not

01:03:57.839 --> 01:04:02.160
releasing but you know publicizing a

01:03:59.599 --> 01:04:04.279
closed model is people will write a blog

01:04:02.160 --> 01:04:05.960
post and they'll write a paper and

01:04:04.279 --> 01:04:07.720
generally what the paper does is it only

01:04:05.960 --> 01:04:09.559
talks about evaluation it only talks

01:04:07.720 --> 01:04:12.039
about like how good the model is on

01:04:09.559 --> 01:04:13.799
various things how safe it is how they

01:04:12.039 --> 01:04:16.279
put a lot of effort into red teeming the

01:04:13.799 --> 01:04:17.680
model uh so that it doesn't do bad

01:04:16.279 --> 01:04:18.839
things and stuff like that and it tells

01:04:17.680 --> 01:04:21.119
you nothing about how they actually

01:04:18.839 --> 01:04:23.279
built the model so mostly like what I

01:04:21.119 --> 01:04:26.279
can talk about are capabilities as

01:04:23.279 --> 01:04:28.520
opposed to um

01:04:26.279 --> 01:04:32.440
talk about our capabilities as opposed

01:04:28.520 --> 01:04:35.319
to like what actually went into the

01:04:32.440 --> 01:04:38.920
model so um there's

01:04:35.319 --> 01:04:40.880
gp4 um gp4 I think everybody knows it's

01:04:38.920 --> 01:04:43.640
kind of the de facto standard strong

01:04:40.880 --> 01:04:45.680
language model it used to be the only

01:04:43.640 --> 01:04:47.680
strong language model like it used to be

01:04:45.680 --> 01:04:50.079
on its own the strongest language model

01:04:47.680 --> 01:04:53.160
and there were no real competitors to

01:04:50.079 --> 01:04:55.000
gp4 from that point of view I think

01:04:53.160 --> 01:04:56.680
still if I wanted a strong language

01:04:55.000 --> 01:04:58.960
model for just something that I'm I'm

01:04:56.680 --> 01:05:00.880
going to do randomly I still rely on G I

01:04:58.960 --> 01:05:03.680
still trust gp4 more than anything else

01:05:00.880 --> 01:05:05.240
to give me a really good answer um but

01:05:03.680 --> 01:05:08.480
there are now other competitors I'd like

01:05:05.240 --> 01:05:11.960
to talk about so gp4 anyway um you know

01:05:08.480 --> 01:05:14.240
it Powers the pro version of chat GPT it

01:05:11.960 --> 01:05:18.039
was tuned to be good as a chat-based

01:05:14.240 --> 01:05:20.440
assistant um it accepts image inputs uh

01:05:18.039 --> 01:05:22.279
and it supports calling external tools

01:05:20.440 --> 01:05:23.599
through function calling uh through a

01:05:22.279 --> 01:05:27.119
function calling

01:05:23.599 --> 01:05:28.720
interface um

01:05:27.119 --> 01:05:30.599
I I think people are are generally

01:05:28.720 --> 01:05:34.000
familiar with this but just in case

01:05:30.599 --> 01:05:36.240
you're not um I'd like to show a few

01:05:34.000 --> 01:05:38.039
things that I like to

01:05:36.240 --> 01:05:39.640
do

01:05:38.039 --> 01:05:42.760
so let

01:05:39.640 --> 01:05:42.760
[Music]

01:05:46.920 --> 01:05:52.480
me so I'll just randomly grab one of my

01:05:50.440 --> 01:05:57.640
papers from

01:05:52.480 --> 01:05:57.640
archive um my Mo my most recent paper

01:06:03.400 --> 01:06:07.559
and I can copy paste

01:06:13.200 --> 01:06:22.240
this and write uh turn this into Json

01:06:19.240 --> 01:06:22.240
forat

01:06:27.960 --> 01:06:31.640
and I drop it in

01:06:29.880 --> 01:06:35.480
here

01:06:31.640 --> 01:06:38.279
and so this is an exhibit of its like

01:06:35.480 --> 01:06:42.240
multimodal abilities because I can throw

01:06:38.279 --> 01:06:44.359
in a uh in a

01:06:42.240 --> 01:06:48.400
table and it basically turns it into

01:06:44.359 --> 01:06:50.599
Json clat for so um I I actually turned

01:06:48.400 --> 01:06:52.119
a fair amount of data FR in that I

01:06:50.599 --> 01:06:53.960
created in creating these slides into

01:06:52.119 --> 01:06:56.039
Json format so I can save it later for

01:06:53.960 --> 01:06:59.079
whatever I want it for and I did it

01:06:56.039 --> 01:07:01.720
through uh this so this is an example of

01:06:59.079 --> 01:07:06.599
the multimodal abilities can also tell

01:07:01.720 --> 01:07:06.599
you about images and stuff like that

01:07:07.000 --> 01:07:14.319
um so also um there was a famous article

01:07:11.760 --> 01:07:16.760
written by Gary Marcus that said deep

01:07:14.319 --> 01:07:19.760
learning is hitting a wall um it

01:07:16.760 --> 01:07:22.880
basically was written two years ago and

01:07:19.760 --> 01:07:25.160
uh Gary Marcus was saying deep learning

01:07:22.880 --> 01:07:26.200
doesn't uh you know is not the way for

01:07:25.160 --> 01:07:27.760
the future sure we're going to need

01:07:26.200 --> 01:07:31.319
things other than deep learning in order

01:07:27.760 --> 01:07:34.559
to uh you know be able to uh make

01:07:31.319 --> 01:07:36.400
progress and whe whether you believe

01:07:34.559 --> 01:07:40.520
that is true or not I I will let you to

01:07:36.400 --> 01:07:46.520
your own opinion um but uh I could also

01:07:40.520 --> 01:07:51.359
say uh create a picture of deep learning

01:07:46.520 --> 01:07:55.400
breaking through a brick wall and it can

01:07:51.359 --> 01:07:55.400
generate images for you

01:08:02.599 --> 01:08:07.440
course if you ever do a live demo even

01:08:05.319 --> 01:08:10.319
if it's a live demo of open AI product

01:08:07.440 --> 01:08:13.559
that a million people use it will break

01:08:10.319 --> 01:08:16.719
when you try to do it so um so this is

01:08:13.559 --> 01:08:17.799
another uh thing that it can do so there

01:08:16.719 --> 01:08:19.560
we have a picture of deep learning

01:08:17.799 --> 01:08:22.640
breaking through a brick wall and it can

01:08:19.560 --> 01:08:26.159
you know generate images and stuff so

01:08:22.640 --> 01:08:28.560
these are like the kinds of things that

01:08:26.159 --> 01:08:30.960
I now

01:08:28.560 --> 01:08:32.880
expect so it's not just like reasoning

01:08:30.960 --> 01:08:35.839
ability and other stuff like that it's

01:08:32.880 --> 01:08:39.199
also multi multimodality being able to

01:08:35.839 --> 01:08:43.679
generate code um another thing that's

01:08:39.199 --> 01:08:46.719
kind of nice um is make a

01:08:43.679 --> 01:08:49.440
histogram of these

01:08:46.719 --> 01:08:54.640
numbers one

01:08:49.440 --> 01:08:54.640
two one two four

01:08:57.600 --> 01:09:04.040
so it can do code generation and and

01:08:59.719 --> 01:09:05.560
display the results for you um there are

01:09:04.040 --> 01:09:08.319
efforts to

01:09:05.560 --> 01:09:12.239
make open source language models be able

01:09:08.319 --> 01:09:14.000
to do these things and um in order to do

01:09:12.239 --> 01:09:16.759
this you need multimodality you need

01:09:14.000 --> 01:09:19.359
also the ability to use tools so

01:09:16.759 --> 01:09:21.400
actually the way that this um worked

01:09:19.359 --> 01:09:24.520
here is very different than the way that

01:09:21.400 --> 01:09:27.920
this worked so this is actually using a

01:09:24.520 --> 01:09:29.759
image input into gp4 so what it's doing

01:09:27.920 --> 01:09:33.040
is it's encoding the image and then

01:09:29.759 --> 01:09:34.719
feeding it in as tokens into gp4 what

01:09:33.040 --> 01:09:37.920
this is doing here is this is rather

01:09:34.719 --> 01:09:40.120
calling a tool this is calling uh dolly3

01:09:37.920 --> 01:09:42.120
as a tool and it's providing the caption

01:09:40.120 --> 01:09:46.880
to Dolly 3 you can even see maybe the

01:09:42.120 --> 01:09:46.880
caption that was provided to

01:09:48.640 --> 01:09:55.560
dolly3 you you previously were able to

01:09:51.239 --> 01:09:57.960
do that um by maybe downloading yeah so

01:09:55.560 --> 01:10:01.600
you can see the the

01:09:57.960 --> 01:10:01.600
caption uh which

01:10:03.560 --> 01:10:08.120
was a visual metaphor of deep learning

01:10:06.320 --> 01:10:10.679
is a powerful force breaking through a

01:10:08.120 --> 01:10:13.400
brick wall um or something like that and

01:10:10.679 --> 01:10:15.480
so gp4 basically what it did is it it

01:10:13.400 --> 01:10:18.000
said it wanted to call a tool and then

01:10:15.480 --> 01:10:19.360
it g provided the caption uh the caption

01:10:18.000 --> 01:10:21.280
and then it called it completely

01:10:19.360 --> 01:10:22.320
separate tool as an API in order to

01:10:21.280 --> 01:10:27.320
generate the

01:10:22.320 --> 01:10:27.320
image so um yeah the final

01:10:28.199 --> 01:10:34.080
well I managed to break chat gbt that's

01:10:30.120 --> 01:10:36.520
no small accomplishment um so but anyway

01:10:34.080 --> 01:10:40.199
these are some of the things that uh

01:10:36.520 --> 01:10:42.360
that the systems can do and because open

01:10:40.199 --> 01:10:47.000
AI has kind of become a standard that a

01:10:42.360 --> 01:10:50.040
lot of people want to uh compete with um

01:10:47.000 --> 01:10:53.480
also I would say Gemini Gemini and Claud

01:10:50.040 --> 01:10:56.400
are maybe the two um the two models that

01:10:53.480 --> 01:10:59.440
can compete with gp4 and terms of uh you

01:10:56.400 --> 01:11:02.600
know accuracy Gemini is a much newer

01:10:59.440 --> 01:11:06.159
model by Google that uh comes in two

01:11:02.600 --> 01:11:08.280
varieties Gemini Pro and Gemini Ultra uh

01:11:06.159 --> 01:11:11.040
one interesting thing about Gemini Pro

01:11:08.280 --> 01:11:13.560
is that it supports um very long inputs

01:11:11.040 --> 01:11:15.679
one to 10 million tokens it also

01:11:13.560 --> 01:11:16.600
supports image and video inputs and

01:11:15.679 --> 01:11:20.239
image

01:11:16.600 --> 01:11:22.320
outputs um I actually put a a video into

01:11:20.239 --> 01:11:24.600
it recently and the video recognition

01:11:22.320 --> 01:11:27.159
capabilities are pretty pretty nice so

01:11:24.600 --> 01:11:29.280
you can uh you can try that out if you

01:11:27.159 --> 01:11:34.320
want

01:11:29.280 --> 01:11:36.640
um and finally there's Claud it pla 3 it

01:11:34.320 --> 01:11:39.280
supports a context window of up to 200k

01:11:36.640 --> 01:11:41.040
also allows for processing images and

01:11:39.280 --> 01:11:46.480
overall has strong results competitive

01:11:41.040 --> 01:11:49.880
with gd4 so if you're looking for um if

01:11:46.480 --> 01:11:51.480
you're looking for models to use uh to

01:11:49.880 --> 01:11:53.600
try out better closed models you can

01:11:51.480 --> 01:11:55.719
definitely use these another thing I'm

01:11:53.600 --> 01:11:58.239
really excited about is how can we get

01:11:55.719 --> 01:11:59.560
like open models to you know demonstrate

01:11:58.239 --> 01:12:01.320
some of the interesting capabilities

01:11:59.560 --> 01:12:02.840
that we see in closed models so you know

01:12:01.320 --> 01:12:07.120
everybody can benefit and everybody

01:12:02.840 --> 01:12:10.040
knows uh you know uh the recipes to make

01:12:07.120 --> 01:12:12.560
models like this so I think that's

01:12:10.040 --> 01:12:16.639
mostly all I have for today another um

01:12:12.560 --> 01:12:23.440
another thing that is kind of neat

01:12:16.639 --> 01:12:23.440
is I just found this a little while ago

01:12:28.800 --> 01:12:32.239
but there is this uh

01:12:33.320 --> 01:12:39.239
interface uh called the god mode that

01:12:36.880 --> 01:12:41.960
allows you to put all of the chat apps

01:12:39.239 --> 01:12:45.840
next to each other and write the same

01:12:41.960 --> 01:12:47.080
chat query into them and uh and get the

01:12:45.840 --> 01:12:48.719
result from all of them so you can

01:12:47.080 --> 01:12:51.080
actually compare all of them in kind of

01:12:48.719 --> 01:12:52.840
an interactive settings so if you want

01:12:51.080 --> 01:12:54.800
to look at all especially all of the

01:12:52.840 --> 01:12:56.679
closed models open models it's you know

01:12:54.800 --> 01:12:58.239
not too are to do it yourself but if you

01:12:56.679 --> 01:12:59.840
want to try all of the Clos models

01:12:58.239 --> 01:13:01.800
together you can do that and like log

01:12:59.840 --> 01:13:03.960
into all of your accounts and then press

01:13:01.800 --> 01:13:05.320
go on aquery and see how they all this F

01:13:03.960 --> 01:13:07.960
so

01:13:05.320 --> 01:13:09.800
um that might be a good way to compare

01:13:07.960 --> 01:13:12.000
all of the models kind of qualitatively

01:13:09.800 --> 01:13:14.679
as opposed to

01:13:12.000 --> 01:13:17.280
qualitatively cool um that's all I have

01:13:14.679 --> 01:13:19.440
for today uh I don't know are there any

01:13:17.280 --> 01:13:23.440
questions or discussion or things like

01:13:19.440 --> 01:13:23.440
this yeah

01:13:28.840 --> 01:13:35.679
so a systematic way um the first thing

01:13:32.760 --> 01:13:37.960
you can do is look at the Benchmark

01:13:35.679 --> 01:13:40.800
results that have been published but

01:13:37.960 --> 01:13:43.320
actually I would like to give a caveat

01:13:40.800 --> 01:13:43.320
about

01:13:45.199 --> 01:13:48.440
this which

01:13:50.000 --> 01:13:54.000
is um

01:14:22.960 --> 01:14:28.239
so these are are the best bench marking

01:14:25.600 --> 01:14:30.840
results for the Gemini

01:14:28.239 --> 01:14:33.440
paper um

01:14:30.840 --> 01:14:36.719
and they have a table here um and

01:14:33.440 --> 01:14:38.679
basically what they kind of obviously to

01:14:36.719 --> 01:14:41.679
me wanted to demonstrate is that Gemini

01:14:38.679 --> 01:14:44.760
was the best model out of all the models

01:14:41.679 --> 01:14:47.800
um and so they have Gemini Pro and

01:14:44.760 --> 01:14:50.040
Gemini Ultra and they put Gemini Pro

01:14:47.800 --> 01:14:52.639
Ultra against gp4 and Gemini Pro against

01:14:50.040 --> 01:14:56.360
GPT 3.5 because they're you know

01:14:52.639 --> 01:14:58.440
comparable models um

01:14:56.360 --> 01:15:01.880
and they're yeah because they're

01:14:58.440 --> 01:15:03.040
comparable models basically and on

01:15:01.880 --> 01:15:05.880
things

01:15:03.040 --> 01:15:07.400
like um and they demonstrate that

01:15:05.880 --> 01:15:08.199
basically they're better in all all of

01:15:07.400 --> 01:15:10.520
these

01:15:08.199 --> 01:15:14.760
situations however there's a few details

01:15:10.520 --> 01:15:17.120
the first detail is um that the method

01:15:14.760 --> 01:15:20.199
that they're using to prompt the model

01:15:17.120 --> 01:15:22.120
is different here so we have like 94.4

01:15:20.199 --> 01:15:23.560
versus 92 but the method they're using

01:15:22.120 --> 01:15:25.520
to prompt the model is different they're

01:15:23.560 --> 01:15:29.159
using they're

01:15:25.520 --> 01:15:33.320
32 and then basically uh getting the

01:15:29.159 --> 01:15:36.320
best from 32 and then another thing

01:15:33.320 --> 01:15:41.360
is if we look at this Human ofal

01:15:36.320 --> 01:15:44.120
Performance here um they reported their

01:15:41.360 --> 01:15:47.000
Human ofel Performance then they pulled

01:15:44.120 --> 01:15:49.400
the number from the original gp4 paper

01:15:47.000 --> 01:15:53.159
and compared to the number from the gp4

01:15:49.400 --> 01:15:54.639
paper but all of these um you know apis

01:15:53.159 --> 01:15:57.719
are constantly changing they're getting

01:15:54.639 --> 01:15:59.480
better and better so we went um I I was

01:15:57.719 --> 01:16:01.400
very excited when Gemini first came out

01:15:59.480 --> 01:16:03.120
and we actually wrote a paper where we

01:16:01.400 --> 01:16:05.320
tried to look deeper into the

01:16:03.120 --> 01:16:08.000
performance and what we actually found

01:16:05.320 --> 01:16:10.199
is comparing Gemini Pro and GPT 3.5

01:16:08.000 --> 01:16:12.719
turbo which should be comparable we

01:16:10.199 --> 01:16:16.120
found that actually GPT 3.5 turbo did a

01:16:12.719 --> 01:16:19.280
little bit better um in in most cases

01:16:16.120 --> 01:16:20.920
although not all cases and one of the

01:16:19.280 --> 01:16:24.000
things we noticed in particular is like

01:16:20.920 --> 01:16:27.960
human ofel GPD 3.5 had gotten like much

01:16:24.000 --> 01:16:29.760
much better over the course of uh like

01:16:27.960 --> 01:16:31.639
the time between the original paper was

01:16:29.760 --> 01:16:34.120
reported it had gone up by almost 30

01:16:31.639 --> 01:16:35.760
points and also in a few cases we had

01:16:34.120 --> 01:16:37.480
like a little bit of trouble reproducing

01:16:35.760 --> 01:16:39.280
the Gemini Pro results just because they

01:16:37.480 --> 01:16:40.360
had like safety filters and other stuff

01:16:39.280 --> 01:16:42.520
like that that we had to get around

01:16:40.360 --> 01:16:45.280
before we got the results so it's not

01:16:42.520 --> 01:16:49.560
necessarily the case that you can

01:16:45.280 --> 01:16:52.639
completely take the um that you can

01:16:49.560 --> 01:16:55.560
completely take the results on face

01:16:52.639 --> 01:16:57.040
value actually as a first St I would

01:16:55.560 --> 01:17:00.080
suggest just trying to chat with the

01:16:57.040 --> 01:17:03.719
model um which is also why I introduced

01:17:00.080 --> 01:17:06.679
the like quote unquote god mode uh like

01:17:03.719 --> 01:17:09.159
browser because like you can kind of

01:17:06.679 --> 01:17:10.639
tell when it like when something's way

01:17:09.159 --> 01:17:14.320
better than another one just by the

01:17:10.639 --> 01:17:17.159
respones ites um separately if you want

01:17:14.320 --> 01:17:17.159
to do it much more

01:17:20.199 --> 01:17:23.840
systematically there are really nice

01:17:22.360 --> 01:17:25.400
tools for evaluation I think I might

01:17:23.840 --> 01:17:26.960
have talked about this before but if I

01:17:25.400 --> 01:17:29.280
haven't then you should definitely take

01:17:26.960 --> 01:17:31.880
a look at this there's the alther

01:17:29.280 --> 01:17:34.040
evaluation harness and the alther

01:17:31.880 --> 01:17:35.679
evaluation harness makes it really easy

01:17:34.040 --> 01:17:37.600
to evaluate for example hugging face

01:17:35.679 --> 01:17:39.040
models against many many different tasks

01:17:37.600 --> 01:17:41.360
so you can just pick which task you want

01:17:39.040 --> 01:17:43.719
to evaluate against pick the model name

01:17:41.360 --> 01:17:47.400
and and go and you can get evaluation

01:17:43.719 --> 01:17:51.960
results um that won't necessarily work

01:17:47.400 --> 01:17:53.960
for close models um but if you look for

01:17:51.960 --> 01:17:55.480
Uther language model evaluation harness

01:17:53.960 --> 01:17:58.800
that's maybe the easiest way to run

01:17:55.480 --> 01:17:58.800
evaluations or s for

01:17:59.239 --> 01:18:05.239
L Cool okay um so we're we're at time

01:18:02.960 --> 01:18:07.480
now uh but I'd be happy to answer a few

01:18:05.239 --> 01:18:10.639
questions if anybody else has any so

01:18:07.480 --> 01:18:10.639
thank you