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+WEBVTT
+
+00:00:00.280 --> 00:00:08.320
+can everyone hear Al set okay great so
+
+00:00:05.400 --> 00:00:09.840
+um today I'll be talking about a tour of
+
+00:00:08.320 --> 00:00:13.960
+modern uh
+
+00:00:09.840 --> 00:00:16.600
+llms and basically the idea here is that
+
+00:00:13.960 --> 00:00:18.600
+there is many many large language models
+
+00:00:16.600 --> 00:00:20.480
+available nowadays but I wanted to go
+
+00:00:18.600 --> 00:00:22.760
+through some of the ones that are
+
+00:00:20.480 --> 00:00:25.880
+particularly interesting for various
+
+00:00:22.760 --> 00:00:26.880
+reasons either because they disclose a
+
+00:00:25.880 --> 00:00:29.519
+lot of
+
+00:00:26.880 --> 00:00:31.119
+information uh you know about exactly
+
+00:00:29.519 --> 00:00:34.120
+how they were trains so we can get an
+
+00:00:31.119 --> 00:00:35.559
+idea about what is involved in training
+
+00:00:34.120 --> 00:00:39.120
+uh a kind of state-ofthe-art large
+
+00:00:35.559 --> 00:00:40.640
+language model or because they're kind
+
+00:00:39.120 --> 00:00:43.200
+of the strongest models that you can
+
+00:00:40.640 --> 00:00:45.160
+download and use on your own um like the
+
+00:00:43.200 --> 00:00:47.360
+best open weights language models that
+
+00:00:45.160 --> 00:00:49.559
+are available or because they're
+
+00:00:47.360 --> 00:00:51.879
+specialized to some particular topic or
+
+00:00:49.559 --> 00:00:53.480
+because they're the best closed uh
+
+00:00:51.879 --> 00:00:56.399
+language models but I'm going to
+
+00:00:53.480 --> 00:00:58.640
+particularly focus on the first two um
+
+00:00:56.399 --> 00:01:00.640
+just so like everybody has an idea about
+
+00:00:58.640 --> 00:01:03.239
+you know what what is going into all the
+
+00:01:00.640 --> 00:01:07.519
+models that you're using for whatever uh
+
+00:01:03.239 --> 00:01:07.519
+you know tasks that you're trying to
+
+00:01:09.119 --> 00:01:14.159
+solve so one important thing is uh what
+
+00:01:12.240 --> 00:01:18.080
+makes a model so we talk about you know
+
+00:01:14.159 --> 00:01:21.680
+like llama 2 or M roll or mix roll or
+
+00:01:18.080 --> 00:01:23.320
+whatever else and I think you know this
+
+00:01:21.680 --> 00:01:24.479
+already but it's worth reiterating again
+
+00:01:23.320 --> 00:01:27.320
+here because I'm going to talk about it
+
+00:01:24.479 --> 00:01:29.320
+a lot today but it's basically the model
+
+00:01:27.320 --> 00:01:31.280
+architecture so what architecture do you
+
+00:01:29.320 --> 00:01:33.799
+decide to use
+
+00:01:31.280 --> 00:01:35.840
+um what data do you decide to use and
+
+00:01:33.799 --> 00:01:39.759
+what training algorithm or Training
+
+00:01:35.840 --> 00:01:42.520
+Method do you decide to use and all of
+
+00:01:39.759 --> 00:01:46.040
+these are important um and there was
+
+00:01:42.520 --> 00:01:49.320
+actually uh a Twitter thread with Tom
+
+00:01:46.040 --> 00:01:52.399
+Wolf who's I guess CSO or CTO or
+
+00:01:49.320 --> 00:01:54.840
+something like that at hugging face um
+
+00:01:52.399 --> 00:01:56.840
+and basically what he was saying is uh a
+
+00:01:54.840 --> 00:01:59.240
+lot of people don't realize that the
+
+00:01:56.840 --> 00:02:01.039
+data is actually one of the most
+
+00:01:59.240 --> 00:02:04.320
+important parts
+
+00:02:01.039 --> 00:02:07.680
+um and the architectures are a lot less
+
+00:02:04.320 --> 00:02:10.920
+important nowadays and I think that
+
+00:02:07.680 --> 00:02:14.280
+there's some truth to that there's also
+
+00:02:10.920 --> 00:02:15.879
+some you know a counterargument to that
+
+00:02:14.280 --> 00:02:17.920
+uh the truth to that which you'll see
+
+00:02:15.879 --> 00:02:19.760
+today is that almost all of the models
+
+00:02:17.920 --> 00:02:21.360
+that we're using use very similar
+
+00:02:19.760 --> 00:02:23.120
+architectures like almost all of the
+
+00:02:21.360 --> 00:02:26.879
+models use an architecture that's very
+
+00:02:23.120 --> 00:02:28.760
+similar Dilma um but despite the fact
+
+00:02:26.879 --> 00:02:31.280
+that they use very similar architectures
+
+00:02:28.760 --> 00:02:33.599
+they're um accuracy is vastly different
+
+00:02:31.280 --> 00:02:36.080
+or their their abilities are vastly
+
+00:02:33.599 --> 00:02:38.519
+different so that must come from the
+
+00:02:36.080 --> 00:02:40.040
+data or the training decisions right so
+
+00:02:38.519 --> 00:02:41.640
+that's an argument for the fact that
+
+00:02:40.040 --> 00:02:44.040
+architecture decisions are a lot less
+
+00:02:41.640 --> 00:02:48.000
+important my counterargument to that is
+
+00:02:44.040 --> 00:02:49.840
+we spent N9 to 10 years fine-tuning and
+
+00:02:48.000 --> 00:02:51.560
+finding the Llama architecture so now we
+
+00:02:49.840 --> 00:02:53.120
+have the Llama architecture which is a
+
+00:02:51.560 --> 00:02:55.480
+really good architecture it works really
+
+00:02:53.120 --> 00:02:57.640
+well when training very large models on
+
+00:02:55.480 --> 00:02:59.239
+lots of data and so now we don't need to
+
+00:02:57.640 --> 00:03:01.360
+use another architecture because the
+
+00:02:59.239 --> 00:03:02.920
+architecture using is good but if we
+
+00:03:01.360 --> 00:03:06.200
+were trying to do the same thing with
+
+00:03:02.920 --> 00:03:07.640
+the like lstm from 2014 uh then none of
+
+00:03:06.200 --> 00:03:09.440
+the stuff we're doing today would work
+
+00:03:07.640 --> 00:03:11.760
+so that's an argument in favor of you
+
+00:03:09.440 --> 00:03:13.560
+know architectures being also
+
+00:03:11.760 --> 00:03:16.920
+architectures can make things faster and
+
+00:03:13.560 --> 00:03:16.920
+that's included in s decisions
+
+00:03:17.280 --> 00:03:21.280
+that
+
+00:03:19.040 --> 00:03:22.640
+so um the first thing I'd like to talk
+
+00:03:21.280 --> 00:03:25.280
+about before I get into any of the
+
+00:03:22.640 --> 00:03:28.000
+actual details is um open versus closed
+
+00:03:25.280 --> 00:03:30.480
+access uh this is not like modeling
+
+00:03:28.000 --> 00:03:31.760
+stuff but I think it's important and
+
+00:03:30.480 --> 00:03:35.599
+also helps you understand the
+
+00:03:31.760 --> 00:03:39.519
+environment a little bit so um there's a
+
+00:03:35.599 --> 00:03:42.200
+nice blog by pyang and others uh at
+
+00:03:39.519 --> 00:03:45.560
+which is also in the reference and they
+
+00:03:42.200 --> 00:03:47.720
+discuss several different varieties of
+
+00:03:45.560 --> 00:03:50.599
+like openness of release of language
+
+00:03:47.720 --> 00:03:52.560
+models in advanced AI systems and there
+
+00:03:50.599 --> 00:03:55.200
+are some things that we can talk about
+
+00:03:52.560 --> 00:03:59.000
+we can talk about the weights being open
+
+00:03:55.200 --> 00:04:01.439
+um described or closed inference uh code
+
+00:03:59.000 --> 00:04:03.319
+being open or inference methods being
+
+00:04:01.439 --> 00:04:04.959
+described or it being fully closed
+
+00:04:03.319 --> 00:04:08.120
+training being open described or closed
+
+00:04:04.959 --> 00:04:13.040
+and data being open described or closed
+
+00:04:08.120 --> 00:04:14.760
+and um in general uh we have like the
+
+00:04:13.040 --> 00:04:16.519
+open weights models that are on hugging
+
+00:04:14.760 --> 00:04:19.040
+face that might just mean the weights
+
+00:04:16.519 --> 00:04:20.600
+are open the inference code also needs
+
+00:04:19.040 --> 00:04:21.919
+to be open because otherwise you can't
+
+00:04:20.600 --> 00:04:24.160
+do inference on them if they're on
+
+00:04:21.919 --> 00:04:25.800
+hugging face but that doesn't mean that
+
+00:04:24.160 --> 00:04:28.120
+the training code is open it also
+
+00:04:25.800 --> 00:04:32.479
+doesn't mean that the data is open um
+
+00:04:28.120 --> 00:04:34.280
+and so there's various degrees of
+
+00:04:32.479 --> 00:04:37.320
+openness
+
+00:04:34.280 --> 00:04:40.919
+um and then of course there are things
+
+00:04:37.320 --> 00:04:42.520
+like uh GPT for or GPT models where
+
+00:04:40.919 --> 00:04:45.560
+basically all of this is closed and we
+
+00:04:42.520 --> 00:04:48.880
+don't know anything about it or know
+
+00:04:45.560 --> 00:04:50.560
+very little about it another thing is
+
+00:04:48.880 --> 00:04:52.600
+about licenses and
+
+00:04:50.560 --> 00:04:54.199
+permissiveness and this is kind of
+
+00:04:52.600 --> 00:04:56.880
+important if you want to do a research
+
+00:04:54.199 --> 00:05:01.240
+project to know because
+
+00:04:56.880 --> 00:05:04.080
+it means it it an impact on the things
+
+00:05:01.240 --> 00:05:05.520
+that you legally can do or can't do in
+
+00:05:04.080 --> 00:05:08.039
+universities I mean we should be
+
+00:05:05.520 --> 00:05:09.479
+following the law but we're maybe people
+
+00:05:08.039 --> 00:05:10.720
+think about this a little bit less if
+
+00:05:09.479 --> 00:05:12.240
+you're in a big company this is
+
+00:05:10.720 --> 00:05:14.919
+something that becomes really important
+
+00:05:12.240 --> 00:05:17.199
+so it's uh it's important to think
+
+00:05:14.919 --> 00:05:20.039
+about so I'm going to go through several
+
+00:05:17.199 --> 00:05:21.440
+degrees of licenses uh that if you've
+
+00:05:20.039 --> 00:05:25.759
+done anything in open source you
+
+00:05:21.440 --> 00:05:27.600
+probably know but um the or you probably
+
+00:05:25.759 --> 00:05:29.919
+know a lot of these the first one is
+
+00:05:27.600 --> 00:05:31.479
+public domain or cc0
+
+00:05:29.919 --> 00:05:33.440
+and this basically means you can do
+
+00:05:31.479 --> 00:05:37.240
+anything with it like I could I could
+
+00:05:33.440 --> 00:05:39.280
+download it and um this includes the
+
+00:05:37.240 --> 00:05:41.680
+download it and redistribute it not give
+
+00:05:39.280 --> 00:05:44.560
+you any credit uh modify it in any way I
+
+00:05:41.680 --> 00:05:47.720
+want and this includes things like old
+
+00:05:44.560 --> 00:05:49.600
+copyrighted works and products of the US
+
+00:05:47.720 --> 00:05:51.400
+government workers so if you work for
+
+00:05:49.600 --> 00:05:53.240
+the US government in some capacities
+
+00:05:51.400 --> 00:05:58.560
+anything you generate becomes public
+
+00:05:53.240 --> 00:06:01.000
+domain um so old copyrighted Works um
+
+00:05:58.560 --> 00:06:04.560
+How how old do you think they need to be
+
+00:06:01.000 --> 00:06:04.560
+before they become uh
+
+00:06:04.720 --> 00:06:12.280
+uncopyrighted
+
+00:06:07.000 --> 00:06:12.280
+yeah uh I think that's pretty close
+
+00:06:14.319 --> 00:06:21.280
+so it's uh 70 years I
+
+00:06:18.520 --> 00:06:23.680
+guess oh sorry the life of the author
+
+00:06:21.280 --> 00:06:25.120
+plus an additional 70 years so like
+
+00:06:23.680 --> 00:06:28.479
+after the after the person has passed
+
+00:06:25.120 --> 00:06:30.720
+away 70 years I guess it says um does
+
+00:06:28.479 --> 00:06:34.520
+anyone know a work that just become
+
+00:06:30.720 --> 00:06:37.520
+became non-copyrighted yeah uh Mickey
+
+00:06:34.520 --> 00:06:43.199
+Mouse is still copyrighted
+
+00:06:37.520 --> 00:06:45.199
+yeah SBO uh did did it I okay so that
+
+00:06:43.199 --> 00:06:48.400
+that's some new news some other new news
+
+00:06:45.199 --> 00:06:50.759
+is wi the Poo um so Winnie the Poo just
+
+00:06:48.400 --> 00:06:54.199
+became non-copyrighted and actually I
+
+00:06:50.759 --> 00:06:55.840
+just heard uh last week that somebody
+
+00:06:54.199 --> 00:06:59.680
+made a horror movie where Winnie the
+
+00:06:55.840 --> 00:07:01.479
+Pooh was a a killer and that one uh a
+
+00:06:59.680 --> 00:07:04.960
+whole bunch of like bad movie awards in
+
+00:07:01.479 --> 00:07:06.639
+2023 so um that's the kind of things
+
+00:07:04.960 --> 00:07:09.080
+that can happen to your copyrighted
+
+00:07:06.639 --> 00:07:11.479
+works if they are released cc0 somebody
+
+00:07:09.080 --> 00:07:12.960
+can do anything they want with them uh
+
+00:07:11.479 --> 00:07:14.400
+you know so you need to be a little bit
+
+00:07:12.960 --> 00:07:18.080
+careful about
+
+00:07:14.400 --> 00:07:20.000
+that um next are MIT and bstd these are
+
+00:07:18.080 --> 00:07:22.400
+very common software licenses you'll see
+
+00:07:20.000 --> 00:07:25.720
+them on a lot of research projects these
+
+00:07:22.400 --> 00:07:27.400
+have very few restrictions um other than
+
+00:07:25.720 --> 00:07:29.319
+maybe maintaining the copyright notice
+
+00:07:27.400 --> 00:07:31.840
+for BC but that's about it you can do
+
+00:07:29.319 --> 00:07:33.840
+just about anything you want with it um
+
+00:07:31.840 --> 00:07:35.599
+actually I'm not sure if people know
+
+00:07:33.840 --> 00:07:39.599
+this but the Mac operating system is
+
+00:07:35.599 --> 00:07:42.199
+based on an old BSD Opera uh operating
+
+00:07:39.599 --> 00:07:44.280
+system where they uh took the they took
+
+00:07:42.199 --> 00:07:46.080
+the code they made it private they
+
+00:07:44.280 --> 00:07:49.560
+forked it made it private and now it's
+
+00:07:46.080 --> 00:07:51.919
+the proprietary Mac operating system so
+
+00:07:49.560 --> 00:07:53.720
+uh that's something you can do with an m
+
+00:07:51.919 --> 00:07:57.840
+m or BSD
+
+00:07:53.720 --> 00:08:00.000
+licensed um there's also a Pachi and CC
+
+00:07:57.840 --> 00:08:02.560
+by um
+
+00:08:00.000 --> 00:08:05.039
+here you must acknowledge the owner of
+
+00:08:02.560 --> 00:08:07.840
+the uh the original creators so you need
+
+00:08:05.039 --> 00:08:08.960
+to say this person actually created uh
+
+00:08:07.840 --> 00:08:11.520
+this
+
+00:08:08.960 --> 00:08:14.680
+originally
+
+00:08:11.520 --> 00:08:17.319
+um Apachi is also kind of interesting
+
+00:08:14.680 --> 00:08:21.759
+because they will give you a license to
+
+00:08:17.319 --> 00:08:25.960
+use that code and any patents that are
+
+00:08:21.759 --> 00:08:29.599
+associated with that code unless you sue
+
+00:08:25.960 --> 00:08:32.159
+the company who released it so um just
+
+00:08:29.599 --> 00:08:34.039
+Give an example let's say uh Google
+
+00:08:32.159 --> 00:08:36.279
+released their code under the Apache
+
+00:08:34.039 --> 00:08:38.919
+license and that code implements
+
+00:08:36.279 --> 00:08:42.680
+Transformers and Google has a patent on
+
+00:08:38.919 --> 00:08:45.760
+Transformers so if you use uh kind of
+
+00:08:42.680 --> 00:08:48.200
+jacks or tensorflow a Jack or tensorflow
+
+00:08:45.760 --> 00:08:50.120
+implementation of Transformers uh that
+
+00:08:48.200 --> 00:08:51.720
+was created by Google you're okay you're
+
+00:08:50.120 --> 00:08:54.640
+safe to use that because they've
+
+00:08:51.720 --> 00:08:57.360
+released it under uh under that license
+
+00:08:54.640 --> 00:08:59.560
+but if you sue Google uh for anything
+
+00:08:57.360 --> 00:09:01.760
+related to intellectual property Google
+
+00:08:59.560 --> 00:09:04.480
+could say uh don't you can't use
+
+00:09:01.760 --> 00:09:06.040
+Transformers anymore um and so like if
+
+00:09:04.480 --> 00:09:08.279
+open AI ever sues Google for
+
+00:09:06.040 --> 00:09:09.680
+intellectual property infringement
+
+00:09:08.279 --> 00:09:12.120
+Google will say okay you can't use
+
+00:09:09.680 --> 00:09:15.959
+Transformers or word embeddings good
+
+00:09:12.120 --> 00:09:17.640
+luck uh open so um there's this
+
+00:09:15.959 --> 00:09:20.760
+interesting thing where all of these uh
+
+00:09:17.640 --> 00:09:22.760
+tech companies now are using patented um
+
+00:09:20.760 --> 00:09:24.440
+patented things a lot of it apachi
+
+00:09:22.760 --> 00:09:26.040
+license software and so none of them can
+
+00:09:24.440 --> 00:09:28.959
+sue each other for patents so patents
+
+00:09:26.040 --> 00:09:30.560
+have become basically mostly worthless
+
+00:09:28.959 --> 00:09:35.320
+uh in big
+
+00:09:30.560 --> 00:09:36.360
+te um moving on um there's also a g GPL
+
+00:09:35.320 --> 00:09:39.360
+in
+
+00:09:36.360 --> 00:09:42.800
+ccbsa these are licenses where if you
+
+00:09:39.360 --> 00:09:45.680
+use them you need to reshare under that
+
+00:09:42.800 --> 00:09:47.839
+license um and so like if you create
+
+00:09:45.680 --> 00:09:49.440
+some software it's GPL licensed and you
+
+00:09:47.839 --> 00:09:52.160
+build on it and build something new you
+
+00:09:49.440 --> 00:09:54.839
+need to release it under the GPL license
+
+00:09:52.160 --> 00:09:58.160
+so a lot of companies will not
+
+00:09:54.839 --> 00:09:59.640
+use um will not use GPL software because
+
+00:09:58.160 --> 00:10:01.920
+that would mean that if they incorporate
+
+00:09:59.640 --> 00:10:04.959
+into their system their whole system
+
+00:10:01.920 --> 00:10:06.720
+like for example Google uh like all of
+
+00:10:04.959 --> 00:10:10.240
+Google would have to be GPL licensed in
+
+00:10:06.720 --> 00:10:11.720
+Rel EAS uh so um and I'm kind of
+
+00:10:10.240 --> 00:10:14.800
+simplifying these licenses I'm just
+
+00:10:11.720 --> 00:10:17.519
+giving you the gist CC BSA and sorry CC
+
+00:10:14.800 --> 00:10:20.640
+licenses are more for data so MIT BSC
+
+00:10:17.519 --> 00:10:22.640
+Apachi and GPL are more for software CC
+
+00:10:20.640 --> 00:10:27.640
+Creative Commons licenses are for data
+
+00:10:22.640 --> 00:10:29.640
+so um for example Wikipedia is CC by SAA
+
+00:10:27.640 --> 00:10:33.560
+I believe
+
+00:10:29.640 --> 00:10:33.560
+let me make sure that I'm not lying
+
+00:10:41.839 --> 00:10:48.240
+there yeah CC bys and so that means that
+
+00:10:46.040 --> 00:10:52.200
+if you make any derivative work of
+
+00:10:48.240 --> 00:10:54.160
+Wikipedia you need to share it um the
+
+00:10:52.200 --> 00:10:57.040
+same way that Wikipedia is uh so you
+
+00:10:54.160 --> 00:10:59.760
+need to give it the same
+
+00:10:57.040 --> 00:11:01.560
+license there's also um cre of Commons
+
+00:10:59.760 --> 00:11:03.240
+non-commercial licenses or software
+
+00:11:01.560 --> 00:11:05.519
+non-commercial licenses you say you
+
+00:11:03.240 --> 00:11:07.079
+can't use them for commercial purposes
+
+00:11:05.519 --> 00:11:09.279
+all the ones above you can use for
+
+00:11:07.079 --> 00:11:11.519
+commercial purposes once you start
+
+00:11:09.279 --> 00:11:13.440
+getting down here this is no often no
+
+00:11:11.519 --> 00:11:15.279
+longer called open source so the open
+
+00:11:13.440 --> 00:11:16.959
+source initiative says anything with a
+
+00:11:15.279 --> 00:11:19.839
+restriction on the way that you can use
+
+00:11:16.959 --> 00:11:22.639
+it is no longer open source and so that
+
+00:11:19.839 --> 00:11:25.360
+means if you say you can't use this for
+
+00:11:22.639 --> 00:11:27.720
+commercial purposes or you can't use
+
+00:11:25.360 --> 00:11:29.639
+this in military systems for example
+
+00:11:27.720 --> 00:11:32.320
+which some language models say that
+
+00:11:29.639 --> 00:11:33.680
+nowadays those are no longer called open
+
+00:11:32.320 --> 00:11:37.040
+source according to the open source
+
+00:11:33.680 --> 00:11:40.320
+initiative so that's a thing to know
+
+00:11:37.040 --> 00:11:42.920
+about then separately uh there are these
+
+00:11:40.320 --> 00:11:45.279
+licenses that a lot of people like meta
+
+00:11:42.920 --> 00:11:48.160
+or hugging face come up with for their
+
+00:11:45.279 --> 00:11:50.360
+um for their models recently so the
+
+00:11:48.160 --> 00:11:51.320
+Llama license um how many people are
+
+00:11:50.360 --> 00:11:54.200
+using
+
+00:11:51.320 --> 00:11:56.519
+llama in your projects how many people
+
+00:11:54.200 --> 00:11:56.519
+read the
+
+00:11:57.000 --> 00:12:00.880
+license so um are you sure you can use
+
+00:11:59.639 --> 00:12:04.959
+it in your
+
+00:12:00.880 --> 00:12:06.839
+project uh so you're you're probably in
+
+00:12:04.959 --> 00:12:09.000
+luck in your project if you're using it
+
+00:12:06.839 --> 00:12:11.560
+the Lama license you can read into it to
+
+00:12:09.000 --> 00:12:13.519
+see what it actually allows but it has
+
+00:12:11.560 --> 00:12:16.399
+um the original llama license has some
+
+00:12:13.519 --> 00:12:18.440
+interesting uh things number one you
+
+00:12:16.399 --> 00:12:21.079
+cannot use llama to train any language
+
+00:12:18.440 --> 00:12:23.000
+model that is not derived from llama so
+
+00:12:21.079 --> 00:12:26.120
+you can't generate data from llama in
+
+00:12:23.000 --> 00:12:30.040
+train M that's not allowed according to
+
+00:12:26.120 --> 00:12:32.440
+the r Li um another thing is uh you
+
+00:12:30.040 --> 00:12:34.680
+can't use it for military purposes so
+
+00:12:32.440 --> 00:12:36.160
+you can't use it um in building a
+
+00:12:34.680 --> 00:12:37.639
+missile system or something like that
+
+00:12:36.160 --> 00:12:41.440
+hopefully none of you are doing that for
+
+00:12:37.639 --> 00:12:42.920
+your project um and you also need to get
+
+00:12:41.440 --> 00:12:45.399
+a license from meta if you have
+
+00:12:42.920 --> 00:12:48.000
+something more than 300 million active
+
+00:12:45.399 --> 00:12:53.800
+user asign your social network service
+
+00:12:48.000 --> 00:12:56.079
+so if you're Google or um you know X or
+
+00:12:53.800 --> 00:12:57.680
+Twitter or you know whatever else you
+
+00:12:56.079 --> 00:13:00.519
+need to get a license for meta before
+
+00:12:57.680 --> 00:13:02.079
+you can start using one so
+
+00:13:00.519 --> 00:13:03.240
+basically they created that license so
+
+00:13:02.079 --> 00:13:06.720
+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