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+1
+00:00:00,280 --> 00:00:08,320
+can everyone hear Al set okay great so
+
+2
+00:00:05,400 --> 00:00:09,840
+um today I'll be talking about a tour of
+
+3
+00:00:08,320 --> 00:00:13,960
+modern uh
+
+4
+00:00:09,840 --> 00:00:16,600
+llms and basically the idea here is that
+
+5
+00:00:13,960 --> 00:00:18,600
+there is many many large language models
+
+6
+00:00:16,600 --> 00:00:20,480
+available nowadays but I wanted to go
+
+7
+00:00:18,600 --> 00:00:22,760
+through some of the ones that are
+
+8
+00:00:20,480 --> 00:00:25,880
+particularly interesting for various
+
+9
+00:00:22,760 --> 00:00:26,880
+reasons either because they disclose a
+
+10
+00:00:25,880 --> 00:00:29,519
+lot of
+
+11
+00:00:26,880 --> 00:00:31,119
+information uh you know about exactly
+
+12
+00:00:29,519 --> 00:00:34,120
+how they were trains so we can get an
+
+13
+00:00:31,119 --> 00:00:35,559
+idea about what is involved in training
+
+14
+00:00:34,120 --> 00:00:39,120
+uh a kind of state-ofthe-art large
+
+15
+00:00:35,559 --> 00:00:40,640
+language model or because they're kind
+
+16
+00:00:39,120 --> 00:00:43,200
+of the strongest models that you can
+
+17
+00:00:40,640 --> 00:00:45,160
+download and use on your own um like the
+
+18
+00:00:43,200 --> 00:00:47,360
+best open weights language models that
+
+19
+00:00:45,160 --> 00:00:49,559
+are available or because they're
+
+20
+00:00:47,360 --> 00:00:51,879
+specialized to some particular topic or
+
+21
+00:00:49,559 --> 00:00:53,480
+because they're the best closed uh
+
+22
+00:00:51,879 --> 00:00:56,399
+language models but I'm going to
+
+23
+00:00:53,480 --> 00:00:58,640
+particularly focus on the first two um
+
+24
+00:00:56,399 --> 00:01:00,640
+just so like everybody has an idea about
+
+25
+00:00:58,640 --> 00:01:03,239
+you know what what is going into all the
+
+26
+00:01:00,640 --> 00:01:07,519
+models that you're using for whatever uh
+
+27
+00:01:03,239 --> 00:01:07,519
+you know tasks that you're trying to
+
+28
+00:01:09,119 --> 00:01:14,159
+solve so one important thing is uh what
+
+29
+00:01:12,240 --> 00:01:18,080
+makes a model so we talk about you know
+
+30
+00:01:14,159 --> 00:01:21,680
+like llama 2 or M roll or mix roll or
+
+31
+00:01:18,080 --> 00:01:23,320
+whatever else and I think you know this
+
+32
+00:01:21,680 --> 00:01:24,479
+already but it's worth reiterating again
+
+33
+00:01:23,320 --> 00:01:27,320
+here because I'm going to talk about it
+
+34
+00:01:24,479 --> 00:01:29,320
+a lot today but it's basically the model
+
+35
+00:01:27,320 --> 00:01:31,280
+architecture so what architecture do you
+
+36
+00:01:29,320 --> 00:01:33,799
+decide to use
+
+37
+00:01:31,280 --> 00:01:35,840
+um what data do you decide to use and
+
+38
+00:01:33,799 --> 00:01:39,759
+what training algorithm or Training
+
+39
+00:01:35,840 --> 00:01:42,520
+Method do you decide to use and all of
+
+40
+00:01:39,759 --> 00:01:46,040
+these are important um and there was
+
+41
+00:01:42,520 --> 00:01:49,320
+actually uh a Twitter thread with Tom
+
+42
+00:01:46,040 --> 00:01:52,399
+Wolf who's I guess CSO or CTO or
+
+43
+00:01:49,320 --> 00:01:54,840
+something like that at hugging face um
+
+44
+00:01:52,399 --> 00:01:56,840
+and basically what he was saying is uh a
+
+45
+00:01:54,840 --> 00:01:59,240
+lot of people don't realize that the
+
+46
+00:01:56,840 --> 00:02:01,039
+data is actually one of the most
+
+47
+00:01:59,240 --> 00:02:04,320
+important parts
+
+48
+00:02:01,039 --> 00:02:07,680
+um and the architectures are a lot less
+
+49
+00:02:04,320 --> 00:02:10,920
+important nowadays and I think that
+
+50
+00:02:07,680 --> 00:02:14,280
+there's some truth to that there's also
+
+51
+00:02:10,920 --> 00:02:15,879
+some you know a counterargument to that
+
+52
+00:02:14,280 --> 00:02:17,920
+uh the truth to that which you'll see
+
+53
+00:02:15,879 --> 00:02:19,760
+today is that almost all of the models
+
+54
+00:02:17,920 --> 00:02:21,360
+that we're using use very similar
+
+55
+00:02:19,760 --> 00:02:23,120
+architectures like almost all of the
+
+56
+00:02:21,360 --> 00:02:26,879
+models use an architecture that's very
+
+57
+00:02:23,120 --> 00:02:28,760
+similar Dilma um but despite the fact
+
+58
+00:02:26,879 --> 00:02:31,280
+that they use very similar architectures
+
+59
+00:02:28,760 --> 00:02:33,599
+they're um accuracy is vastly different
+
+60
+00:02:31,280 --> 00:02:36,080
+or their their abilities are vastly
+
+61
+00:02:33,599 --> 00:02:38,519
+different so that must come from the
+
+62
+00:02:36,080 --> 00:02:40,040
+data or the training decisions right so
+
+63
+00:02:38,519 --> 00:02:41,640
+that's an argument for the fact that
+
+64
+00:02:40,040 --> 00:02:44,040
+architecture decisions are a lot less
+
+65
+00:02:41,640 --> 00:02:48,000
+important my counterargument to that is
+
+66
+00:02:44,040 --> 00:02:49,840
+we spent N9 to 10 years fine-tuning and
+
+67
+00:02:48,000 --> 00:02:51,560
+finding the Llama architecture so now we
+
+68
+00:02:49,840 --> 00:02:53,120
+have the Llama architecture which is a
+
+69
+00:02:51,560 --> 00:02:55,480
+really good architecture it works really
+
+70
+00:02:53,120 --> 00:02:57,640
+well when training very large models on
+
+71
+00:02:55,480 --> 00:02:59,239
+lots of data and so now we don't need to
+
+72
+00:02:57,640 --> 00:03:01,360
+use another architecture because the
+
+73
+00:02:59,239 --> 00:03:02,920
+architecture using is good but if we
+
+74
+00:03:01,360 --> 00:03:06,200
+were trying to do the same thing with
+
+75
+00:03:02,920 --> 00:03:07,640
+the like lstm from 2014 uh then none of
+
+76
+00:03:06,200 --> 00:03:09,440
+the stuff we're doing today would work
+
+77
+00:03:07,640 --> 00:03:11,760
+so that's an argument in favor of you
+
+78
+00:03:09,440 --> 00:03:13,560
+know architectures being also
+
+79
+00:03:11,760 --> 00:03:16,920
+architectures can make things faster and
+
+80
+00:03:13,560 --> 00:03:16,920
+that's included in s decisions
+
+81
+00:03:17,280 --> 00:03:21,280
+that
+
+82
+00:03:19,040 --> 00:03:22,640
+so um the first thing I'd like to talk
+
+83
+00:03:21,280 --> 00:03:25,280
+about before I get into any of the
+
+84
+00:03:22,640 --> 00:03:28,000
+actual details is um open versus closed
+
+85
+00:03:25,280 --> 00:03:30,480
+access uh this is not like modeling
+
+86
+00:03:28,000 --> 00:03:31,760
+stuff but I think it's important and
+
+87
+00:03:30,480 --> 00:03:35,599
+also helps you understand the
+
+88
+00:03:31,760 --> 00:03:39,519
+environment a little bit so um there's a
+
+89
+00:03:35,599 --> 00:03:42,200
+nice blog by pyang and others uh at
+
+90
+00:03:39,519 --> 00:03:45,560
+which is also in the reference and they
+
+91
+00:03:42,200 --> 00:03:47,720
+discuss several different varieties of
+
+92
+00:03:45,560 --> 00:03:50,599
+like openness of release of language
+
+93
+00:03:47,720 --> 00:03:52,560
+models in advanced AI systems and there
+
+94
+00:03:50,599 --> 00:03:55,200
+are some things that we can talk about
+
+95
+00:03:52,560 --> 00:03:59,000
+we can talk about the weights being open
+
+96
+00:03:55,200 --> 00:04:01,439
+um described or closed inference uh code
+
+97
+00:03:59,000 --> 00:04:03,319
+being open or inference methods being
+
+98
+00:04:01,439 --> 00:04:04,959
+described or it being fully closed
+
+99
+00:04:03,319 --> 00:04:08,120
+training being open described or closed
+
+100
+00:04:04,959 --> 00:04:13,040
+and data being open described or closed
+
+101
+00:04:08,120 --> 00:04:14,760
+and um in general uh we have like the
+
+102
+00:04:13,040 --> 00:04:16,519
+open weights models that are on hugging
+
+103
+00:04:14,760 --> 00:04:19,040
+face that might just mean the weights
+
+104
+00:04:16,519 --> 00:04:20,600
+are open the inference code also needs
+
+105
+00:04:19,040 --> 00:04:21,919
+to be open because otherwise you can't
+
+106
+00:04:20,600 --> 00:04:24,160
+do inference on them if they're on
+
+107
+00:04:21,919 --> 00:04:25,800
+hugging face but that doesn't mean that
+
+108
+00:04:24,160 --> 00:04:28,120
+the training code is open it also
+
+109
+00:04:25,800 --> 00:04:32,479
+doesn't mean that the data is open um
+
+110
+00:04:28,120 --> 00:04:34,280
+and so there's various degrees of
+
+111
+00:04:32,479 --> 00:04:37,320
+openness
+
+112
+00:04:34,280 --> 00:04:40,919
+um and then of course there are things
+
+113
+00:04:37,320 --> 00:04:42,520
+like uh GPT for or GPT models where
+
+114
+00:04:40,919 --> 00:04:45,560
+basically all of this is closed and we
+
+115
+00:04:42,520 --> 00:04:48,880
+don't know anything about it or know
+
+116
+00:04:45,560 --> 00:04:50,560
+very little about it another thing is
+
+117
+00:04:48,880 --> 00:04:52,600
+about licenses and
+
+118
+00:04:50,560 --> 00:04:54,199
+permissiveness and this is kind of
+
+119
+00:04:52,600 --> 00:04:56,880
+important if you want to do a research
+
+120
+00:04:54,199 --> 00:05:01,240
+project to know because
+
+121
+00:04:56,880 --> 00:05:04,080
+it means it it an impact on the things
+
+122
+00:05:01,240 --> 00:05:05,520
+that you legally can do or can't do in
+
+123
+00:05:04,080 --> 00:05:08,039
+universities I mean we should be
+
+124
+00:05:05,520 --> 00:05:09,479
+following the law but we're maybe people
+
+125
+00:05:08,039 --> 00:05:10,720
+think about this a little bit less if
+
+126
+00:05:09,479 --> 00:05:12,240
+you're in a big company this is
+
+127
+00:05:10,720 --> 00:05:14,919
+something that becomes really important
+
+128
+00:05:12,240 --> 00:05:17,199
+so it's uh it's important to think
+
+129
+00:05:14,919 --> 00:05:20,039
+about so I'm going to go through several
+
+130
+00:05:17,199 --> 00:05:21,440
+degrees of licenses uh that if you've
+
+131
+00:05:20,039 --> 00:05:25,759
+done anything in open source you
+
+132
+00:05:21,440 --> 00:05:27,600
+probably know but um the or you probably
+
+133
+00:05:25,759 --> 00:05:29,919
+know a lot of these the first one is
+
+134
+00:05:27,600 --> 00:05:31,479
+public domain or cc0
+
+135
+00:05:29,919 --> 00:05:33,440
+and this basically means you can do
+
+136
+00:05:31,479 --> 00:05:37,240
+anything with it like I could I could
+
+137
+00:05:33,440 --> 00:05:39,280
+download it and um this includes the
+
+138
+00:05:37,240 --> 00:05:41,680
+download it and redistribute it not give
+
+139
+00:05:39,280 --> 00:05:44,560
+you any credit uh modify it in any way I
+
+140
+00:05:41,680 --> 00:05:47,720
+want and this includes things like old
+
+141
+00:05:44,560 --> 00:05:49,600
+copyrighted works and products of the US
+
+142
+00:05:47,720 --> 00:05:51,400
+government workers so if you work for
+
+143
+00:05:49,600 --> 00:05:53,240
+the US government in some capacities
+
+144
+00:05:51,400 --> 00:05:58,560
+anything you generate becomes public
+
+145
+00:05:53,240 --> 00:06:01,000
+domain um so old copyrighted Works um
+
+146
+00:05:58,560 --> 00:06:04,560
+How how old do you think they need to be
+
+147
+00:06:01,000 --> 00:06:04,560
+before they become uh
+
+148
+00:06:04,720 --> 00:06:12,280
+uncopyrighted
+
+149
+00:06:07,000 --> 00:06:12,280
+yeah uh I think that's pretty close
+
+150
+00:06:14,319 --> 00:06:21,280
+so it's uh 70 years I
+
+151
+00:06:18,520 --> 00:06:23,680
+guess oh sorry the life of the author
+
+152
+00:06:21,280 --> 00:06:25,120
+plus an additional 70 years so like
+
+153
+00:06:23,680 --> 00:06:28,479
+after the after the person has passed
+
+154
+00:06:25,120 --> 00:06:30,720
+away 70 years I guess it says um does
+
+155
+00:06:28,479 --> 00:06:34,520
+anyone know a work that just become
+
+156
+00:06:30,720 --> 00:06:37,520
+became non-copyrighted yeah uh Mickey
+
+157
+00:06:34,520 --> 00:06:43,199
+Mouse is still copyrighted
+
+158
+00:06:37,520 --> 00:06:45,199
+yeah SBO uh did did it I okay so that
+
+159
+00:06:43,199 --> 00:06:48,400
+that's some new news some other new news
+
+160
+00:06:45,199 --> 00:06:50,759
+is wi the Poo um so Winnie the Poo just
+
+161
+00:06:48,400 --> 00:06:54,199
+became non-copyrighted and actually I
+
+162
+00:06:50,759 --> 00:06:55,840
+just heard uh last week that somebody
+
+163
+00:06:54,199 --> 00:06:59,680
+made a horror movie where Winnie the
+
+164
+00:06:55,840 --> 00:07:01,479
+Pooh was a a killer and that one uh a
+
+165
+00:06:59,680 --> 00:07:04,960
+whole bunch of like bad movie awards in
+
+166
+00:07:01,479 --> 00:07:06,639
+2023 so um that's the kind of things
+
+167
+00:07:04,960 --> 00:07:09,080
+that can happen to your copyrighted
+
+168
+00:07:06,639 --> 00:07:11,479
+works if they are released cc0 somebody
+
+169
+00:07:09,080 --> 00:07:12,960
+can do anything they want with them uh
+
+170
+00:07:11,479 --> 00:07:14,400
+you know so you need to be a little bit
+
+171
+00:07:12,960 --> 00:07:18,080
+careful about
+
+172
+00:07:14,400 --> 00:07:20,000
+that um next are MIT and bstd these are
+
+173
+00:07:18,080 --> 00:07:22,400
+very common software licenses you'll see
+
+174
+00:07:20,000 --> 00:07:25,720
+them on a lot of research projects these
+
+175
+00:07:22,400 --> 00:07:27,400
+have very few restrictions um other than
+
+176
+00:07:25,720 --> 00:07:29,319
+maybe maintaining the copyright notice
+
+177
+00:07:27,400 --> 00:07:31,840
+for BC but that's about it you can do
+
+178
+00:07:29,319 --> 00:07:33,840
+just about anything you want with it um
+
+179
+00:07:31,840 --> 00:07:35,599
+actually I'm not sure if people know
+
+180
+00:07:33,840 --> 00:07:39,599
+this but the Mac operating system is
+
+181
+00:07:35,599 --> 00:07:42,199
+based on an old BSD Opera uh operating
+
+182
+00:07:39,599 --> 00:07:44,280
+system where they uh took the they took
+
+183
+00:07:42,199 --> 00:07:46,080
+the code they made it private they
+
+184
+00:07:44,280 --> 00:07:49,560
+forked it made it private and now it's
+
+185
+00:07:46,080 --> 00:07:51,919
+the proprietary Mac operating system so
+
+186
+00:07:49,560 --> 00:07:53,720
+uh that's something you can do with an m
+
+187
+00:07:51,919 --> 00:07:57,840
+m or BSD
+
+188
+00:07:53,720 --> 00:08:00,000
+licensed um there's also a Pachi and CC
+
+189
+00:07:57,840 --> 00:08:02,560
+by um
+
+190
+00:08:00,000 --> 00:08:05,039
+here you must acknowledge the owner of
+
+191
+00:08:02,560 --> 00:08:07,840
+the uh the original creators so you need
+
+192
+00:08:05,039 --> 00:08:08,960
+to say this person actually created uh
+
+193
+00:08:07,840 --> 00:08:11,520
+this
+
+194
+00:08:08,960 --> 00:08:14,680
+originally
+
+195
+00:08:11,520 --> 00:08:17,319
+um Apachi is also kind of interesting
+
+196
+00:08:14,680 --> 00:08:21,759
+because they will give you a license to
+
+197
+00:08:17,319 --> 00:08:25,960
+use that code and any patents that are
+
+198
+00:08:21,759 --> 00:08:29,599
+associated with that code unless you sue
+
+199
+00:08:25,960 --> 00:08:32,159
+the company who released it so um just
+
+200
+00:08:29,599 --> 00:08:34,039
+Give an example let's say uh Google
+
+201
+00:08:32,159 --> 00:08:36,279
+released their code under the Apache
+
+202
+00:08:34,039 --> 00:08:38,919
+license and that code implements
+
+203
+00:08:36,279 --> 00:08:42,680
+Transformers and Google has a patent on
+
+204
+00:08:38,919 --> 00:08:45,760
+Transformers so if you use uh kind of
+
+205
+00:08:42,680 --> 00:08:48,200
+jacks or tensorflow a Jack or tensorflow
+
+206
+00:08:45,760 --> 00:08:50,120
+implementation of Transformers uh that
+
+207
+00:08:48,200 --> 00:08:51,720
+was created by Google you're okay you're
+
+208
+00:08:50,120 --> 00:08:54,640
+safe to use that because they've
+
+209
+00:08:51,720 --> 00:08:57,360
+released it under uh under that license
+
+210
+00:08:54,640 --> 00:08:59,560
+but if you sue Google uh for anything
+
+211
+00:08:57,360 --> 00:09:01,760
+related to intellectual property Google
+
+212
+00:08:59,560 --> 00:09:04,480
+could say uh don't you can't use
+
+213
+00:09:01,760 --> 00:09:06,040
+Transformers anymore um and so like if
+
+214
+00:09:04,480 --> 00:09:08,279
+open AI ever sues Google for
+
+215
+00:09:06,040 --> 00:09:09,680
+intellectual property infringement
+
+216
+00:09:08,279 --> 00:09:12,120
+Google will say okay you can't use
+
+217
+00:09:09,680 --> 00:09:15,959
+Transformers or word embeddings good
+
+218
+00:09:12,120 --> 00:09:17,640
+luck uh open so um there's this
+
+219
+00:09:15,959 --> 00:09:20,760
+interesting thing where all of these uh
+
+220
+00:09:17,640 --> 00:09:22,760
+tech companies now are using patented um
+
+221
+00:09:20,760 --> 00:09:24,440
+patented things a lot of it apachi
+
+222
+00:09:22,760 --> 00:09:26,040
+license software and so none of them can
+
+223
+00:09:24,440 --> 00:09:28,959
+sue each other for patents so patents
+
+224
+00:09:26,040 --> 00:09:30,560
+have become basically mostly worthless
+
+225
+00:09:28,959 --> 00:09:35,320
+uh in big
+
+226
+00:09:30,560 --> 00:09:36,360
+te um moving on um there's also a g GPL
+
+227
+00:09:35,320 --> 00:09:39,360
+in
+
+228
+00:09:36,360 --> 00:09:42,800
+ccbsa these are licenses where if you
+
+229
+00:09:39,360 --> 00:09:45,680
+use them you need to reshare under that
+
+230
+00:09:42,800 --> 00:09:47,839
+license um and so like if you create
+
+231
+00:09:45,680 --> 00:09:49,440
+some software it's GPL licensed and you
+
+232
+00:09:47,839 --> 00:09:52,160
+build on it and build something new you
+
+233
+00:09:49,440 --> 00:09:54,839
+need to release it under the GPL license
+
+234
+00:09:52,160 --> 00:09:58,160
+so a lot of companies will not
+
+235
+00:09:54,839 --> 00:09:59,640
+use um will not use GPL software because
+
+236
+00:09:58,160 --> 00:10:01,920
+that would mean that if they incorporate
+
+237
+00:09:59,640 --> 00:10:04,959
+into their system their whole system
+
+238
+00:10:01,920 --> 00:10:06,720
+like for example Google uh like all of
+
+239
+00:10:04,959 --> 00:10:10,240
+Google would have to be GPL licensed in
+
+240
+00:10:06,720 --> 00:10:11,720
+Rel EAS uh so um and I'm kind of
+
+241
+00:10:10,240 --> 00:10:14,800
+simplifying these licenses I'm just
+
+242
+00:10:11,720 --> 00:10:17,519
+giving you the gist CC BSA and sorry CC
+
+243
+00:10:14,800 --> 00:10:20,640
+licenses are more for data so MIT BSC
+
+244
+00:10:17,519 --> 00:10:22,640
+Apachi and GPL are more for software CC
+
+245
+00:10:20,640 --> 00:10:27,640
+Creative Commons licenses are for data
+
+246
+00:10:22,640 --> 00:10:29,640
+so um for example Wikipedia is CC by SAA
+
+247
+00:10:27,640 --> 00:10:33,560
+I believe
+
+248
+00:10:29,640 --> 00:10:33,560
+let me make sure that I'm not lying
+
+249
+00:10:41,839 --> 00:10:48,240
+there yeah CC bys and so that means that
+
+250
+00:10:46,040 --> 00:10:52,200
+if you make any derivative work of
+
+251
+00:10:48,240 --> 00:10:54,160
+Wikipedia you need to share it um the
+
+252
+00:10:52,200 --> 00:10:57,040
+same way that Wikipedia is uh so you
+
+253
+00:10:54,160 --> 00:10:59,760
+need to give it the same
+
+254
+00:10:57,040 --> 00:11:01,560
+license there's also um cre of Commons
+
+255
+00:10:59,760 --> 00:11:03,240
+non-commercial licenses or software
+
+256
+00:11:01,560 --> 00:11:05,519
+non-commercial licenses you say you
+
+257
+00:11:03,240 --> 00:11:07,079
+can't use them for commercial purposes
+
+258
+00:11:05,519 --> 00:11:09,279
+all the ones above you can use for
+
+259
+00:11:07,079 --> 00:11:11,519
+commercial purposes once you start
+
+260
+00:11:09,279 --> 00:11:13,440
+getting down here this is no often no
+
+261
+00:11:11,519 --> 00:11:15,279
+longer called open source so the open
+
+262
+00:11:13,440 --> 00:11:16,959
+source initiative says anything with a
+
+263
+00:11:15,279 --> 00:11:19,839
+restriction on the way that you can use
+
+264
+00:11:16,959 --> 00:11:22,639
+it is no longer open source and so that
+
+265
+00:11:19,839 --> 00:11:25,360
+means if you say you can't use this for
+
+266
+00:11:22,639 --> 00:11:27,720
+commercial purposes or you can't use
+
+267
+00:11:25,360 --> 00:11:29,639
+this in military systems for example
+
+268
+00:11:27,720 --> 00:11:32,320
+which some language models say that
+
+269
+00:11:29,639 --> 00:11:33,680
+nowadays those are no longer called open
+
+270
+00:11:32,320 --> 00:11:37,040
+source according to the open source
+
+271
+00:11:33,680 --> 00:11:40,320
+initiative so that's a thing to know
+
+272
+00:11:37,040 --> 00:11:42,920
+about then separately uh there are these
+
+273
+00:11:40,320 --> 00:11:45,279
+licenses that a lot of people like meta
+
+274
+00:11:42,920 --> 00:11:48,160
+or hugging face come up with for their
+
+275
+00:11:45,279 --> 00:11:50,360
+um for their models recently so the
+
+276
+00:11:48,160 --> 00:11:51,320
+Llama license um how many people are
+
+277
+00:11:50,360 --> 00:11:54,200
+using
+
+278
+00:11:51,320 --> 00:11:56,519
+llama in your projects how many people
+
+279
+00:11:54,200 --> 00:11:56,519
+read the
+
+280
+00:11:57,000 --> 00:12:00,880
+license so um are you sure you can use
+
+281
+00:11:59,639 --> 00:12:04,959
+it in your
+
+282
+00:12:00,880 --> 00:12:06,839
+project uh so you're you're probably in
+
+283
+00:12:04,959 --> 00:12:09,000
+luck in your project if you're using it
+
+284
+00:12:06,839 --> 00:12:11,560
+the Lama license you can read into it to
+
+285
+00:12:09,000 --> 00:12:13,519
+see what it actually allows but it has
+
+286
+00:12:11,560 --> 00:12:16,399
+um the original llama license has some
+
+287
+00:12:13,519 --> 00:12:18,440
+interesting uh things number one you
+
+288
+00:12:16,399 --> 00:12:21,079
+cannot use llama to train any language
+
+289
+00:12:18,440 --> 00:12:23,000
+model that is not derived from llama so
+
+290
+00:12:21,079 --> 00:12:26,120
+you can't generate data from llama in
+
+291
+00:12:23,000 --> 00:12:30,040
+train M that's not allowed according to
+
+292
+00:12:26,120 --> 00:12:32,440
+the r Li um another thing is uh you
+
+293
+00:12:30,040 --> 00:12:34,680
+can't use it for military purposes so
+
+294
+00:12:32,440 --> 00:12:36,160
+you can't use it um in building a
+
+295
+00:12:34,680 --> 00:12:37,639
+missile system or something like that
+
+296
+00:12:36,160 --> 00:12:41,440
+hopefully none of you are doing that for
+
+297
+00:12:37,639 --> 00:12:42,920
+your project um and you also need to get
+
+298
+00:12:41,440 --> 00:12:45,399
+a license from meta if you have
+
+299
+00:12:42,920 --> 00:12:48,000
+something more than 300 million active
+
+300
+00:12:45,399 --> 00:12:53,800
+user asign your social network service
+
+301
+00:12:48,000 --> 00:12:56,079
+so if you're Google or um you know X or
+
+302
+00:12:53,800 --> 00:12:57,680
+Twitter or you know whatever else you
+
+303
+00:12:56,079 --> 00:13:00,519
+need to get a license for meta before
+
+304
+00:12:57,680 --> 00:13:02,079
+you can start using one so
+
+305
+00:13:00,519 --> 00:13:03,240
+basically they created that license so
+
+306
+00:13:02,079 --> 00:13:06,720
+their competitors don't take their
+
+307
+00:13:03,240 --> 00:13:08,959
+language model and just use it for free
+
+308
+00:13:06,720 --> 00:13:11,000
+um and then the final thing is no
+
+309
+00:13:08,959 --> 00:13:13,240
+license so like let's say you have some
+
+310
+00:13:11,000 --> 00:13:15,560
+code that you upload to GitHub and you
+
+311
+00:13:13,240 --> 00:13:17,839
+don't put a license on your code this
+
+312
+00:13:15,560 --> 00:13:20,880
+means that you have only agreed to the
+
+313
+00:13:17,839 --> 00:13:23,360
+GitHub licensing terms which means that
+
+314
+00:13:20,880 --> 00:13:26,199
+actually nobody can use their code they
+
+315
+00:13:23,360 --> 00:13:30,079
+can view it possibly but they can't you
+
+316
+00:13:26,199 --> 00:13:31,720
+download it use it they can't like um
+
+317
+00:13:30,079 --> 00:13:34,160
+they can't incorporate it into their own
+
+318
+00:13:31,720 --> 00:13:36,000
+system so actually if you release
+
+319
+00:13:34,160 --> 00:13:39,120
+research code I would highly encourage
+
+320
+00:13:36,000 --> 00:13:41,120
+you to use MIT or BSD um or one of these
+
+321
+00:13:39,120 --> 00:13:43,040
+permissive licenses so other people can
+
+322
+00:13:41,120 --> 00:13:45,720
+use it and follow up and your code can
+
+323
+00:13:43,040 --> 00:13:46,920
+be effectful so um this is an important
+
+324
+00:13:45,720 --> 00:13:49,040
+thing to know about there's obviously
+
+325
+00:13:46,920 --> 00:13:52,959
+lots more to know
+
+326
+00:13:49,040 --> 00:13:56,440
+about um so then my question my next
+
+327
+00:13:52,959 --> 00:13:57,360
+question is uh what is most of the text
+
+328
+00:13:56,440 --> 00:13:59,560
+on the
+
+329
+00:13:57,360 --> 00:14:01,160
+internet the majority of the text on the
+
+330
+00:13:59,560 --> 00:14:04,839
+internet falls into one of these
+
+331
+00:14:01,160 --> 00:14:04,839
+categories any idea which
+
+332
+00:14:05,120 --> 00:14:12,759
+one so Wikipedia is CC bya what what
+
+333
+00:14:09,040 --> 00:14:12,759
+about uh Mo most of the text
+
+334
+00:14:14,199 --> 00:14:18,959
+on yeah it's not maybe not no license
+
+335
+00:14:16,880 --> 00:14:21,680
+but all rights reserved so basically you
+
+336
+00:14:18,959 --> 00:14:23,079
+can't use it without having permission
+
+337
+00:14:21,680 --> 00:14:27,639
+from the copyright
+
+338
+00:14:23,079 --> 00:14:30,639
+holders and so because of that
+
+339
+00:14:27,639 --> 00:14:33,800
+um the idea of fair use becomes very
+
+340
+00:14:30,639 --> 00:14:35,320
+important this is a us specific thing
+
+341
+00:14:33,800 --> 00:14:36,880
+and the rules in other countries are
+
+342
+00:14:35,320 --> 00:14:39,199
+different they're not the same as the us
+
+343
+00:14:36,880 --> 00:14:41,680
+but in the US uh we have rules about
+
+344
+00:14:39,199 --> 00:14:44,600
+where you can use particular types of
+
+345
+00:14:41,680 --> 00:14:46,279
+data so the US fair use Doctrine is
+
+346
+00:14:44,600 --> 00:14:50,240
+basically that you can use copyrighted
+
+347
+00:14:46,279 --> 00:14:52,920
+material in some cases so
+
+348
+00:14:50,240 --> 00:14:56,279
+um as a gross
+
+349
+00:14:52,920 --> 00:15:01,800
+simplification um quoting a small amount
+
+350
+00:14:56,279 --> 00:15:04,320
+of material in like a textbook or slides
+
+351
+00:15:01,800 --> 00:15:07,079
+or something like this this is likely
+
+352
+00:15:04,320 --> 00:15:10,040
+okay um there are going to be very few
+
+353
+00:15:07,079 --> 00:15:11,399
+cases where this is not going to um you
+
+354
+00:15:10,040 --> 00:15:12,720
+know where you're going to get in
+
+355
+00:15:11,399 --> 00:15:15,600
+trouble for
+
+356
+00:15:12,720 --> 00:15:18,000
+this another important uh judgment
+
+357
+00:15:15,600 --> 00:15:19,600
+criteria for whether this is fair use is
+
+358
+00:15:18,000 --> 00:15:22,440
+that it doesn't diminish the value of
+
+359
+00:15:19,600 --> 00:15:25,120
+the original work so if I quote
+
+360
+00:15:22,440 --> 00:15:27,759
+something in my like let's say I quoted
+
+361
+00:15:25,120 --> 00:15:30,839
+all of Harry Potter in a textbook and
+
+362
+00:15:27,759 --> 00:15:32,600
+then I sold my textbook for $3 anybody
+
+363
+00:15:30,839 --> 00:15:34,279
+could take my textbook and read all of
+
+364
+00:15:32,600 --> 00:15:35,800
+Harry Potter for $3 and the money
+
+365
+00:15:34,279 --> 00:15:37,480
+wouldn't go to JK rolling and that would
+
+366
+00:15:35,800 --> 00:15:41,040
+not be fair use because it's diminishing
+
+367
+00:15:37,480 --> 00:15:42,920
+the value of similarly if I create a big
+
+368
+00:15:41,040 --> 00:15:44,319
+Corpus of books and I upload them to a
+
+369
+00:15:42,920 --> 00:15:46,079
+site where anyone can browse them that
+
+370
+00:15:44,319 --> 00:15:48,319
+would also probably not be for use
+
+371
+00:15:46,079 --> 00:15:49,160
+because the authors would not get paid
+
+372
+00:15:48,319 --> 00:15:52,319
+for
+
+373
+00:15:49,160 --> 00:15:54,480
+it another judgment Criterion is whether
+
+374
+00:15:52,319 --> 00:15:57,399
+it's for non commercial purposes or not
+
+375
+00:15:54,480 --> 00:15:59,639
+so like in universities we're actually
+
+376
+00:15:57,399 --> 00:16:01,120
+held to a probably held to a more
+
+377
+00:15:59,639 --> 00:16:03,000
+lenient standard of fa use if we're
+
+378
+00:16:01,120 --> 00:16:06,120
+doing non-commercial research compared
+
+379
+00:16:03,000 --> 00:16:08,519
+to a company that's doing it
+
+380
+00:16:06,120 --> 00:16:11,480
+so um most data on the Internet is
+
+381
+00:16:08,519 --> 00:16:13,279
+copyrighted so right now most model
+
+382
+00:16:11,480 --> 00:16:16,240
+training not all model training but most
+
+383
+00:16:13,279 --> 00:16:18,680
+model training is done um assuming fair
+
+384
+00:16:16,240 --> 00:16:21,800
+use which means that training an AI
+
+385
+00:16:18,680 --> 00:16:25,800
+model on copyrighted
+
+386
+00:16:21,800 --> 00:16:29,480
+data is number one it cannot reproduce
+
+387
+00:16:25,800 --> 00:16:32,240
+the material easily so it's instead of
+
+388
+00:16:29,480 --> 00:16:33,600
+quoting material directly it's kind of
+
+389
+00:16:32,240 --> 00:16:35,880
+combining the material together to
+
+390
+00:16:33,600 --> 00:16:37,519
+create a new thing they're saying it
+
+391
+00:16:35,880 --> 00:16:40,639
+doesn't diminish the commercial value of
+
+392
+00:16:37,519 --> 00:16:42,360
+the original uh data um and then the
+
+393
+00:16:40,639 --> 00:16:44,839
+non-commercial purposes is maybe a
+
+394
+00:16:42,360 --> 00:16:47,240
+secondary concern since the first two
+
+395
+00:16:44,839 --> 00:16:50,600
+hold um but there are lawsuits about
+
+396
+00:16:47,240 --> 00:16:52,360
+this and so um this is a clip from The
+
+397
+00:16:50,600 --> 00:16:55,560
+New York Times where the New York Times
+
+398
+00:16:52,360 --> 00:16:58,279
+is suing open AI in Microsoft over uh
+
+399
+00:16:55,560 --> 00:16:59,759
+them training on New York Times articles
+
+400
+00:16:58,279 --> 00:17:02,040
+and they did do a lot of things like
+
+401
+00:16:59,759 --> 00:17:05,799
+they demonstrate that you can get uh gp4
+
+402
+00:17:02,040 --> 00:17:08,319
+to reproduce uh like um New York Times
+
+403
+00:17:05,799 --> 00:17:11,480
+articles and they also argue that people
+
+404
+00:17:08,319 --> 00:17:12,880
+are using this gp4 as a source of news
+
+405
+00:17:11,480 --> 00:17:14,079
+instead of going to the New York Times
+
+406
+00:17:12,880 --> 00:17:15,959
+site so they're losing money from
+
+407
+00:17:14,079 --> 00:17:19,199
+advertising and like other other things
+
+408
+00:17:15,959 --> 00:17:21,679
+like that um another example is GitHub
+
+409
+00:17:19,199 --> 00:17:24,000
+co-pilot was sued by people who uh
+
+410
+00:17:21,679 --> 00:17:26,439
+uploaded software to GitHub and said
+
+411
+00:17:24,000 --> 00:17:29,039
+that uh basically GitHub didn't have the
+
+412
+00:17:26,439 --> 00:17:32,400
+right to use it to profit from it and
+
+413
+00:17:29,039 --> 00:17:34,799
+diminish their uh you know their money
+
+414
+00:17:32,400 --> 00:17:37,520
+so notably uh on this slide I'm using
+
+415
+00:17:34,799 --> 00:17:42,039
+fair use I don't know if you've noticed
+
+416
+00:17:37,520 --> 00:17:44,679
+like I copy I copy pasted an image from
+
+417
+00:17:42,039 --> 00:17:46,360
+somebody's uh you know website and used
+
+418
+00:17:44,679 --> 00:17:48,520
+it here that's copyrighted material but
+
+419
+00:17:46,360 --> 00:17:49,640
+I'm using it because I'm quoting a small
+
+420
+00:17:48,520 --> 00:17:52,440
+amount of material and I'm not
+
+421
+00:17:49,640 --> 00:17:54,360
+diminishing the ostial values so um like
+
+422
+00:17:52,440 --> 00:17:56,320
+fair use is very ubiquitous it's very
+
+423
+00:17:54,360 --> 00:17:58,480
+important so we can do things like this
+
+424
+00:17:56,320 --> 00:18:00,840
+but also um it's currently under thep
+
+425
+00:17:58,480 --> 00:18:00,840
+with this
+
+426
+00:18:01,280 --> 00:18:07,799
+models so then another question is why
+
+427
+00:18:04,360 --> 00:18:12,520
+restrict model access why do we number
+
+428
+00:18:07,799 --> 00:18:14,320
+one make models closed number two um you
+
+429
+00:18:12,520 --> 00:18:16,159
+know maybe not even describe what we did
+
+430
+00:18:14,320 --> 00:18:18,880
+in our models and I think there's three
+
+431
+00:18:16,159 --> 00:18:21,360
+main reasons the first reason is
+
+432
+00:18:18,880 --> 00:18:23,480
+commercial concerns and so they want to
+
+433
+00:18:21,360 --> 00:18:25,760
+make money from the models so open AI
+
+434
+00:18:23,480 --> 00:18:27,520
+makes money from the open AI API Gemini
+
+435
+00:18:25,760 --> 00:18:29,480
+makes uh sorry Google makes money from
+
+436
+00:18:27,520 --> 00:18:31,799
+the Gemini API
+
+437
+00:18:29,480 --> 00:18:33,720
+um and anthropic makes money from the
+
+438
+00:18:31,799 --> 00:18:34,760
+CLA API these are all models that I'm
+
+439
+00:18:33,720 --> 00:18:37,640
+going to talk
+
+440
+00:18:34,760 --> 00:18:39,440
+about number two safety I I think there
+
+441
+00:18:37,640 --> 00:18:41,640
+are very legitimate concerns where if
+
+442
+00:18:39,440 --> 00:18:43,840
+you release strong models people might
+
+443
+00:18:41,640 --> 00:18:47,200
+use them for bad things so you know
+
+444
+00:18:43,840 --> 00:18:49,120
+creating fake content online or uh doing
+
+445
+00:18:47,200 --> 00:18:50,720
+spear fishing attacks against people and
+
+446
+00:18:49,120 --> 00:18:52,600
+trying to you know scam them out of
+
+447
+00:18:50,720 --> 00:18:55,600
+money or things like that so I think
+
+448
+00:18:52,600 --> 00:18:57,240
+there are legitimate concerns about this
+
+449
+00:18:55,600 --> 00:18:58,880
+and then the final one is legal
+
+450
+00:18:57,240 --> 00:19:01,520
+liability so training models on
+
+451
+00:18:58,880 --> 00:19:03,640
+copyrighted data is a legal gray area as
+
+452
+00:19:01,520 --> 00:19:05,159
+I just mentioned so they don't want to
+
+453
+00:19:03,640 --> 00:19:07,159
+say what data they trained on because if
+
+454
+00:19:05,159 --> 00:19:10,240
+they say what data they trained on then
+
+455
+00:19:07,159 --> 00:19:11,960
+they might get sued so these are the
+
+456
+00:19:10,240 --> 00:19:14,960
+three main
+
+457
+00:19:11,960 --> 00:19:17,960
+concerns so
+
+458
+00:19:14,960 --> 00:19:19,480
+um anyway this this is a preface and
+
+459
+00:19:17,960 --> 00:19:23,360
+then I want to go into like the actual
+
+460
+00:19:19,480 --> 00:19:23,360
+models but are there any questions about
+
+461
+00:19:24,679 --> 00:19:30,280
+this so if any of you
+
+462
+00:19:27,280 --> 00:19:31,720
+are working at a company or starting a
+
+463
+00:19:30,280 --> 00:19:33,120
+company thinking about working at a
+
+464
+00:19:31,720 --> 00:19:35,440
+company or starting a company this is
+
+465
+00:19:33,120 --> 00:19:37,320
+something you should be aware of um you
+
+466
+00:19:35,440 --> 00:19:39,720
+should also be aware of the fact that
+
+467
+00:19:37,320 --> 00:19:42,360
+you know open AI has been doing sketchy
+
+468
+00:19:39,720 --> 00:19:46,640
+things for a long time and look where
+
+469
+00:19:42,360 --> 00:19:48,440
+they are so you know it it's uh like
+
+470
+00:19:46,640 --> 00:19:51,400
+this is very much a legal gray area and
+
+471
+00:19:48,440 --> 00:19:53,880
+people are are uh moving through that
+
+472
+00:19:51,400 --> 00:19:55,640
+gray area but anyway it's worth knowing
+
+473
+00:19:53,880 --> 00:19:59,480
+that so next I'm going to talk about
+
+474
+00:19:55,640 --> 00:20:00,679
+open models um so first bird's eye view
+
+475
+00:19:59,480 --> 00:20:02,600
+I'm going to talk about five different
+
+476
+00:20:00,679 --> 00:20:04,080
+models and I picked them for a reason
+
+477
+00:20:02,600 --> 00:20:06,440
+the first two are because they're open
+
+478
+00:20:04,080 --> 00:20:08,159
+source and fully reproducible namely
+
+479
+00:20:06,440 --> 00:20:10,360
+pipia
+
+480
+00:20:08,159 --> 00:20:11,919
+Ino and the reason why I want to talk
+
+481
+00:20:10,360 --> 00:20:13,120
+about these is we know everything about
+
+482
+00:20:11,919 --> 00:20:14,679
+them including what data they were
+
+483
+00:20:13,120 --> 00:20:16,799
+trained on um what their training
+
+484
+00:20:14,679 --> 00:20:19,080
+procedures are you can download all the
+
+485
+00:20:16,799 --> 00:20:21,000
+the stuff so you can kind of know uh
+
+486
+00:20:19,080 --> 00:20:24,840
+exactly what goes into making a strong
+
+487
+00:20:21,000 --> 00:20:26,520
+model um Pia is uh actually has many
+
+488
+00:20:24,840 --> 00:20:28,159
+sizes in checkpoints which is pretty
+
+489
+00:20:26,520 --> 00:20:30,919
+interesting Ando is maybe the strongest
+
+490
+00:20:28,159 --> 00:20:32,559
+reproduced model at the moment um then
+
+491
+00:20:30,919 --> 00:20:34,120
+we have open weights models and these
+
+492
+00:20:32,559 --> 00:20:35,520
+are models that aren't fully open they
+
+493
+00:20:34,120 --> 00:20:38,679
+don't disclose everything they don't
+
+494
+00:20:35,520 --> 00:20:40,760
+release their training data uh or
+
+495
+00:20:38,679 --> 00:20:43,799
+code um but I'm going to talk about
+
+496
+00:20:40,760 --> 00:20:46,520
+llama 2 which is the most popular um
+
+497
+00:20:43,799 --> 00:20:48,280
+it's also heavily safety tuned mistol
+
+498
+00:20:46,520 --> 00:20:50,840
+and mixol which is a strong and fast
+
+499
+00:20:48,280 --> 00:20:53,200
+model um it's somewhat multilingual and
+
+500
+00:20:50,840 --> 00:20:55,200
+also quen which is a very uh strong
+
+501
+00:20:53,200 --> 00:20:57,520
+model it's more multilingual and
+
+502
+00:20:55,200 --> 00:21:00,600
+specifically it's good in English and
+
+503
+00:20:57,520 --> 00:21:03,440
+Chinese because it was train down of
+
+504
+00:21:00,600 --> 00:21:04,720
+that so first going into Pia for each of
+
+505
+00:21:03,440 --> 00:21:06,159
+them I'm going to give an overview and
+
+506
+00:21:04,720 --> 00:21:08,880
+then talk about some interesting points
+
+507
+00:21:06,159 --> 00:21:12,320
+about them so pythia was created by
+
+508
+00:21:08,880 --> 00:21:14,799
+alther ai alther ai is one of the first
+
+509
+00:21:12,320 --> 00:21:16,279
+um kind of open- source AI organizations
+
+510
+00:21:14,799 --> 00:21:18,720
+they've created a huge number of really
+
+511
+00:21:16,279 --> 00:21:21,480
+useful things including training code
+
+512
+00:21:18,720 --> 00:21:25,279
+models training data sets and also
+
+513
+00:21:21,480 --> 00:21:28,080
+evaluation that's used pretty widely um
+
+514
+00:21:25,279 --> 00:21:29,760
+the goal of pythia was basically joint
+
+515
+00:21:28,080 --> 00:21:32,159
+understanding model training Dynamics
+
+516
+00:21:29,760 --> 00:21:36,320
+and scaling and so from that point of
+
+517
+00:21:32,159 --> 00:21:39,120
+view um they released eight model sizes
+
+518
+00:21:36,320 --> 00:21:41,880
+from 70 million parameters to 12 billion
+
+519
+00:21:39,120 --> 00:21:44,960
+parameters for each model size they have
+
+520
+00:21:41,880 --> 00:21:47,440
+154 checkpoints throughout the training
+
+521
+00:21:44,960 --> 00:21:52,880
+process um so they basically trained on
+
+522
+00:21:47,440 --> 00:21:55,960
+uh 3300 billion uh parameter uh tokens
+
+523
+00:21:52,880 --> 00:21:57,400
+and uh did checkpoints you know
+
+524
+00:21:55,960 --> 00:21:59,000
+periodically during that training
+
+525
+00:21:57,400 --> 00:22:02,400
+process so you can do interest things
+
+526
+00:21:59,000 --> 00:22:04,400
+like say uh how quickly do small models
+
+527
+00:22:02,400 --> 00:22:06,919
+learn things how quickly do large models
+
+528
+00:22:04,400 --> 00:22:09,480
+learn things and other stuff like
+
+529
+00:22:06,919 --> 00:22:10,760
+that in terms of the architecture as I
+
+530
+00:22:09,480 --> 00:22:12,760
+mentioned at the very beginning the
+
+531
+00:22:10,760 --> 00:22:14,799
+architectures are actually very similar
+
+532
+00:22:12,760 --> 00:22:17,840
+between them so it's almost easier to
+
+533
+00:22:14,799 --> 00:22:21,080
+point out their differences than uh
+
+534
+00:22:17,840 --> 00:22:22,559
+their like their similarities um
+
+535
+00:22:21,080 --> 00:22:25,400
+actually one thing that's not on the
+
+536
+00:22:22,559 --> 00:22:27,159
+slide is um I mainly focused on the
+
+537
+00:22:25,400 --> 00:22:29,080
+seven billion models because almost
+
+538
+00:22:27,159 --> 00:22:30,320
+everybody trains a seven billi model
+
+539
+00:22:29,080 --> 00:22:32,720
+it's just kind of like one of the
+
+540
+00:22:30,320 --> 00:22:34,640
+standard sizes it's the smallest size of
+
+541
+00:22:32,720 --> 00:22:36,559
+llama it's one of the largest it's the
+
+542
+00:22:34,640 --> 00:22:40,240
+largest size ofo and one of the largest
+
+543
+00:22:36,559 --> 00:22:46,880
+sizes of pipon 7 billion models are
+
+544
+00:22:40,240 --> 00:22:52,880
+generally um 4096 wide 32 uh
+
+545
+00:22:46,880 --> 00:22:52,880
+deep uh 32 attention heads and they're
+
+546
+00:22:54,200 --> 00:23:01,159
+um and their um hidden layer size is
+
+547
+00:22:57,400 --> 00:23:04,400
+about like eight3 of the size of this
+
+548
+00:23:01,159 --> 00:23:07,360
+and this is kind of a standard llama 7B
+
+549
+00:23:04,400 --> 00:23:09,240
+architecture um as you scale up to
+
+550
+00:23:07,360 --> 00:23:11,520
+larger sizes you just increase the
+
+551
+00:23:09,240 --> 00:23:13,880
+number of layers you increase the the
+
+552
+00:23:11,520 --> 00:23:16,080
+width and other things like that so
+
+553
+00:23:13,880 --> 00:23:19,039
+that's very standard um the other
+
+554
+00:23:16,080 --> 00:23:21,320
+standard is everybody uses a Transformer
+
+555
+00:23:19,039 --> 00:23:24,440
+um everybody uses pre-layer Norm like I
+
+556
+00:23:21,320 --> 00:23:27,120
+talked about before everybody uses rope
+
+557
+00:23:24,440 --> 00:23:29,520
+eddings um almost everybody uses a swig
+
+558
+00:23:27,120 --> 00:23:30,919
+glue activation so this is just kind of
+
+559
+00:23:29,520 --> 00:23:31,880
+the standard recipe that almost
+
+560
+00:23:30,919 --> 00:23:35,120
+everybody
+
+561
+00:23:31,880 --> 00:23:37,000
+uses um where things start to change a
+
+562
+00:23:35,120 --> 00:23:38,559
+little bit between the architectures
+
+563
+00:23:37,000 --> 00:23:40,559
+which arguably might not be very
+
+564
+00:23:38,559 --> 00:23:44,679
+important is how long is the context
+
+565
+00:23:40,559 --> 00:23:48,320
+length so um pythia is 2K context
+
+566
+00:23:44,679 --> 00:23:51,360
+compared to llama llama 2's 4K context
+
+567
+00:23:48,320 --> 00:23:55,000
+um actually llama 1 is 1K context so
+
+568
+00:23:51,360 --> 00:24:00,000
+Llama Llama Or sorry llama one is 2K
+
+569
+00:23:55,000 --> 00:24:02,120
+context and llama 2 is 4K context um
+
+570
+00:24:00,000 --> 00:24:03,880
+another thing is where do they put
+
+571
+00:24:02,120 --> 00:24:06,240
+biases in the model most people don't
+
+572
+00:24:03,880 --> 00:24:08,200
+use biases uh anywhere but sometimes
+
+573
+00:24:06,240 --> 00:24:09,840
+they put them in various places the
+
+574
+00:24:08,200 --> 00:24:11,919
+other thing is a variety of layer Norm
+
+575
+00:24:09,840 --> 00:24:13,559
+that people use and Pia was using
+
+576
+00:24:11,919 --> 00:24:16,240
+standard parametric layer Norm but
+
+577
+00:24:13,559 --> 00:24:18,000
+gradually people are stepping back from
+
+578
+00:24:16,240 --> 00:24:21,360
+that and they're using like RMS Norm or
+
+579
+00:24:18,000 --> 00:24:22,880
+even nonparametric LMS so um small
+
+580
+00:24:21,360 --> 00:24:25,559
+architecture differences but almost
+
+581
+00:24:22,880 --> 00:24:29,240
+everybody uses something pretty
+
+582
+00:24:25,559 --> 00:24:31,960
+similar um the data this was trained on
+
+583
+00:24:29,240 --> 00:24:34,600
+300 billion tokens of the pile uh which
+
+584
+00:24:31,960 --> 00:24:37,440
+is on the next slide but one interesting
+
+585
+00:24:34,600 --> 00:24:39,000
+thing is that they also did a duplicated
+
+586
+00:24:37,440 --> 00:24:43,320
+training run on
+
+587
+00:24:39,000 --> 00:24:47,679
+270 s billions of the token ah sorry 207
+
+588
+00:24:43,320 --> 00:24:50,559
+billion tokens and um the idea is that
+
+589
+00:24:47,679 --> 00:24:53,039
+they um they wanted to test how
+
+590
+00:24:50,559 --> 00:24:54,919
+important it is to duplicate how much do
+
+591
+00:24:53,039 --> 00:24:56,279
+you gain by D duplicating in terms of
+
+592
+00:24:54,919 --> 00:24:59,559
+training
+
+593
+00:24:56,279 --> 00:25:01,520
+efficiency and um
+
+594
+00:24:59,559 --> 00:25:04,760
+they have different learning rates for
+
+595
+00:25:01,520 --> 00:25:08,640
+different model sizes the 7B model is uh
+
+596
+00:25:04,760 --> 00:25:11,760
+1.2 * e to Theus 4 in contrast llama is
+
+597
+00:25:08,640 --> 00:25:13,120
+3 * eus 4 so this is a potentially big
+
+598
+00:25:11,760 --> 00:25:16,840
+change because the learning rate is
+
+599
+00:25:13,120 --> 00:25:18,880
+actually half the size here um is the
+
+600
+00:25:16,840 --> 00:25:20,559
+batch size they use 2 million tokens and
+
+601
+00:25:18,880 --> 00:25:23,600
+actually llama 2 uses four million
+
+602
+00:25:20,559 --> 00:25:26,520
+tokens for the batch size so um there
+
+603
+00:25:23,600 --> 00:25:29,000
+are some small differences
+
+604
+00:25:26,520 --> 00:25:31,480
+there so next next I'd like to talk
+
+605
+00:25:29,000 --> 00:25:33,760
+about the pile um this is kind of the
+
+606
+00:25:31,480 --> 00:25:36,279
+original open data set for training
+
+607
+00:25:33,760 --> 00:25:37,960
+large language models um that being said
+
+608
+00:25:36,279 --> 00:25:42,159
+it's a really nice data set made out of
+
+609
+00:25:37,960 --> 00:25:47,039
+lots of uh different types of data and
+
+610
+00:25:42,159 --> 00:25:49,960
+namely it's trained on academic data so
+
+611
+00:25:47,039 --> 00:25:52,559
+that includes things like PubMed archive
+
+612
+00:25:49,960 --> 00:25:55,240
+free law the US patent office other
+
+613
+00:25:52,559 --> 00:25:57,000
+stuff like that it's also trained on
+
+614
+00:25:55,240 --> 00:26:00,080
+internet data so this is data that's
+
+615
+00:25:57,000 --> 00:26:02,840
+just scraped from parts of the internet
+
+616
+00:26:00,080 --> 00:26:05,799
+but also stack Exchange in
+
+617
+00:26:02,840 --> 00:26:09,480
+Wikipedia um it also has some pros so
+
+618
+00:26:05,799 --> 00:26:12,200
+these are um like book data sets it has
+
+619
+00:26:09,480 --> 00:26:15,640
+some code data sets and it has some like
+
+620
+00:26:12,200 --> 00:26:18,799
+subtitle dialog data sets in it so this
+
+621
+00:26:15,640 --> 00:26:22,399
+overall is 800 gigabytes or about 300
+
+622
+00:26:18,799 --> 00:26:22,399
+billion tokens according to
+
+623
+00:26:23,360 --> 00:26:28,080
+Tok so some of the findings from the
+
+624
+00:26:25,760 --> 00:26:30,919
+pipia paper in addition to just being
+
+625
+00:26:28,080 --> 00:26:33,399
+like one of the original strong uh open
+
+626
+00:26:30,919 --> 00:26:36,279
+language models is they have some
+
+627
+00:26:33,399 --> 00:26:38,600
+interesting analysis into um model
+
+628
+00:26:36,279 --> 00:26:40,960
+memorization and how quickly models
+
+629
+00:26:38,600 --> 00:26:44,080
+learn uh based on the number of tokens
+
+630
+00:26:40,960 --> 00:26:45,520
+that you show them and this graph is
+
+631
+00:26:44,080 --> 00:26:47,520
+maybe a little bit hard to see from the
+
+632
+00:26:45,520 --> 00:26:49,440
+back so I'll interpret it the left side
+
+633
+00:26:47,520 --> 00:26:50,840
+is one of their smaller models 160
+
+634
+00:26:49,440 --> 00:26:54,880
+million the right side is their biggest
+
+635
+00:26:50,840 --> 00:26:57,799
+Model 12 billion um the different lines
+
+636
+00:26:54,880 --> 00:26:58,840
+here are different steps of the training
+
+637
+00:26:57,799 --> 00:27:03,120
+process
+
+638
+00:26:58,840 --> 00:27:09,640
+so like uh 13,000 steps uh
+
+639
+00:27:03,120 --> 00:27:13,840
+30 sorry 39,000 steps and uh etc etc and
+
+640
+00:27:09,640 --> 00:27:18,240
+the xaxis here is the frequency of a
+
+641
+00:27:13,840 --> 00:27:21,679
+fact in or a frequency of a fact in the
+
+642
+00:27:18,240 --> 00:27:24,640
+training data and the y axis is question
+
+643
+00:27:21,679 --> 00:27:29,159
+answering accuracy about that fact and
+
+644
+00:27:24,640 --> 00:27:30,919
+so what this is basically showing is
+
+645
+00:27:29,159 --> 00:27:35,679
+as you scale up the
+
+646
+00:27:30,919 --> 00:27:38,520
+model um the larger models learn faster
+
+647
+00:27:35,679 --> 00:27:41,120
+um up to a point so like right here you
+
+648
+00:27:38,520 --> 00:27:44,519
+see the 2.8 billion model is about the
+
+649
+00:27:41,120 --> 00:27:46,080
+same as the 12 billion model at earlier
+
+650
+00:27:44,519 --> 00:27:48,080
+parts of the training
+
+651
+00:27:46,080 --> 00:27:51,000
+process but as you get later in the
+
+652
+00:27:48,080 --> 00:27:54,200
+training process the 12 billion model is
+
+653
+00:27:51,000 --> 00:27:57,279
+like memorizing and being able to recall
+
+654
+00:27:54,200 --> 00:27:58,840
+more facts uh so like right at the very
+
+655
+00:27:57,279 --> 00:28:02,519
+beginning you need to scale up to about
+
+656
+00:27:58,840 --> 00:28:05,840
+2.8 billion to learn efficiently uh but
+
+657
+00:28:02,519 --> 00:28:07,799
+at the end this model is like better uh
+
+658
+00:28:05,840 --> 00:28:10,399
+further on
+
+659
+00:28:07,799 --> 00:28:12,000
+so this is really nice all of this all
+
+660
+00:28:10,399 --> 00:28:14,240
+of these checkpoints all this data is
+
+661
+00:28:12,000 --> 00:28:15,840
+open they even made the data loaders so
+
+662
+00:28:14,240 --> 00:28:17,360
+it's reproducible so you can look at the
+
+663
+00:28:15,840 --> 00:28:19,559
+actual data that the model was trained
+
+664
+00:28:17,360 --> 00:28:21,000
+on um at each of the checkpoints so if
+
+665
+00:28:19,559 --> 00:28:24,320
+you want to do this sort of analysis
+
+666
+00:28:21,000 --> 00:28:27,120
+this is a good set of um models to look
+
+667
+00:28:24,320 --> 00:28:28,720
+at um another thing that they did is
+
+668
+00:28:27,120 --> 00:28:31,120
+they actually did interv itions on the
+
+669
+00:28:28,720 --> 00:28:35,640
+data so they um tried to intervene on
+
+670
+00:28:31,120 --> 00:28:37,279
+the data to modify it because uh male or
+
+671
+00:28:35,640 --> 00:28:38,840
+masculine pronouns were much more
+
+672
+00:28:37,279 --> 00:28:42,000
+frequent than feminine pronouns in the
+
+673
+00:28:38,840 --> 00:28:43,919
+data so they intervened on the data um
+
+674
+00:28:42,000 --> 00:28:45,559
+to try to balance out the distribution
+
+675
+00:28:43,919 --> 00:28:48,000
+of masculine and feminine pronouns and
+
+676
+00:28:45,559 --> 00:28:49,559
+demonstrated that the model became less
+
+677
+00:28:48,000 --> 00:28:52,080
+biased towards generating masculine
+
+678
+00:28:49,559 --> 00:28:55,480
+pronouns later so they also were able to
+
+679
+00:28:52,080 --> 00:28:55,480
+do those sorts of intervention
+
+680
+00:28:55,919 --> 00:29:00,039
+studies um any any questions about
+
+681
+00:29:00,519 --> 00:29:07,919
+Pia okay um next I want to go too Soo is
+
+682
+00:29:04,720 --> 00:29:10,279
+a more recent model um Pia I think came
+
+683
+00:29:07,919 --> 00:29:13,200
+came out around a year agoo is very
+
+684
+00:29:10,279 --> 00:29:15,440
+recent about a month ago and um this was
+
+685
+00:29:13,200 --> 00:29:18,360
+created by ai2 the Ellen Institute for
+
+686
+00:29:15,440 --> 00:29:20,440
+AI one thing you'll notice is the two um
+
+687
+00:29:18,360 --> 00:29:22,279
+completely open models that I'm talking
+
+688
+00:29:20,440 --> 00:29:24,799
+about both came from nonprofit
+
+689
+00:29:22,279 --> 00:29:28,640
+organizations um so Al Luther is
+
+690
+00:29:24,799 --> 00:29:30,039
+nonprofit uh ai2 is nonprofit so uh
+
+691
+00:29:28,640 --> 00:29:31,519
+they're maybe a little bit less worried
+
+692
+00:29:30,039 --> 00:29:34,919
+about people trying to sue them for lots
+
+693
+00:29:31,519 --> 00:29:36,720
+of money for fair use violations uh so
+
+694
+00:29:34,919 --> 00:29:38,120
+uh that's the cynical point of view the
+
+695
+00:29:36,720 --> 00:29:39,679
+the non cynical point of view is they
+
+696
+00:29:38,120 --> 00:29:42,279
+have nothing to profit by creating a
+
+697
+00:29:39,679 --> 00:29:44,240
+better model uh by having other people
+
+698
+00:29:42,279 --> 00:29:47,039
+create a better model so um they're
+
+699
+00:29:44,240 --> 00:29:50,840
+willing to do this for open uh in good
+
+700
+00:29:47,039 --> 00:29:54,080
+science um their goal is better science
+
+701
+00:29:50,840 --> 00:29:55,880
+of State ofth art LMS and uh some of the
+
+702
+00:29:54,080 --> 00:29:57,600
+unique features are top performance of a
+
+703
+00:29:55,880 --> 00:29:59,840
+fully documented model and they also
+
+704
+00:29:57,600 --> 00:30:02,960
+have in construction tun models
+
+705
+00:29:59,840 --> 00:30:04,960
+Etc looking at the parameters um
+
+706
+00:30:02,960 --> 00:30:06,240
+basically similar to llama the one big
+
+707
+00:30:04,960 --> 00:30:08,440
+difference is they're using
+
+708
+00:30:06,240 --> 00:30:10,440
+non-parametric layer Norm instead of RMS
+
+709
+00:30:08,440 --> 00:30:13,640
+Norm so this is basically layer Norm
+
+710
+00:30:10,440 --> 00:30:15,960
+with no parameters whatsoever um they
+
+711
+00:30:13,640 --> 00:30:18,880
+they didn't super clearly justify why
+
+712
+00:30:15,960 --> 00:30:21,760
+they decided to do this one difference
+
+713
+00:30:18,880 --> 00:30:25,519
+from Pia uh this was actually trained on
+
+714
+00:30:21,760 --> 00:30:29,559
+2.46 trillion tokens uh so compare this
+
+715
+00:30:25,519 --> 00:30:32,600
+to uh to Pia which was trained on 300
+
+716
+00:30:29,559 --> 00:30:34,480
+billion tokens and so they basically
+
+717
+00:30:32,600 --> 00:30:36,120
+trained it for a lot longer they trained
+
+718
+00:30:34,480 --> 00:30:37,960
+it on something called the dolma Corpus
+
+719
+00:30:36,120 --> 00:30:41,480
+which they also created at
+
+720
+00:30:37,960 --> 00:30:44,279
+ai2 um actually I think this might be
+
+721
+00:30:41,480 --> 00:30:47,279
+wrong uh so just ignore that that was
+
+722
+00:30:44,279 --> 00:30:49,760
+copy paste mistake from typ so um they
+
+723
+00:30:47,279 --> 00:30:52,039
+always use 3E to the minus 4 is a
+
+724
+00:30:49,760 --> 00:30:53,679
+learning rate which is the same as uh as
+
+725
+00:30:52,039 --> 00:30:56,039
+llama and the batch size is 4 million
+
+726
+00:30:53,679 --> 00:30:59,960
+tokens which is also the same as
+
+727
+00:30:56,039 --> 00:31:02,000
+llama so the domma that they created is
+
+728
+00:30:59,960 --> 00:31:04,320
+um actually pretty similar to the pile
+
+729
+00:31:02,000 --> 00:31:07,320
+but it's a larger Corpus it's three
+
+730
+00:31:04,320 --> 00:31:09,240
+trillion tokens this is also fully open
+
+731
+00:31:07,320 --> 00:31:11,480
+so you can download it from hugging face
+
+732
+00:31:09,240 --> 00:31:15,399
+uh if you could find some dis to put
+
+733
+00:31:11,480 --> 00:31:19,200
+three trillion tokens on um
+
+734
+00:31:15,399 --> 00:31:21,080
+so uh another thing is that they have a
+
+735
+00:31:19,200 --> 00:31:23,360
+data processing pipeline of language
+
+736
+00:31:21,080 --> 00:31:26,240
+filtering quality filtering content
+
+737
+00:31:23,360 --> 00:31:28,399
+filtering D duplication uh multisource
+
+738
+00:31:26,240 --> 00:31:31,440
+mixing and tokenization
+
+739
+00:31:28,399 --> 00:31:33,279
+and so the nice thing about this is a
+
+740
+00:31:31,440 --> 00:31:35,639
+lot of this stuff is usually proprietary
+
+741
+00:31:33,279 --> 00:31:38,240
+for most language modeling creators so
+
+742
+00:31:35,639 --> 00:31:39,600
+if you want to see all of the like data
+
+743
+00:31:38,240 --> 00:31:41,039
+processing pipeline that goes into
+
+744
+00:31:39,600 --> 00:31:42,799
+training a model this is a pretty good
+
+745
+00:31:41,039 --> 00:31:45,320
+example of
+
+746
+00:31:42,799 --> 00:31:48,120
+that um the document types that are
+
+747
+00:31:45,320 --> 00:31:51,080
+included are the common crawl and so the
+
+748
+00:31:48,120 --> 00:31:53,919
+common crawl is just um data crawled
+
+749
+00:31:51,080 --> 00:31:56,760
+from the Internet it's uh about 2.2
+
+750
+00:31:53,919 --> 00:32:00,039
+trillion tokens uh they also have the
+
+751
+00:31:56,760 --> 00:32:03,399
+stack which is um lots of code about 400
+
+752
+00:32:00,039 --> 00:32:09,120
+billion tokens of code um C4 which is
+
+753
+00:32:03,399 --> 00:32:13,039
+also uh web data uh Reddit um stem
+
+754
+00:32:09,120 --> 00:32:16,960
+papers books and uh Wikipedia
+
+755
+00:32:13,039 --> 00:32:19,039
+encyclopedia T so um you can see that it
+
+756
+00:32:16,960 --> 00:32:21,440
+has a fairly large amount of coverage
+
+757
+00:32:19,039 --> 00:32:24,480
+although mostly in
+
+758
+00:32:21,440 --> 00:32:26,799
+English um so some findings from omo
+
+759
+00:32:24,480 --> 00:32:29,440
+that I found interesting um number one
+
+760
+00:32:26,799 --> 00:32:31,279
+it has competitive average performance
+
+761
+00:32:29,440 --> 00:32:34,320
+so as I mentioned I think this is the
+
+762
+00:32:31,279 --> 00:32:38,519
+first fully open and documented language
+
+763
+00:32:34,320 --> 00:32:40,639
+model on the 7 billion range that is
+
+764
+00:32:38,519 --> 00:32:43,360
+competitive with all the other uh kind
+
+765
+00:32:40,639 --> 00:32:47,080
+of like Less open models in this range
+
+766
+00:32:43,360 --> 00:32:49,200
+so uh for example uh llama 2 is 70.5
+
+767
+00:32:47,080 --> 00:32:51,840
+average on on all of the data sets that
+
+768
+00:32:49,200 --> 00:32:53,960
+they're evaluating on Falcon is
+
+769
+00:32:51,840 --> 00:32:58,000
+70.3 MPT is
+
+770
+00:32:53,960 --> 00:33:00,000
+69.8 and almost 69.3 so it's not a
+
+771
+00:32:58,000 --> 00:33:04,639
+slouch with respect to accuracy compared
+
+772
+00:33:00,000 --> 00:33:06,399
+to pipia which had 63 um much of the
+
+773
+00:33:04,639 --> 00:33:09,120
+issue with pipia could just be that they
+
+774
+00:33:06,399 --> 00:33:12,080
+didn't train for long enough and some
+
+775
+00:33:09,120 --> 00:33:15,039
+evidence of this is this is
+
+776
+00:33:12,080 --> 00:33:17,000
+um where they measured performance
+
+777
+00:33:15,039 --> 00:33:18,880
+constantly as they train for longer so
+
+778
+00:33:17,000 --> 00:33:21,440
+the left side is training on 500 billion
+
+779
+00:33:18,880 --> 00:33:24,080
+tokens which is already more than what
+
+780
+00:33:21,440 --> 00:33:25,840
+pipia trained on the right side is uh
+
+781
+00:33:24,080 --> 00:33:30,360
+two uh
+
+782
+00:33:25,840 --> 00:33:32,679
+2.4 or 2.5 TR I tokens and you can see
+
+783
+00:33:30,360 --> 00:33:34,440
+interestingly that the numbers are just
+
+784
+00:33:32,679 --> 00:33:36,760
+continuing to increase as they train for
+
+785
+00:33:34,440 --> 00:33:39,480
+longer so it seems that training for
+
+786
+00:33:36,760 --> 00:33:43,679
+longer and longer just kind of
+
+787
+00:33:39,480 --> 00:33:47,000
+helps um one question is whether they're
+
+788
+00:33:43,679 --> 00:33:48,679
+like overfitting to uh the data set like
+
+789
+00:33:47,000 --> 00:33:52,000
+is any of the test data included in
+
+790
+00:33:48,679 --> 00:33:53,799
+their training data here um they did do
+
+791
+00:33:52,000 --> 00:33:57,440
+D duplication to some extent to try to
+
+792
+00:33:53,799 --> 00:33:59,320
+remove the test data so um I I think
+
+793
+00:33:57,440 --> 00:34:00,919
+it's quite probable that this these are
+
+794
+00:33:59,320 --> 00:34:02,720
+real gains and if they train for longer
+
+795
+00:34:00,919 --> 00:34:07,559
+they might get an even better model but
+
+796
+00:34:02,720 --> 00:34:07,559
+um I'm not you know 100% sure about
+
+797
+00:34:07,679 --> 00:34:12,639
+that cool
+
+798
+00:34:10,480 --> 00:34:14,359
+um yeah one one other thing that I
+
+799
+00:34:12,639 --> 00:34:16,119
+noticed which might be uh might be a
+
+800
+00:34:14,359 --> 00:34:18,119
+little bit interesting is um all of
+
+801
+00:34:16,119 --> 00:34:20,240
+these that I didn't mention here is all
+
+802
+00:34:18,119 --> 00:34:21,760
+of these have a learning rate schedule
+
+803
+00:34:20,240 --> 00:34:23,679
+and typically they have a learning rate
+
+804
+00:34:21,760 --> 00:34:25,760
+schedule where they do this standard
+
+805
+00:34:23,679 --> 00:34:29,159
+warmup where they increase and then they
+
+806
+00:34:25,760 --> 00:34:30,960
+decrease but they St decreasing at a a
+
+807
+00:34:29,159 --> 00:34:34,040
+floor and usually that floor is about
+
+808
+00:34:30,960 --> 00:34:36,720
+one1 the size of the um of the original
+
+809
+00:34:34,040 --> 00:34:38,520
+learning rate so the if they start out 3
+
+810
+00:34:36,720 --> 00:34:41,919
+e to Theus 4 they'll decrease it but
+
+811
+00:34:38,520 --> 00:34:43,960
+only to 3 eus2 and then they're can so
+
+812
+00:34:41,919 --> 00:34:46,079
+that might be another good thing to put
+
+813
+00:34:43,960 --> 00:34:46,079
+it
+
+814
+00:34:46,480 --> 00:34:51,240
+out cool any questions about
+
+815
+00:34:51,320 --> 00:34:58,599
+this okay um so now I'll get into L 2 um
+
+816
+00:34:56,560 --> 00:35:00,200
+in Lama 2 you know is a model that
+
+817
+00:34:58,599 --> 00:35:04,400
+probably most people have heard about it
+
+818
+00:35:00,200 --> 00:35:07,599
+was created by meta um it's one of the
+
+819
+00:35:04,400 --> 00:35:09,480
+uh strongest open language models now
+
+820
+00:35:07,599 --> 00:35:10,839
+although arguably there might be
+
+821
+00:35:09,480 --> 00:35:15,000
+stronger open language
+
+822
+00:35:10,839 --> 00:35:18,400
+models and the goal is a strong and safe
+
+823
+00:35:15,000 --> 00:35:21,320
+open LM and they have base and chat
+
+824
+00:35:18,400 --> 00:35:23,400
+versions of it and some unique features
+
+825
+00:35:21,320 --> 00:35:24,680
+are I think this is the open model with
+
+826
+00:35:23,400 --> 00:35:30,119
+the strongest
+
+827
+00:35:24,680 --> 00:35:30,119
+safety uh safeguards so it
+
+828
+00:35:30,200 --> 00:35:35,079
+is if I were to pick one model that I
+
+829
+00:35:33,079 --> 00:35:37,200
+wanted to use in an actual system that
+
+830
+00:35:35,079 --> 00:35:39,599
+was directly conversing with users I
+
+831
+00:35:37,200 --> 00:35:41,920
+would probably pick this one over
+
+832
+00:35:39,599 --> 00:35:43,760
+something like uh mistol even though
+
+833
+00:35:41,920 --> 00:35:46,599
+mistol shows Superior performance some
+
+834
+00:35:43,760 --> 00:35:48,680
+of the time um it might say things that
+
+835
+00:35:46,599 --> 00:35:52,000
+you don't want it to be saying to like
+
+836
+00:35:48,680 --> 00:35:55,520
+users so I think that's one of the uh
+
+837
+00:35:52,000 --> 00:35:56,880
+the nice things about M so I've been
+
+838
+00:35:55,520 --> 00:35:58,280
+comparing everything else to it so
+
+839
+00:35:56,880 --> 00:36:00,560
+that's pretty normal
+
+840
+00:35:58,280 --> 00:36:03,160
+um one thing about the data is the data
+
+841
+00:36:00,560 --> 00:36:04,520
+is not open they didn't say what data
+
+842
+00:36:03,160 --> 00:36:06,960
+they trained on for reasons that I
+
+843
+00:36:04,520 --> 00:36:08,960
+talked about before um what they did say
+
+844
+00:36:06,960 --> 00:36:12,400
+is it was trained on public sources
+
+845
+00:36:08,960 --> 00:36:14,240
+upsampling the most factual sources so
+
+846
+00:36:12,400 --> 00:36:17,640
+um that's what they
+
+847
+00:36:14,240 --> 00:36:19,240
+said the Llama one paper has more
+
+848
+00:36:17,640 --> 00:36:20,760
+information and so I'll talk about what
+
+849
+00:36:19,240 --> 00:36:22,400
+they did in the Llama one paper and we
+
+850
+00:36:20,760 --> 00:36:24,920
+can maybe extrapolate that they did
+
+851
+00:36:22,400 --> 00:36:26,560
+something similar in the LL tube paper
+
+852
+00:36:24,920 --> 00:36:28,200
+um and then the total training amount is
+
+853
+00:36:26,560 --> 00:36:30,079
+2 trillion tokens so that's actually
+
+854
+00:36:28,200 --> 00:36:32,680
+less
+
+855
+00:36:30,079 --> 00:36:34,520
+than um so if we look at the Llama 1
+
+856
+00:36:32,680 --> 00:36:36,319
+training data it looks a little bit like
+
+857
+00:36:34,520 --> 00:36:38,839
+it looks very much like Theo training
+
+858
+00:36:36,319 --> 00:36:41,200
+data it's common crawl C4 GitHub
+
+859
+00:36:38,839 --> 00:36:45,160
+Wikipedia books archives stack
+
+860
+00:36:41,200 --> 00:36:46,400
+exchange um and one thing you'll notice
+
+861
+00:36:45,160 --> 00:36:49,200
+is that they
+
+862
+00:36:46,400 --> 00:36:51,599
+upsampled uh Wikipedia and books and
+
+863
+00:36:49,200 --> 00:36:53,319
+down sampled GitHub according compared
+
+864
+00:36:51,599 --> 00:36:57,000
+to the amount of data that they actually
+
+865
+00:36:53,319 --> 00:37:00,760
+had and so they did 2.4 EPO over
+
+866
+00:36:57,000 --> 00:37:03,040
+Wikipedia 2.2 epochs over books and only
+
+867
+00:37:00,760 --> 00:37:05,880
+one Epoch over like the standard web
+
+868
+00:37:03,040 --> 00:37:08,240
+data and archive and stack exchange and
+
+869
+00:37:05,880 --> 00:37:09,760
+0.6 epx over the GitHub data that they
+
+870
+00:37:08,240 --> 00:37:11,520
+had so
+
+871
+00:37:09,760 --> 00:37:13,800
+obviously
+
+872
+00:37:11,520 --> 00:37:15,520
+they thought that this Wikipedia and
+
+873
+00:37:13,800 --> 00:37:17,040
+books data was more valuable for some
+
+874
+00:37:15,520 --> 00:37:20,560
+reason and they really wanted the model
+
+875
+00:37:17,040 --> 00:37:22,319
+to to learn well out it so I think um
+
+876
+00:37:20,560 --> 00:37:24,240
+when they say that they upsampled
+
+877
+00:37:22,319 --> 00:37:27,960
+factual data I'm assuming that that's
+
+878
+00:37:24,240 --> 00:37:27,960
+also what they did in mud
+
+879
+00:37:29,440 --> 00:37:33,640
+so the next thing um that's
+
+880
+00:37:35,960 --> 00:37:43,160
+yeah uh what does it need to have
+
+881
+00:37:40,280 --> 00:37:45,400
+like oh um yeah actually that's a really
+
+882
+00:37:43,160 --> 00:37:47,960
+good question so why are EPO not integer
+
+883
+00:37:45,400 --> 00:37:50,240
+values there's actually no reason at all
+
+884
+00:37:47,960 --> 00:37:52,040
+that you should do you know an integer
+
+885
+00:37:50,240 --> 00:37:54,760
+value of epo you can always save out a
+
+886
+00:37:52,040 --> 00:37:57,560
+checkpoint every you know 10,000 steps
+
+887
+00:37:54,760 --> 00:37:59,200
+or something so I'd actually encourage
+
+888
+00:37:57,560 --> 00:38:02,040
+people to get away from saving out
+
+889
+00:37:59,200 --> 00:38:03,640
+checkpoints every Epoch because that
+
+890
+00:38:02,040 --> 00:38:05,319
+kind of discourages you from making your
+
+891
+00:38:03,640 --> 00:38:07,160
+training data larger because if you make
+
+892
+00:38:05,319 --> 00:38:09,359
+your training data larger it will take
+
+893
+00:38:07,160 --> 00:38:11,760
+you'll think oh training takes forever
+
+894
+00:38:09,359 --> 00:38:13,480
+um because it takes forever to use an
+
+895
+00:38:11,760 --> 00:38:16,599
+Epoch but in reality you can just save
+
+896
+00:38:13,480 --> 00:38:18,760
+out you know periodically and um and
+
+897
+00:38:16,599 --> 00:38:21,319
+keep the checkpoints from earlier
+
+898
+00:38:18,760 --> 00:38:22,680
+so many language models don't train on
+
+899
+00:38:21,319 --> 00:38:24,480
+all the data on the web because it would
+
+900
+00:38:22,680 --> 00:38:25,800
+just be too expensive to do so despite
+
+901
+00:38:24,480 --> 00:38:27,640
+the fact that they have all the data on
+
+902
+00:38:25,800 --> 00:38:29,079
+the web
+
+903
+00:38:27,640 --> 00:38:31,000
+but very good question though it's
+
+904
+00:38:29,079 --> 00:38:34,560
+that's an important
+
+905
+00:38:31,000 --> 00:38:36,280
+Point um okay so now I'd like to talk a
+
+906
+00:38:34,560 --> 00:38:39,440
+little bit about the safety tuning that
+
+907
+00:38:36,280 --> 00:38:42,359
+goes into uh the Llama models I might
+
+908
+00:38:39,440 --> 00:38:45,640
+talk a little bit more about this um
+
+909
+00:38:42,359 --> 00:38:48,960
+later but I I think uh I'll I'll talk
+
+910
+00:38:45,640 --> 00:38:51,480
+about it now um basically the Llama 2
+
+911
+00:38:48,960 --> 00:38:54,200
+developers put a lot of effort into
+
+912
+00:38:51,480 --> 00:38:56,400
+training the model to be safe because um
+
+913
+00:38:54,200 --> 00:38:59,599
+you know they're a big company and they
+
+914
+00:38:56,400 --> 00:39:01,200
+don't want any PR design disasters um uh
+
+915
+00:38:59,599 --> 00:39:02,680
+and also you know they want an actual
+
+916
+00:39:01,200 --> 00:39:04,960
+safe model that they can use and to BL
+
+917
+00:39:02,680 --> 00:39:08,240
+their products so I think they have the
+
+918
+00:39:04,960 --> 00:39:10,880
+Dual uh you know dual motivation
+
+919
+00:39:08,240 --> 00:39:13,200
+there the first thing that they did was
+
+920
+00:39:10,880 --> 00:39:15,960
+they collected lots of data for reward
+
+921
+00:39:13,200 --> 00:39:17,520
+modeling and reward modeling what they
+
+922
+00:39:15,960 --> 00:39:19,720
+say what they're calling reward modeling
+
+923
+00:39:17,520 --> 00:39:23,720
+is basically preference modeling so they
+
+924
+00:39:19,720 --> 00:39:26,359
+have you know multiple outputs where the
+
+925
+00:39:23,720 --> 00:39:28,359
+two outputs are somehow ranked for
+
+926
+00:39:26,359 --> 00:39:29,960
+preferences and I talked about this when
+
+927
+00:39:28,359 --> 00:39:31,839
+I was talking about DPO in the
+
+928
+00:39:29,960 --> 00:39:35,720
+reinforcement learning class for
+
+929
+00:39:31,839 --> 00:39:38,480
+example um a lot of these actually exist
+
+930
+00:39:35,720 --> 00:39:41,920
+so there's um like the anthropic helpful
+
+931
+00:39:38,480 --> 00:39:45,599
+and harmless data sets uh these open AI
+
+932
+00:39:41,920 --> 00:39:48,200
+data sets uh from web GPT stack exchange
+
+933
+00:39:45,599 --> 00:39:50,160
+on stack exchange they have um helpful
+
+934
+00:39:48,200 --> 00:39:52,240
+answers and not helpful answers so once
+
+935
+00:39:50,160 --> 00:39:57,720
+that you give thumbs up and thumbs down
+
+936
+00:39:52,240 --> 00:39:59,839
+to and um the Stanford uh human
+
+937
+00:39:57,720 --> 00:40:03,040
+preferences data set I I forget what s
+
+938
+00:39:59,839 --> 00:40:05,800
+stands for human preferences data set
+
+939
+00:40:03,040 --> 00:40:09,400
+basically this is um where they tried to
+
+940
+00:40:05,800 --> 00:40:11,599
+find Reddit posts I think Reddit posts
+
+941
+00:40:09,400 --> 00:40:13,720
+that got more upvotes despite the fact
+
+942
+00:40:11,599 --> 00:40:16,400
+that they were posted later than a a
+
+943
+00:40:13,720 --> 00:40:18,720
+previous one so the idea is like usually
+
+944
+00:40:16,400 --> 00:40:21,359
+the first post posts get more up votes
+
+945
+00:40:18,720 --> 00:40:22,880
+so if you get more up votes for a later
+
+946
+00:40:21,359 --> 00:40:25,240
+post that indicates that you're probably
+
+947
+00:40:22,880 --> 00:40:27,640
+more valuable than the earlier post so
+
+948
+00:40:25,240 --> 00:40:30,880
+kind of clever uh clever way of creating
+
+949
+00:40:27,640 --> 00:40:33,680
+data um I'm actually not sure what the
+
+950
+00:40:30,880 --> 00:40:36,240
+synthetic jpj was I didn't look at that
+
+951
+00:40:33,680 --> 00:40:37,640
+and then separately from that um meta
+
+952
+00:40:36,240 --> 00:40:39,599
+collected a very large amount of
+
+953
+00:40:37,640 --> 00:40:42,400
+internal data that they didn't release
+
+954
+00:40:39,599 --> 00:40:44,319
+uh for tuning llama and they did this
+
+955
+00:40:42,400 --> 00:40:46,760
+through various iterations so basically
+
+956
+00:40:44,319 --> 00:40:49,839
+what they did is they created a first
+
+957
+00:40:46,760 --> 00:40:53,240
+version of the model um they let it you
+
+958
+00:40:49,839 --> 00:40:55,599
+loose on users they also did some uh
+
+959
+00:40:53,240 --> 00:40:56,960
+some data collection with uh people who
+
+960
+00:40:55,599 --> 00:40:59,720
+were actually trying to break the model
+
+961
+00:40:56,960 --> 00:41:01,200
+and get getting it to say bad things
+
+962
+00:40:59,720 --> 00:41:02,760
+they collected preference data from
+
+963
+00:41:01,200 --> 00:41:04,599
+these people and then they iterated over
+
+964
+00:41:02,760 --> 00:41:06,960
+and over again to collect more and more
+
+965
+00:41:04,599 --> 00:41:09,720
+of this data on various uh versions of
+
+966
+00:41:06,960 --> 00:41:11,280
+the model so as the model got gets
+
+967
+00:41:09,720 --> 00:41:14,079
+better you know it's going to be harder
+
+968
+00:41:11,280 --> 00:41:16,240
+to collect this data but um they want to
+
+969
+00:41:14,079 --> 00:41:17,920
+try to improve the current model that
+
+970
+00:41:16,240 --> 00:41:20,599
+they
+
+971
+00:41:17,920 --> 00:41:22,680
+have so the next step that they did was
+
+972
+00:41:20,599 --> 00:41:26,079
+they trained a model to follow these
+
+973
+00:41:22,680 --> 00:41:27,920
+preferences and so they trained a model
+
+974
+00:41:26,079 --> 00:41:32,560
+that basically can predict human
+
+975
+00:41:27,920 --> 00:41:35,119
+preference given um given to uh language
+
+976
+00:41:32,560 --> 00:41:37,680
+model outputs and this is a hard problem
+
+977
+00:41:35,119 --> 00:41:40,440
+right because these are language model
+
+978
+00:41:37,680 --> 00:41:42,760
+outputs and the language model thought
+
+979
+00:41:40,440 --> 00:41:45,480
+it was a good output regardless because
+
+980
+00:41:42,760 --> 00:41:47,319
+otherwise it wouldn't be sampling and so
+
+981
+00:41:45,480 --> 00:41:49,720
+you need to distinguish between two very
+
+982
+00:41:47,319 --> 00:41:52,240
+fluent looking outputs where one is
+
+983
+00:41:49,720 --> 00:41:56,880
+preferred and one is not preferred so
+
+984
+00:41:52,240 --> 00:41:58,359
+even kind of strong models like um oh by
+
+985
+00:41:56,880 --> 00:42:00,319
+the way there are some open reward
+
+986
+00:41:58,359 --> 00:42:02,119
+models like this open Assistant reward
+
+987
+00:42:00,319 --> 00:42:03,839
+model is publicly available and you can
+
+988
+00:42:02,119 --> 00:42:08,520
+just go and download it if you want if
+
+989
+00:42:03,839 --> 00:42:10,920
+you want it um but this if you evaluate
+
+990
+00:42:08,520 --> 00:42:14,720
+it on this anthropic uh helpful and
+
+991
+00:42:10,920 --> 00:42:16,160
+harmless data set um this gets about 67
+
+992
+00:42:14,720 --> 00:42:18,760
+or 68
+
+993
+00:42:16,160 --> 00:42:24,680
+accuracy
+
+994
+00:42:18,760 --> 00:42:27,200
+um but if you evaluate it on um this
+
+995
+00:42:24,680 --> 00:42:29,480
+like open Assistant data set or sorry if
+
+996
+00:42:27,200 --> 00:42:33,359
+you evaluate the public models including
+
+997
+00:42:29,480 --> 00:42:36,079
+gp4 on The Meta data set actually it's
+
+998
+00:42:33,359 --> 00:42:38,720
+pretty hard for um to distinguish
+
+999
+00:42:36,079 --> 00:42:41,319
+between the things and here they're
+
+1000
+00:42:38,720 --> 00:42:44,720
+evaluating both helpful and harmless or
+
+1001
+00:42:41,319 --> 00:42:47,400
+helpful and safety and the reason why is
+
+1002
+00:42:44,720 --> 00:42:49,119
+because like it's very easy to create a
+
+1003
+00:42:47,400 --> 00:42:51,119
+very safe but not helpful at all model
+
+1004
+00:42:49,119 --> 00:42:53,640
+by saying I don't know all the time it's
+
+1005
+00:42:51,119 --> 00:42:55,480
+very it's relatively easy to create a
+
+1006
+00:42:53,640 --> 00:42:57,880
+helpful model that's very unsafe like it
+
+1007
+00:42:55,480 --> 00:42:59,480
+will do anything you want and so they
+
+1008
+00:42:57,880 --> 00:43:01,599
+want a balance between the two and they
+
+1009
+00:42:59,480 --> 00:43:03,480
+evaluate them separately they also
+
+1010
+00:43:01,599 --> 00:43:05,280
+created two different separate reward
+
+1011
+00:43:03,480 --> 00:43:07,880
+models so they created one reward model
+
+1012
+00:43:05,280 --> 00:43:10,079
+to distinguish safety and another reward
+
+1013
+00:43:07,880 --> 00:43:13,440
+model to distinguish helpfulness and
+
+1014
+00:43:10,079 --> 00:43:14,760
+they Ed these separately to uh to train
+
+1015
+00:43:13,440 --> 00:43:17,359
+the model and you can see that the
+
+1016
+00:43:14,760 --> 00:43:18,920
+helpfulness model does a lot better on
+
+1017
+00:43:17,359 --> 00:43:20,640
+discriminating between helpful things
+
+1018
+00:43:18,920 --> 00:43:22,319
+and the safety model does a lot better
+
+1019
+00:43:20,640 --> 00:43:23,760
+on discriminate or does a little better
+
+1020
+00:43:22,319 --> 00:43:25,960
+on discriminating between safe and
+
+1021
+00:43:23,760 --> 00:43:28,480
+unsafe
+
+1022
+00:43:25,960 --> 00:43:29,920
+things um
+
+1023
+00:43:28,480 --> 00:43:33,640
+actually I didn't include this in the
+
+1024
+00:43:29,920 --> 00:43:35,400
+slides but they also have an interesting
+
+1025
+00:43:33,640 --> 00:43:38,920
+graph that
+
+1026
+00:43:35,400 --> 00:43:41,119
+demonstrates um how good the reward
+
+1027
+00:43:38,920 --> 00:43:42,640
+models are based on their size and it
+
+1028
+00:43:41,119 --> 00:43:44,359
+turns out that this is a place where
+
+1029
+00:43:42,640 --> 00:43:47,559
+it's really really important to use a
+
+1030
+00:43:44,359 --> 00:43:49,760
+large and Powerful language model to
+
+1031
+00:43:47,559 --> 00:43:51,319
+determine your reward because they
+
+1032
+00:43:49,760 --> 00:43:52,680
+demonstrate that the 70 billion
+
+1033
+00:43:51,319 --> 00:43:55,280
+parameter model that they used is
+
+1034
+00:43:52,680 --> 00:43:57,359
+actually far better than the um than the
+
+1035
+00:43:55,280 --> 00:44:00,079
+smaller models that they used it
+
+1036
+00:43:57,359 --> 00:44:00,079
+predicting this
+
+1037
+00:44:01,359 --> 00:44:07,760
+reward so this is um a graph of their
+
+1038
+00:44:05,200 --> 00:44:10,480
+incremental training process for safety
+
+1039
+00:44:07,760 --> 00:44:12,640
+tuning and um you can see they have
+
+1040
+00:44:10,480 --> 00:44:15,920
+their first supervised fine tuned model
+
+1041
+00:44:12,640 --> 00:44:19,440
+this is with no um like RL or anything
+
+1042
+00:44:15,920 --> 00:44:22,240
+like this this is a second model
+
+1043
+00:44:19,440 --> 00:44:24,760
+um and uh it improves a lot with respect
+
+1044
+00:44:22,240 --> 00:44:28,119
+to helpfulness and then they do more and
+
+1045
+00:44:24,760 --> 00:44:30,400
+more rhf uh where they start with the
+
+1046
+00:44:28,119 --> 00:44:33,200
+like supervised fine tune model and and
+
+1047
+00:44:30,400 --> 00:44:36,079
+gradually do um add more reward data
+
+1048
+00:44:33,200 --> 00:44:38,200
+train with a better reward model and get
+
+1049
+00:44:36,079 --> 00:44:39,800
+to the end where they finally have the
+
+1050
+00:44:38,200 --> 00:44:41,359
+best model that and I believe this is
+
+1051
+00:44:39,800 --> 00:44:43,200
+the one that they actually released so
+
+1052
+00:44:41,359 --> 00:44:45,000
+you can see that they really put a lot
+
+1053
+00:44:43,200 --> 00:44:46,520
+of effort into making this model you
+
+1054
+00:44:45,000 --> 00:44:49,800
+know safe and that's one of the main
+
+1055
+00:44:46,520 --> 00:44:49,800
+points of the paper that they had
+
+1056
+00:44:51,319 --> 00:44:57,920
+here um another interesting part of the
+
+1057
+00:44:55,119 --> 00:45:02,319
+Llama 2 paper is how how they got it to
+
+1058
+00:44:57,920 --> 00:45:05,280
+follow chat instructions and so um I I
+
+1059
+00:45:02,319 --> 00:45:06,640
+think you're all familiar from the class
+
+1060
+00:45:05,280 --> 00:45:10,040
+where I talked about
+
+1061
+00:45:06,640 --> 00:45:13,000
+prompting B where basically they um
+
+1062
+00:45:10,040 --> 00:45:16,119
+prompt the language model using a system
+
+1063
+00:45:13,000 --> 00:45:20,359
+message and um a user message and an
+
+1064
+00:45:16,119 --> 00:45:23,160
+assistant message and so um the
+
+1065
+00:45:20,359 --> 00:45:25,000
+characteristic of the system message is
+
+1066
+00:45:23,160 --> 00:45:28,240
+this is something that you want to be
+
+1067
+00:45:25,000 --> 00:45:32,319
+obeyed throughout the um entire
+
+1068
+00:45:28,240 --> 00:45:34,599
+conversation right and
+
+1069
+00:45:32,319 --> 00:45:36,760
+so in order to get this obeyed
+
+1070
+00:45:34,599 --> 00:45:38,079
+throughout the entire conversation you
+
+1071
+00:45:36,760 --> 00:45:39,760
+need a model that's good at paying
+
+1072
+00:45:38,079 --> 00:45:40,760
+attent paying particular attention to
+
+1073
+00:45:39,760 --> 00:45:43,160
+the system
+
+1074
+00:45:40,760 --> 00:45:45,319
+message um in this example I'm saying
+
+1075
+00:45:43,160 --> 00:45:46,880
+write in only emojis so you no matter
+
+1076
+00:45:45,319 --> 00:45:48,720
+how long this conversation gets you want
+
+1077
+00:45:46,880 --> 00:45:50,599
+your model to continue writing in emojis
+
+1078
+00:45:48,720 --> 00:45:53,440
+and models don't do this
+
+1079
+00:45:50,599 --> 00:45:56,559
+spontaneously so what they did here and
+
+1080
+00:45:53,440 --> 00:45:58,359
+I'm I'm 90% 95% certain that my
+
+1081
+00:45:56,559 --> 00:45:59,800
+interpret of the paper is correct the
+
+1082
+00:45:58,359 --> 00:46:03,319
+paper is a little bit hard to understand
+
+1083
+00:45:59,800 --> 00:46:06,720
+with respect to this but um the uh what
+
+1084
+00:46:03,319 --> 00:46:10,480
+they I think they do is they take the
+
+1085
+00:46:06,720 --> 00:46:13,200
+system message and then they have a data
+
+1086
+00:46:10,480 --> 00:46:16,160
+generation step where they
+
+1087
+00:46:13,200 --> 00:46:19,079
+basically ask an existing model to write
+
+1088
+00:46:16,160 --> 00:46:21,400
+and only emojis and then say hello and
+
+1089
+00:46:19,079 --> 00:46:23,640
+then the model generates something and
+
+1090
+00:46:21,400 --> 00:46:26,599
+then they say again write in only emojis
+
+1091
+00:46:23,640 --> 00:46:28,440
+how are you doing and then they uh they
+
+1092
+00:46:26,599 --> 00:46:29,599
+generate it again and because this is so
+
+1093
+00:46:28,440 --> 00:46:32,680
+close in the
+
+1094
+00:46:29,599 --> 00:46:35,440
+context um the assistant basically will
+
+1095
+00:46:32,680 --> 00:46:36,760
+be will you know continue paying
+
+1096
+00:46:35,440 --> 00:46:39,119
+attention to these
+
+1097
+00:46:36,760 --> 00:46:40,599
+directions um and then after that now
+
+1098
+00:46:39,119 --> 00:46:42,640
+you have a data set that you can train
+
+1099
+00:46:40,599 --> 00:46:44,280
+your model on you can train your model
+
+1100
+00:46:42,640 --> 00:46:46,880
+on this generated data set that looks
+
+1101
+00:46:44,280 --> 00:46:49,079
+like write an only emojis say hello uh
+
+1102
+00:46:46,880 --> 00:46:50,480
+how are you doing and stuff like this
+
+1103
+00:46:49,079 --> 00:46:54,040
+and they try this with a whole bunch of
+
+1104
+00:46:50,480 --> 00:46:57,880
+rules it's like right um right as if
+
+1105
+00:46:54,040 --> 00:47:00,559
+you're explaining to a 5-year-old or um
+
+1106
+00:46:57,880 --> 00:47:02,720
+write in a very polite manner write in a
+
+1107
+00:47:00,559 --> 00:47:03,960
+very informal Manner and stuff like that
+
+1108
+00:47:02,720 --> 00:47:06,480
+so they generate a whole bunch of the
+
+1109
+00:47:03,960 --> 00:47:08,480
+synthetic data and in doing this they
+
+1110
+00:47:06,480 --> 00:47:09,960
+basically are able to train the model to
+
+1111
+00:47:08,480 --> 00:47:11,559
+pay very close attention to the system
+
+1112
+00:47:09,960 --> 00:47:13,480
+message because it needs to do so in
+
+1113
+00:47:11,559 --> 00:47:17,319
+order to do
+
+1114
+00:47:13,480 --> 00:47:19,160
+better so um yeah these are kind of the
+
+1115
+00:47:17,319 --> 00:47:20,599
+unique characteristics from lava 2 I'd
+
+1116
+00:47:19,160 --> 00:47:21,960
+love to tell you more about its training
+
+1117
+00:47:20,599 --> 00:47:24,520
+data and all that other stuff but they
+
+1118
+00:47:21,960 --> 00:47:26,240
+didn't tell us uh like what they did
+
+1119
+00:47:24,520 --> 00:47:28,839
+with respect to that so we'll just have
+
+1120
+00:47:26,240 --> 00:47:28,839
+to infer
+
+1121
+00:47:28,960 --> 00:47:33,559
+on cool uh any questions about
+
+1122
+00:47:33,800 --> 00:47:39,160
+this okay
+
+1123
+00:47:36,640 --> 00:47:40,839
+go so next I want to go into mistol and
+
+1124
+00:47:39,160 --> 00:47:42,599
+mixol this is going to be a little bit
+
+1125
+00:47:40,839 --> 00:47:44,200
+short because I've kind of covered some
+
+1126
+00:47:42,599 --> 00:47:45,720
+of the stuff already and also they
+
+1127
+00:47:44,200 --> 00:47:48,240
+didn't tell you very much about the
+
+1128
+00:47:45,720 --> 00:47:52,240
+training process um basically it was
+
+1129
+00:47:48,240 --> 00:47:54,079
+created by mistol um AI the company and
+
+1130
+00:47:52,240 --> 00:47:56,839
+it's a strong and somewhat multilingual
+
+1131
+00:47:54,079 --> 00:47:59,400
+open language model um it has some
+
+1132
+00:47:56,839 --> 00:48:01,760
+unique features like speed optimizations
+
+1133
+00:47:59,400 --> 00:48:03,200
+in um including grouped query attention
+
+1134
+00:48:01,760 --> 00:48:06,200
+and mixture of
+
+1135
+00:48:03,200 --> 00:48:06,200
+experts
+
+1136
+00:48:06,599 --> 00:48:12,359
+um it makes unlike the other ones it
+
+1137
+00:48:10,599 --> 00:48:14,599
+makes some actual architectural
+
+1138
+00:48:12,359 --> 00:48:17,599
+modifications including sliding window
+
+1139
+00:48:14,599 --> 00:48:19,160
+attention and um mixture of experts and
+
+1140
+00:48:17,599 --> 00:48:21,079
+I I have actually talked about both of
+
+1141
+00:48:19,160 --> 00:48:23,640
+them so I'll just very briefly go
+
+1142
+00:48:21,079 --> 00:48:26,040
+through them here um the data as far as
+
+1143
+00:48:23,640 --> 00:48:28,559
+I could tell was not disclosed uh very
+
+1144
+00:48:26,040 --> 00:48:30,480
+completely but one important thing is it
+
+1145
+00:48:28,559 --> 00:48:32,160
+includes English and European languages
+
+1146
+00:48:30,480 --> 00:48:35,520
+so at least theoretically it should be
+
+1147
+00:48:32,160 --> 00:48:38,040
+better than llama at this um one
+
+1148
+00:48:35,520 --> 00:48:39,559
+interesting thing about llama is llama
+
+1149
+00:48:38,040 --> 00:48:40,680
+if I remember correctly the actual
+
+1150
+00:48:39,559 --> 00:48:42,880
+numbers are in the paper but it's
+
+1151
+00:48:40,680 --> 00:48:47,920
+something like 85%
+
+1152
+00:48:42,880 --> 00:48:52,400
+English um 8% code and then like
+
+1153
+00:48:47,920 --> 00:48:54,559
+0.3% other languages like um starting at
+
+1154
+00:48:52,400 --> 00:48:57,280
+all the other languages it's like 0.3%
+
+1155
+00:48:54,559 --> 00:48:59,680
+so it's not very multilingual at all
+
+1156
+00:48:57,280 --> 00:49:01,319
+um and they were really only aiming to
+
+1157
+00:48:59,680 --> 00:49:04,799
+create a good uh English
+
+1158
+00:49:01,319 --> 00:49:06,200
+model um also the training uh details
+
+1159
+00:49:04,799 --> 00:49:08,280
+were not disclosed here like I wasn't
+
+1160
+00:49:06,200 --> 00:49:12,400
+able to find the back sides as far as I
+
+1161
+00:49:08,280 --> 00:49:15,119
+know um so mistol uses sliding window
+
+1162
+00:49:12,400 --> 00:49:18,200
+attention uh vanilla attention basically
+
+1163
+00:49:15,119 --> 00:49:21,440
+you always attend to all of the previous
+
+1164
+00:49:18,200 --> 00:49:24,880
+things in the sequence what mistol does
+
+1165
+00:49:21,440 --> 00:49:28,119
+is it attends to the previous n um
+
+1166
+00:49:24,880 --> 00:49:30,559
+examples where n is equal to 4090 6 and
+
+1167
+00:49:28,119 --> 00:49:34,839
+because of this uh what this means is
+
+1168
+00:49:30,559 --> 00:49:37,200
+you can attend uh 4096 back and then in
+
+1169
+00:49:34,839 --> 00:49:39,280
+the next layer you can attend 4096 back
+
+1170
+00:49:37,200 --> 00:49:41,599
+then you can attend 4096 back so
+
+1171
+00:49:39,280 --> 00:49:44,400
+basically as many layers as you have
+
+1172
+00:49:41,599 --> 00:49:47,240
+times 4096 you can attend that many
+
+1173
+00:49:44,400 --> 00:49:49,000
+tokens back for a minimal training
+
+1174
+00:49:47,240 --> 00:49:50,760
+penalty because still the length of
+
+1175
+00:49:49,000 --> 00:49:55,079
+attention for any particular token is
+
+1176
+00:49:50,760 --> 00:49:57,440
+the same uh so that's one
+
+1177
+00:49:55,079 --> 00:50:00,400
+feature oh and then yeah sorry the other
+
+1178
+00:49:57,440 --> 00:50:01,920
+feature is mixol is using um is using a
+
+1179
+00:50:00,400 --> 00:50:05,920
+mixture of experts like we talked about
+
+1180
+00:50:01,920 --> 00:50:07,720
+in the previous time so um despite these
+
+1181
+00:50:05,920 --> 00:50:09,520
+uh these are very strong models they're
+
+1182
+00:50:07,720 --> 00:50:12,960
+generally stronger than llama at a lot
+
+1183
+00:50:09,520 --> 00:50:15,480
+of things um and mixol is actually a lot
+
+1184
+00:50:12,960 --> 00:50:18,200
+faster and easier to deploy than llama
+
+1185
+00:50:15,480 --> 00:50:20,680
+70b uh it's smaller it only has 45
+
+1186
+00:50:18,200 --> 00:50:23,680
+billion parameters so it's definitely a
+
+1187
+00:50:20,680 --> 00:50:26,680
+good choice if you want to use it yeah
+
+1188
+00:50:23,680 --> 00:50:26,680
+makinging
+
+1189
+00:50:28,720 --> 00:50:33,000
+yeah so it's attending to 496
+
+1190
+00:50:33,520 --> 00:50:39,559
+C so the contact size
+
+1191
+00:50:37,720 --> 00:50:43,240
+typically like let's say you have a
+
+1192
+00:50:39,559 --> 00:50:45,240
+block of 4096 tokens here typically that
+
+1193
+00:50:43,240 --> 00:50:48,079
+means that the first token attends to
+
+1194
+00:50:45,240 --> 00:50:51,200
+zero tokens the second token attends to
+
+1195
+00:50:48,079 --> 00:50:54,640
+one token and the third token attends to
+
+1196
+00:50:51,200 --> 00:50:58,920
+two tokens here this is maybe a little
+
+1197
+00:50:54,640 --> 00:51:01,680
+bit uh Mis mislead I guess but if your
+
+1198
+00:50:58,920 --> 00:51:04,079
+context length is 4096 you actually get
+
+1199
+00:51:01,680 --> 00:51:07,760
+a block of twice that size you get a
+
+1200
+00:51:04,079 --> 00:51:10,960
+block of 8192 tokens and so the first
+
+1201
+00:51:07,760 --> 00:51:15,839
+one attends to all of the previous
+
+1202
+00:51:10,960 --> 00:51:17,760
+ones so the first uh sorry so
+
+1203
+00:51:15,839 --> 00:51:19,960
+the
+
+1204
+00:51:17,760 --> 00:51:22,280
+um so the
+
+1205
+00:51:19,960 --> 00:51:26,760
+40
+
+1206
+00:51:22,280 --> 00:51:29,280
+9 7 token
+
+1207
+00:51:26,760 --> 00:51:32,280
+back to um all from
+
+1208
+00:51:29,280 --> 00:51:36,319
+[Music]
+
+1209
+00:51:32,280 --> 00:51:36,319
+to sorry either
+
+1210
+00:51:41,160 --> 00:51:46,880
+one96 and
+
+1211
+00:51:43,839 --> 00:51:50,520
+so because of that you moan to the very
+
+1212
+00:51:46,880 --> 00:51:50,520
+end then you have the 8198
+
+1213
+00:51:50,880 --> 00:51:55,359
+seconding from like9
+
+1214
+00:51:58,480 --> 00:52:01,920
+and so like every token is always
+
+1215
+00:52:00,319 --> 00:52:05,280
+attending to the previous one and that
+
+1216
+00:52:01,920 --> 00:52:08,200
+allows you to um to kind of attend to
+
+1217
+00:52:05,280 --> 00:52:08,200
+things in the previous
+
+1218
+00:52:11,760 --> 00:52:18,520
+BL uh no it's big so that allows them to
+
+1219
+00:52:15,000 --> 00:52:22,000
+attend a very large
+
+1220
+00:52:18,520 --> 00:52:24,599
+am cool um so the next one I'd like to
+
+1221
+00:52:22,000 --> 00:52:26,559
+talk about is quen this is one that in
+
+1222
+00:52:24,599 --> 00:52:29,040
+the US at least people maybe pay a a
+
+1223
+00:52:26,559 --> 00:52:33,000
+little bit less attention to um but it
+
+1224
+00:52:29,040 --> 00:52:35,680
+was created by Alibaba and it's a strong
+
+1225
+00:52:33,000 --> 00:52:37,559
+um multilingual model especially English
+
+1226
+00:52:35,680 --> 00:52:39,119
+and Chinese but even uh in other
+
+1227
+00:52:37,559 --> 00:52:41,000
+languages as
+
+1228
+00:52:39,119 --> 00:52:43,480
+well
+
+1229
+00:52:41,000 --> 00:52:45,160
+and uh one of its defining
+
+1230
+00:52:43,480 --> 00:52:48,240
+characteristics other than just being a
+
+1231
+00:52:45,160 --> 00:52:50,160
+strong model overall is that it's has a
+
+1232
+00:52:48,240 --> 00:52:51,799
+large vocabulary for multilingual
+
+1233
+00:52:50,160 --> 00:52:56,000
+support and strong
+
+1234
+00:52:51,799 --> 00:52:58,760
+performance um it comes in several sizes
+
+1235
+00:52:56,000 --> 00:53:01,880
+um I
+
+1236
+00:52:58,760 --> 00:53:04,799
+believe uh there's a 7B version and then
+
+1237
+00:53:01,880 --> 00:53:10,119
+there's also like a large like 70b
+
+1238
+00:53:04,799 --> 00:53:13,480
+version 72b I think and it's using very
+
+1239
+00:53:10,119 --> 00:53:15,319
+standard uh architecture things the only
+
+1240
+00:53:13,480 --> 00:53:18,119
+small difference it has is it has a bias
+
+1241
+00:53:15,319 --> 00:53:19,920
+in the attention layer which is doesn't
+
+1242
+00:53:18,119 --> 00:53:23,559
+uh exist in
+
+1243
+00:53:19,920 --> 00:53:25,880
+llama um an important thing is it's
+
+1244
+00:53:23,559 --> 00:53:28,920
+actually trained on multilingual data
+
+1245
+00:53:25,880 --> 00:53:32,720
+and they use a large vocabulary um they
+
+1246
+00:53:28,920 --> 00:53:33,839
+use a vocabulary of 150k in contrast to
+
+1247
+00:53:32,720 --> 00:53:36,599
+llama's
+
+1248
+00:53:33,839 --> 00:53:39,839
+32k and that allows it to handle
+
+1249
+00:53:36,599 --> 00:53:41,720
+multilingual uh data relatively
+
+1250
+00:53:39,839 --> 00:53:47,079
+well
+
+1251
+00:53:41,720 --> 00:53:49,359
+and um we have the three uh similar you
+
+1252
+00:53:47,079 --> 00:53:52,760
+know training regimes so overall it's
+
+1253
+00:53:49,359 --> 00:53:55,559
+not very diff different from uh
+
+1254
+00:53:52,760 --> 00:53:57,040
+llama what might be different is data
+
+1255
+00:53:55,559 --> 00:53:59,319
+engineering
+
+1256
+00:53:57,040 --> 00:54:00,680
+uh and actually I I expect the data
+
+1257
+00:53:59,319 --> 00:54:02,760
+engineering part is a bit different
+
+1258
+00:54:00,680 --> 00:54:06,400
+because overall it's a bit stronger than
+
+1259
+00:54:02,760 --> 00:54:09,920
+llama 2 um and I I think uh that has to
+
+1260
+00:54:06,400 --> 00:54:12,119
+do with data in in various areas one
+
+1261
+00:54:09,920 --> 00:54:16,920
+interesting piece from the paper that
+
+1262
+00:54:12,119 --> 00:54:18,280
+they have is uh if we think all the way
+
+1263
+00:54:16,920 --> 00:54:21,720
+back to when we talked about word
+
+1264
+00:54:18,280 --> 00:54:23,839
+subword models and word tokenization we
+
+1265
+00:54:21,720 --> 00:54:27,760
+remember that subword models split up
+
+1266
+00:54:23,839 --> 00:54:29,920
+the input and they split up the input uh
+
+1267
+00:54:27,760 --> 00:54:31,799
+so that frequent tokens get longer
+
+1268
+00:54:29,920 --> 00:54:34,520
+outputs and infrequent tokens get
+
+1269
+00:54:31,799 --> 00:54:36,359
+shorter outputs so one of the problems
+
+1270
+00:54:34,520 --> 00:54:40,559
+as I mentioned a long time ago when we
+
+1271
+00:54:36,359 --> 00:54:42,040
+covered this topic is this causes issues
+
+1272
+00:54:40,559 --> 00:54:43,000
+if you're doing multilingual things
+
+1273
+00:54:42,040 --> 00:54:44,880
+because if you have very little
+
+1274
+00:54:43,000 --> 00:54:47,520
+multilingual data in your training data
+
+1275
+00:54:44,880 --> 00:54:49,040
+for the subword tokenization model um it
+
+1276
+00:54:47,520 --> 00:54:51,559
+will end up splitting all of the words
+
+1277
+00:54:49,040 --> 00:54:55,680
+into basically characters or even bytes
+
+1278
+00:54:51,559 --> 00:54:59,040
+so what this shows here is this is
+
+1279
+00:54:55,680 --> 00:55:00,960
+comparing the amount of subord
+
+1280
+00:54:59,040 --> 00:55:03,040
+tokenization that happens according to
+
+1281
+00:55:00,960 --> 00:55:05,520
+each of the llms
+
+1282
+00:55:03,040 --> 00:55:08,599
+tokenizers with another explicitly
+
+1283
+00:55:05,520 --> 00:55:10,799
+multilingual model xlmr so xlmr is kind
+
+1284
+00:55:08,599 --> 00:55:12,760
+of their Baseline here with respect to
+
+1285
+00:55:10,799 --> 00:55:16,319
+how much it tokenizes each
+
+1286
+00:55:12,760 --> 00:55:19,079
+language and on the very left we have
+
+1287
+00:55:16,319 --> 00:55:22,839
+llama and so what we can see is that
+
+1288
+00:55:19,079 --> 00:55:26,599
+llama tokenizes TI
+
+1289
+00:55:22,839 --> 00:55:28,640
+3.7 times as much as it as xlmr does so
+
+1290
+00:55:26,599 --> 00:55:30,359
+it's basically splitting tie into tie up
+
+1291
+00:55:28,640 --> 00:55:32,480
+into little tiny bits which makes it
+
+1292
+00:55:30,359 --> 00:55:35,440
+very expensive and ineffective to
+
+1293
+00:55:32,480 --> 00:55:38,039
+process uh let's let's find some other
+
+1294
+00:55:35,440 --> 00:55:41,599
+languages that we care about we have he
+
+1295
+00:55:38,039 --> 00:55:43,760
+Hebrew Arabic
+
+1296
+00:55:41,599 --> 00:55:47,079
+Korean uh
+
+1297
+00:55:43,760 --> 00:55:49,559
+Japanese uh Chinese so all of these you
+
+1298
+00:55:47,079 --> 00:55:52,319
+can see are split up pretty into many
+
+1299
+00:55:49,559 --> 00:55:55,440
+many different chunks by
+
+1300
+00:55:52,319 --> 00:55:56,799
+Lama and then we we have a few other
+
+1301
+00:55:55,440 --> 00:55:58,359
+language models in the middle and then
+
+1302
+00:55:56,799 --> 00:56:01,440
+we have quen on the right side and what
+
+1303
+00:55:58,359 --> 00:56:04,039
+we can see is basically it's pretty
+
+1304
+00:56:01,440 --> 00:56:06,400
+comparable to xlmr maybe a little bit
+
+1305
+00:56:04,039 --> 00:56:09,520
+more than xlmr but pretty comparable to
+
+1306
+00:56:06,400 --> 00:56:12,839
+xlmr on many languages and then on code
+
+1307
+00:56:09,520 --> 00:56:15,000
+it actually um splits up code much less
+
+1308
+00:56:12,839 --> 00:56:17,039
+so we can see that you know its
+
+1309
+00:56:15,000 --> 00:56:18,960
+tokenizer is heavily
+
+1310
+00:56:17,039 --> 00:56:22,640
+multilingual um another thing I'd like
+
+1311
+00:56:18,960 --> 00:56:24,640
+to point out is um I I let I'm focusing
+
+1312
+00:56:22,640 --> 00:56:27,000
+on this particular language model for a
+
+1313
+00:56:24,640 --> 00:56:29,799
+number of reasons
+
+1314
+00:56:27,000 --> 00:56:32,440
+um the first one is multilinguality and
+
+1315
+00:56:29,799 --> 00:56:36,599
+I I like multilinguality I hope other
+
+1316
+00:56:32,440 --> 00:56:39,039
+people like multilinguality too um but
+
+1317
+00:56:36,599 --> 00:56:43,799
+another motivation is just it has quite
+
+1318
+00:56:39,039 --> 00:56:45,680
+strong performance and it's uh topping
+
+1319
+00:56:43,799 --> 00:56:47,960
+topping the leaderboards in in several
+
+1320
+00:56:45,680 --> 00:56:52,160
+different uh
+
+1321
+00:56:47,960 --> 00:56:57,640
+places so if we look at the open llm
+
+1322
+00:56:52,160 --> 00:56:57,640
+leaderboard um at least recently
+
+1323
+00:56:59,480 --> 00:57:07,440
+this was a fine-tuned model by Abus
+
+1324
+00:57:04,240 --> 00:57:09,440
+AI which was uh originally based on quen
+
+1325
+00:57:07,440 --> 00:57:11,079
+so you can see that this is like a
+
+1326
+00:57:09,440 --> 00:57:13,920
+strong found Foundation model that lots
+
+1327
+00:57:11,079 --> 00:57:16,440
+of people are using for fing things so
+
+1328
+00:57:13,920 --> 00:57:18,960
+um I would definitely uh encourage you
+
+1329
+00:57:16,440 --> 00:57:20,240
+to take a look at that too of course
+
+1330
+00:57:18,960 --> 00:57:22,520
+there's many many different models that
+
+1331
+00:57:20,240 --> 00:57:24,880
+I didn't cover because if I covered all
+
+1332
+00:57:22,520 --> 00:57:26,839
+of the general purpose models then we'd
+
+1333
+00:57:24,880 --> 00:57:29,599
+be here all day but um
+
+1334
+00:57:26,839 --> 00:57:31,200
+that's uh first start so next I want to
+
+1335
+00:57:29,599 --> 00:57:33,200
+go into other kind of special purpose
+
+1336
+00:57:31,200 --> 00:57:36,839
+models but are there any questions about
+
+1337
+00:57:33,200 --> 00:57:36,839
+um about the things I covered so
+
+1338
+00:57:38,000 --> 00:57:44,079
+far cool okay
+
+1339
+00:57:41,440 --> 00:57:47,960
+um so next I'd like to go into other
+
+1340
+00:57:44,079 --> 00:57:49,760
+models um first is code models so code
+
+1341
+00:57:47,960 --> 00:57:52,680
+models are models that were specifically
+
+1342
+00:57:49,760 --> 00:57:55,280
+trained on code actually right now every
+
+1343
+00:57:52,680 --> 00:57:56,960
+model is a code model um like nobody
+
+1344
+00:57:55,280 --> 00:57:58,799
+pre-train a large language model and is
+
+1345
+00:57:56,960 --> 00:58:01,720
+serious about it and doesn't train on
+
+1346
+00:57:58,799 --> 00:58:04,680
+code because um generating code is a
+
+1347
+00:58:01,720 --> 00:58:06,680
+huge use case and also um some work has
+
+1348
+00:58:04,680 --> 00:58:08,880
+demonstrated that gen training on code
+
+1349
+00:58:06,680 --> 00:58:13,720
+seems to improve reasoning abilities of
+
+1350
+00:58:08,880 --> 00:58:16,160
+language models as well um but uh these
+
+1351
+00:58:13,720 --> 00:58:19,319
+models were very heavily trained on code
+
+1352
+00:58:16,160 --> 00:58:22,400
+so um we have star coder 2 this is a
+
+1353
+00:58:19,319 --> 00:58:24,079
+very recent uh entry this is a fully
+
+1354
+00:58:22,400 --> 00:58:26,720
+open model so you can see the data it
+
+1355
+00:58:24,079 --> 00:58:29,039
+was trained on um all the training
+
+1356
+00:58:26,720 --> 00:58:31,640
+details are released and other stuff
+
+1357
+00:58:29,039 --> 00:58:36,760
+like that so this is kind of in the
+
+1358
+00:58:31,640 --> 00:58:38,599
+pythia you know piao category but it's
+
+1359
+00:58:36,760 --> 00:58:41,240
+very uh it's actually a very strong
+
+1360
+00:58:38,599 --> 00:58:42,839
+model very good model so it's uh a good
+
+1361
+00:58:41,240 --> 00:58:46,480
+one to know
+
+1362
+00:58:42,839 --> 00:58:48,680
+about um separately there's code llama
+
+1363
+00:58:46,480 --> 00:58:52,520
+by meta which is a code adaptation of
+
+1364
+00:58:48,680 --> 00:58:54,799
+llama and uh it also gets quite a quite
+
+1365
+00:58:52,520 --> 00:58:57,720
+good performance there's also another
+
+1366
+00:58:54,799 --> 00:58:59,760
+model uh called seek coder I would say
+
+1367
+00:58:57,720 --> 00:59:01,720
+all three of these are topping some
+
+1368
+00:58:59,760 --> 00:59:03,119
+variety of leaderboard where deep seek
+
+1369
+00:59:01,720 --> 00:59:04,640
+maybe is topping a few more leader
+
+1370
+00:59:03,119 --> 00:59:06,319
+boards than the other ones are but all
+
+1371
+00:59:04,640 --> 00:59:09,960
+of them are very competitive and might
+
+1372
+00:59:06,319 --> 00:59:11,680
+be the best in class for code things um
+
+1373
+00:59:09,960 --> 00:59:13,119
+I'm not talking very much about these
+
+1374
+00:59:11,680 --> 00:59:15,119
+because we're going to have a a class on
+
+1375
+00:59:13,119 --> 00:59:18,280
+code generation and code related things
+
+1376
+00:59:15,119 --> 00:59:21,000
+later so um I'm not going to go into a
+
+1377
+00:59:18,280 --> 00:59:21,000
+lot of detail
+
+1378
+00:59:21,319 --> 00:59:27,839
+here another thing is about math models
+
+1379
+00:59:24,680 --> 00:59:31,960
+and so like one thing is large language
+
+1380
+00:59:27,839 --> 00:59:35,480
+models are not particularly good at math
+
+1381
+00:59:31,960 --> 00:59:38,839
+um so there are quite a few models that
+
+1382
+00:59:35,480 --> 00:59:40,200
+were trained specifically for math um
+
+1383
+00:59:38,839 --> 00:59:45,160
+the first one is
+
+1384
+00:59:40,200 --> 00:59:47,280
+Lemma um yes that is a pun um for like
+
+1385
+00:59:45,160 --> 00:59:49,920
+LMA from
+
+1386
+00:59:47,280 --> 00:59:51,160
+maap I I'm I'm not responsible for it
+
+1387
+00:59:49,920 --> 00:59:55,240
+but I I thought it was kind of funny
+
+1388
+00:59:51,160 --> 00:59:56,920
+anyway um so uh this was by alther AI so
+
+1389
+00:59:55,240 --> 01:00:00,359
+because this was by Luther again this is
+
+1390
+00:59:56,920 --> 01:00:03,640
+a fully open model all the data is open
+
+1391
+01:00:00,359 --> 01:00:05,960
+um everything is known about it um also
+
+1392
+01:00:03,640 --> 01:00:08,480
+uh our our very own Shan wck was one of
+
+1393
+01:00:05,960 --> 01:00:10,559
+the contributors to it uh so if you want
+
+1394
+01:00:08,480 --> 01:00:13,839
+to know more about LMA you can go bother
+
+1395
+01:00:10,559 --> 01:00:17,440
+Sean so uh that's another thing that I
+
+1396
+01:00:13,839 --> 01:00:19,240
+should mention um another thing is deep
+
+1397
+01:00:17,440 --> 01:00:20,839
+seek who made the Deep seek Cod model
+
+1398
+01:00:19,240 --> 01:00:23,480
+has also created a very strong math
+
+1399
+01:00:20,839 --> 01:00:26,200
+model uh that's competitive with gp4 on
+
+1400
+01:00:23,480 --> 01:00:28,160
+a lot of math things uh basically the
+
+1401
+01:00:26,200 --> 01:00:30,480
+way they did this was they did this by
+
+1402
+01:00:28,160 --> 01:00:32,559
+um training a classifier to try to
+
+1403
+01:00:30,480 --> 01:00:34,640
+identify data on the web that is related
+
+1404
+01:00:32,559 --> 01:00:37,599
+to math and scraping all of that data
+
+1405
+01:00:34,640 --> 01:00:39,960
+and fine tuning on it so um you can get
+
+1406
+01:00:37,599 --> 01:00:42,280
+gold standard data from like proof pile
+
+1407
+01:00:39,960 --> 01:00:44,359
+and a whole bunch of other sources and
+
+1408
+01:00:42,280 --> 01:00:46,200
+so they trained a like math or not maath
+
+1409
+01:00:44,359 --> 01:00:48,400
+classifier and and harvested a lot of
+
+1410
+01:00:46,200 --> 01:00:52,400
+math related
+
+1411
+01:00:48,400 --> 01:00:52,400
+dat yeah
+
+1412
+01:00:59,880 --> 01:01:04,920
+it's mostly mostly data sets um I
+
+1413
+01:01:03,599 --> 01:01:07,119
+actually might be talking a little bit
+
+1414
+01:01:04,920 --> 01:01:10,039
+more about these in the reasoning class
+
+1415
+01:01:07,119 --> 01:01:11,799
+and I did a lot of uh I did a lot of
+
+1416
+01:01:10,039 --> 01:01:13,599
+prep to create these slides and actually
+
+1417
+01:01:11,799 --> 01:01:15,680
+ran out of time to do the math stuff so
+
+1418
+01:01:13,599 --> 01:01:17,200
+I might talk about it later um but I
+
+1419
+01:01:15,680 --> 01:01:18,480
+don't think they're really doing a lot
+
+1420
+01:01:17,200 --> 01:01:21,799
+of things like you could think of
+
+1421
+01:01:18,480 --> 01:01:23,440
+obvious things like doing RL rlf based
+
+1422
+01:01:21,799 --> 01:01:26,799
+on like whether it gets the answer right
+
+1423
+01:01:23,440 --> 01:01:28,559
+or not in the end um as far as I know
+
+1424
+01:01:26,799 --> 01:01:30,359
+that's not a big ingredient here but
+
+1425
+01:01:28,559 --> 01:01:31,920
+I'll be more sure of that when we talk
+
+1426
+01:01:30,359 --> 01:01:37,599
+about it
+
+1427
+01:01:31,920 --> 01:01:39,559
+later um cool and a final one uh it's
+
+1428
+01:01:37,599 --> 01:01:43,200
+not a Sy model it's a science model
+
+1429
+01:01:39,559 --> 01:01:45,920
+sorry for the typo um but uh this model
+
+1430
+01:01:43,200 --> 01:01:49,160
+Galactica um was a model for science
+
+1431
+01:01:45,920 --> 01:01:51,799
+that was trained by meta
+
+1432
+01:01:49,160 --> 01:01:54,359
+um does anyone remember this model or
+
+1433
+01:01:51,799 --> 01:01:58,079
+was anybody around when this model came
+
+1434
+01:01:54,359 --> 01:01:59,640
+out no there was a big uh a big PR
+
+1435
+01:01:58,079 --> 01:02:01,160
+disaster for meta when they released
+
+1436
+01:01:59,640 --> 01:02:03,480
+this model because they said this is a
+
+1437
+01:02:01,160 --> 01:02:05,520
+great model for math use it in your in
+
+1438
+01:02:03,480 --> 01:02:08,599
+writing your science paper sorry this is
+
+1439
+01:02:05,520 --> 01:02:10,480
+a great model for science try using it
+
+1440
+01:02:08,599 --> 01:02:12,640
+it in your science papers and this came
+
+1441
+01:02:10,480 --> 01:02:14,839
+out about two years ago and two years
+
+1442
+01:02:12,640 --> 01:02:16,640
+ago language models hallucinated all the
+
+1443
+01:02:14,839 --> 01:02:19,279
+time and came up with false scientific
+
+1444
+01:02:16,640 --> 01:02:22,039
+facts and stuff and so basically um a
+
+1445
+01:02:19,279 --> 01:02:25,680
+lot of people kind of bashed this model
+
+1446
+01:02:22,039 --> 01:02:27,440
+uh in my mind kind of unfairly because
+
+1447
+01:02:25,680 --> 01:02:31,200
+they actually have a lot of really
+
+1448
+01:02:27,440 --> 01:02:32,960
+interesting things in this paper um one
+
+1449
+01:02:31,200 --> 01:02:34,720
+interesting thing in this paper is they
+
+1450
+01:02:32,960 --> 01:02:37,000
+tried to create a general purpose model
+
+1451
+01:02:34,720 --> 01:02:38,960
+for science that's able to understand
+
+1452
+01:02:37,000 --> 01:02:41,960
+not only text but also various
+
+1453
+01:02:38,960 --> 01:02:47,720
+modalities of scientific data and so
+
+1454
+01:02:41,960 --> 01:02:51,000
+that includes text it includes latex um
+
+1455
+01:02:47,720 --> 01:02:53,799
+you know equations it includes code but
+
+1456
+01:02:51,000 --> 01:02:58,559
+it also included things like molecular
+
+1457
+01:02:53,799 --> 01:03:01,799
+structures and uh like collagens and DNA
+
+1458
+01:02:58,559 --> 01:03:04,160
+and stuff like this so they tried to
+
+1459
+01:03:01,799 --> 01:03:06,160
+like model biology and other things like
+
+1460
+01:03:04,160 --> 01:03:08,079
+this as well so I I think it's really
+
+1461
+01:03:06,160 --> 01:03:10,640
+kind of too bad that this model got a a
+
+1462
+01:03:08,079 --> 01:03:12,400
+bad WAP because I I really like the you
+
+1463
+01:03:10,640 --> 01:03:14,839
+know the work that went into it and I
+
+1464
+01:03:12,400 --> 01:03:16,359
+hope we'll see more of this um because
+
+1465
+01:03:14,839 --> 01:03:17,640
+language models for science is a really
+
+1466
+01:03:16,359 --> 01:03:19,880
+big topic that a lot of people are
+
+1467
+01:03:17,640 --> 01:03:19,880
+thinking
+
+1468
+01:03:20,760 --> 01:03:24,240
+about
+
+1469
+01:03:22,400 --> 01:03:26,440
+cool
+
+1470
+01:03:24,240 --> 01:03:28,000
+um one thing I didn't talk about is
+
+1471
+01:03:26,440 --> 01:03:29,880
+multimodal models but I hope to talk
+
+1472
+01:03:28,000 --> 01:03:32,440
+about multimodal models in a a future
+
+1473
+01:03:29,880 --> 01:03:33,359
+class so um I'll I'll talk more about
+
+1474
+01:03:32,440 --> 01:03:38,680
+that
+
+1475
+01:03:33,359 --> 01:03:41,640
+soon um the next thing is Clos models um
+
+1476
+01:03:38,680 --> 01:03:44,480
+so Clos models we don't know a whole lot
+
+1477
+01:03:41,640 --> 01:03:46,880
+about them uh most of what we know about
+
+1478
+01:03:44,480 --> 01:03:49,480
+them in their training data and other
+
+1479
+01:03:46,880 --> 01:03:52,359
+things like that is their uh is
+
+1480
+01:03:49,480 --> 01:03:54,720
+conjecture so the
+
+1481
+01:03:52,359 --> 01:03:57,839
+standard the standard format for
+
+1482
+01:03:54,720 --> 01:03:59,599
+releasing in a closed model or not
+
+1483
+01:03:57,839 --> 01:04:02,160
+releasing but you know publicizing a
+
+1484
+01:03:59,599 --> 01:04:04,279
+closed model is people will write a blog
+
+1485
+01:04:02,160 --> 01:04:05,960
+post and they'll write a paper and
+
+1486
+01:04:04,279 --> 01:04:07,720
+generally what the paper does is it only
+
+1487
+01:04:05,960 --> 01:04:09,559
+talks about evaluation it only talks
+
+1488
+01:04:07,720 --> 01:04:12,039
+about like how good the model is on
+
+1489
+01:04:09,559 --> 01:04:13,799
+various things how safe it is how they
+
+1490
+01:04:12,039 --> 01:04:16,279
+put a lot of effort into red teeming the
+
+1491
+01:04:13,799 --> 01:04:17,680
+model uh so that it doesn't do bad
+
+1492
+01:04:16,279 --> 01:04:18,839
+things and stuff like that and it tells
+
+1493
+01:04:17,680 --> 01:04:21,119
+you nothing about how they actually
+
+1494
+01:04:18,839 --> 01:04:23,279
+built the model so mostly like what I
+
+1495
+01:04:21,119 --> 01:04:26,279
+can talk about are capabilities as
+
+1496
+01:04:23,279 --> 01:04:28,520
+opposed to um
+
+1497
+01:04:26,279 --> 01:04:32,440
+talk about our capabilities as opposed
+
+1498
+01:04:28,520 --> 01:04:35,319
+to like what actually went into the
+
+1499
+01:04:32,440 --> 01:04:38,920
+model so um there's
+
+1500
+01:04:35,319 --> 01:04:40,880
+gp4 um gp4 I think everybody knows it's
+
+1501
+01:04:38,920 --> 01:04:43,640
+kind of the de facto standard strong
+
+1502
+01:04:40,880 --> 01:04:45,680
+language model it used to be the only
+
+1503
+01:04:43,640 --> 01:04:47,680
+strong language model like it used to be
+
+1504
+01:04:45,680 --> 01:04:50,079
+on its own the strongest language model
+
+1505
+01:04:47,680 --> 01:04:53,160
+and there were no real competitors to
+
+1506
+01:04:50,079 --> 01:04:55,000
+gp4 from that point of view I think
+
+1507
+01:04:53,160 --> 01:04:56,680
+still if I wanted a strong language
+
+1508
+01:04:55,000 --> 01:04:58,960
+model for just something that I'm I'm
+
+1509
+01:04:56,680 --> 01:05:00,880
+going to do randomly I still rely on G I
+
+1510
+01:04:58,960 --> 01:05:03,680
+still trust gp4 more than anything else
+
+1511
+01:05:00,880 --> 01:05:05,240
+to give me a really good answer um but
+
+1512
+01:05:03,680 --> 01:05:08,480
+there are now other competitors I'd like
+
+1513
+01:05:05,240 --> 01:05:11,960
+to talk about so gp4 anyway um you know
+
+1514
+01:05:08,480 --> 01:05:14,240
+it Powers the pro version of chat GPT it
+
+1515
+01:05:11,960 --> 01:05:18,039
+was tuned to be good as a chat-based
+
+1516
+01:05:14,240 --> 01:05:20,440
+assistant um it accepts image inputs uh
+
+1517
+01:05:18,039 --> 01:05:22,279
+and it supports calling external tools
+
+1518
+01:05:20,440 --> 01:05:23,599
+through function calling uh through a
+
+1519
+01:05:22,279 --> 01:05:27,119
+function calling
+
+1520
+01:05:23,599 --> 01:05:28,720
+interface um
+
+1521
+01:05:27,119 --> 01:05:30,599
+I I think people are are generally
+
+1522
+01:05:28,720 --> 01:05:34,000
+familiar with this but just in case
+
+1523
+01:05:30,599 --> 01:05:36,240
+you're not um I'd like to show a few
+
+1524
+01:05:34,000 --> 01:05:38,039
+things that I like to
+
+1525
+01:05:36,240 --> 01:05:39,640
+do
+
+1526
+01:05:38,039 --> 01:05:42,760
+so let
+
+1527
+01:05:39,640 --> 01:05:42,760
+[Music]
+
+1528
+01:05:46,920 --> 01:05:52,480
+me so I'll just randomly grab one of my
+
+1529
+01:05:50,440 --> 01:05:57,640
+papers from
+
+1530
+01:05:52,480 --> 01:05:57,640
+archive um my Mo my most recent paper
+
+1531
+01:06:03,400 --> 01:06:07,559
+and I can copy paste
+
+1532
+01:06:13,200 --> 01:06:22,240
+this and write uh turn this into Json
+
+1533
+01:06:19,240 --> 01:06:22,240
+forat
+
+1534
+01:06:27,960 --> 01:06:31,640
+and I drop it in
+
+1535
+01:06:29,880 --> 01:06:35,480
+here
+
+1536
+01:06:31,640 --> 01:06:38,279
+and so this is an exhibit of its like
+
+1537
+01:06:35,480 --> 01:06:42,240
+multimodal abilities because I can throw
+
+1538
+01:06:38,279 --> 01:06:44,359
+in a uh in a
+
+1539
+01:06:42,240 --> 01:06:48,400
+table and it basically turns it into
+
+1540
+01:06:44,359 --> 01:06:50,599
+Json clat for so um I I actually turned
+
+1541
+01:06:48,400 --> 01:06:52,119
+a fair amount of data FR in that I
+
+1542
+01:06:50,599 --> 01:06:53,960
+created in creating these slides into
+
+1543
+01:06:52,119 --> 01:06:56,039
+Json format so I can save it later for
+
+1544
+01:06:53,960 --> 01:06:59,079
+whatever I want it for and I did it
+
+1545
+01:06:56,039 --> 01:07:01,720
+through uh this so this is an example of
+
+1546
+01:06:59,079 --> 01:07:06,599
+the multimodal abilities can also tell
+
+1547
+01:07:01,720 --> 01:07:06,599
+you about images and stuff like that
+
+1548
+01:07:07,000 --> 01:07:14,319
+um so also um there was a famous article
+
+1549
+01:07:11,760 --> 01:07:16,760
+written by Gary Marcus that said deep
+
+1550
+01:07:14,319 --> 01:07:19,760
+learning is hitting a wall um it
+
+1551
+01:07:16,760 --> 01:07:22,880
+basically was written two years ago and
+
+1552
+01:07:19,760 --> 01:07:25,160
+uh Gary Marcus was saying deep learning
+
+1553
+01:07:22,880 --> 01:07:26,200
+doesn't uh you know is not the way for
+
+1554
+01:07:25,160 --> 01:07:27,760
+the future sure we're going to need
+
+1555
+01:07:26,200 --> 01:07:31,319
+things other than deep learning in order
+
+1556
+01:07:27,760 --> 01:07:34,559
+to uh you know be able to uh make
+
+1557
+01:07:31,319 --> 01:07:36,400
+progress and whe whether you believe
+
+1558
+01:07:34,559 --> 01:07:40,520
+that is true or not I I will let you to
+
+1559
+01:07:36,400 --> 01:07:46,520
+your own opinion um but uh I could also
+
+1560
+01:07:40,520 --> 01:07:51,359
+say uh create a picture of deep learning
+
+1561
+01:07:46,520 --> 01:07:55,400
+breaking through a brick wall and it can
+
+1562
+01:07:51,359 --> 01:07:55,400
+generate images for you
+
+1563
+01:08:02,599 --> 01:08:07,440
+course if you ever do a live demo even
+
+1564
+01:08:05,319 --> 01:08:10,319
+if it's a live demo of open AI product
+
+1565
+01:08:07,440 --> 01:08:13,559
+that a million people use it will break
+
+1566
+01:08:10,319 --> 01:08:16,719
+when you try to do it so um so this is
+
+1567
+01:08:13,559 --> 01:08:17,799
+another uh thing that it can do so there
+
+1568
+01:08:16,719 --> 01:08:19,560
+we have a picture of deep learning
+
+1569
+01:08:17,799 --> 01:08:22,640
+breaking through a brick wall and it can
+
+1570
+01:08:19,560 --> 01:08:26,159
+you know generate images and stuff so
+
+1571
+01:08:22,640 --> 01:08:28,560
+these are like the kinds of things that
+
+1572
+01:08:26,159 --> 01:08:30,960
+I now
+
+1573
+01:08:28,560 --> 01:08:32,880
+expect so it's not just like reasoning
+
+1574
+01:08:30,960 --> 01:08:35,839
+ability and other stuff like that it's
+
+1575
+01:08:32,880 --> 01:08:39,199
+also multi multimodality being able to
+
+1576
+01:08:35,839 --> 01:08:43,679
+generate code um another thing that's
+
+1577
+01:08:39,199 --> 01:08:46,719
+kind of nice um is make a
+
+1578
+01:08:43,679 --> 01:08:49,440
+histogram of these
+
+1579
+01:08:46,719 --> 01:08:54,640
+numbers one
+
+1580
+01:08:49,440 --> 01:08:54,640
+two one two four
+
+1581
+01:08:57,600 --> 01:09:04,040
+so it can do code generation and and
+
+1582
+01:08:59,719 --> 01:09:05,560
+display the results for you um there are
+
+1583
+01:09:04,040 --> 01:09:08,319
+efforts to
+
+1584
+01:09:05,560 --> 01:09:12,239
+make open source language models be able
+
+1585
+01:09:08,319 --> 01:09:14,000
+to do these things and um in order to do
+
+1586
+01:09:12,239 --> 01:09:16,759
+this you need multimodality you need
+
+1587
+01:09:14,000 --> 01:09:19,359
+also the ability to use tools so
+
+1588
+01:09:16,759 --> 01:09:21,400
+actually the way that this um worked
+
+1589
+01:09:19,359 --> 01:09:24,520
+here is very different than the way that
+
+1590
+01:09:21,400 --> 01:09:27,920
+this worked so this is actually using a
+
+1591
+01:09:24,520 --> 01:09:29,759
+image input into gp4 so what it's doing
+
+1592
+01:09:27,920 --> 01:09:33,040
+is it's encoding the image and then
+
+1593
+01:09:29,759 --> 01:09:34,719
+feeding it in as tokens into gp4 what
+
+1594
+01:09:33,040 --> 01:09:37,920
+this is doing here is this is rather
+
+1595
+01:09:34,719 --> 01:09:40,120
+calling a tool this is calling uh dolly3
+
+1596
+01:09:37,920 --> 01:09:42,120
+as a tool and it's providing the caption
+
+1597
+01:09:40,120 --> 01:09:46,880
+to Dolly 3 you can even see maybe the
+
+1598
+01:09:42,120 --> 01:09:46,880
+caption that was provided to
+
+1599
+01:09:48,640 --> 01:09:55,560
+dolly3 you you previously were able to
+
+1600
+01:09:51,239 --> 01:09:57,960
+do that um by maybe downloading yeah so
+
+1601
+01:09:55,560 --> 01:10:01,600
+you can see the the
+
+1602
+01:09:57,960 --> 01:10:01,600
+caption uh which
+
+1603
+01:10:03,560 --> 01:10:08,120
+was a visual metaphor of deep learning
+
+1604
+01:10:06,320 --> 01:10:10,679
+is a powerful force breaking through a
+
+1605
+01:10:08,120 --> 01:10:13,400
+brick wall um or something like that and
+
+1606
+01:10:10,679 --> 01:10:15,480
+so gp4 basically what it did is it it
+
+1607
+01:10:13,400 --> 01:10:18,000
+said it wanted to call a tool and then
+
+1608
+01:10:15,480 --> 01:10:19,360
+it g provided the caption uh the caption
+
+1609
+01:10:18,000 --> 01:10:21,280
+and then it called it completely
+
+1610
+01:10:19,360 --> 01:10:22,320
+separate tool as an API in order to
+
+1611
+01:10:21,280 --> 01:10:27,320
+generate the
+
+1612
+01:10:22,320 --> 01:10:27,320
+image so um yeah the final
+
+1613
+01:10:28,199 --> 01:10:34,080
+well I managed to break chat gbt that's
+
+1614
+01:10:30,120 --> 01:10:36,520
+no small accomplishment um so but anyway
+
+1615
+01:10:34,080 --> 01:10:40,199
+these are some of the things that uh
+
+1616
+01:10:36,520 --> 01:10:42,360
+that the systems can do and because open
+
+1617
+01:10:40,199 --> 01:10:47,000
+AI has kind of become a standard that a
+
+1618
+01:10:42,360 --> 01:10:50,040
+lot of people want to uh compete with um
+
+1619
+01:10:47,000 --> 01:10:53,480
+also I would say Gemini Gemini and Claud
+
+1620
+01:10:50,040 --> 01:10:56,400
+are maybe the two um the two models that
+
+1621
+01:10:53,480 --> 01:10:59,440
+can compete with gp4 and terms of uh you
+
+1622
+01:10:56,400 --> 01:11:02,600
+know accuracy Gemini is a much newer
+
+1623
+01:10:59,440 --> 01:11:06,159
+model by Google that uh comes in two
+
+1624
+01:11:02,600 --> 01:11:08,280
+varieties Gemini Pro and Gemini Ultra uh
+
+1625
+01:11:06,159 --> 01:11:11,040
+one interesting thing about Gemini Pro
+
+1626
+01:11:08,280 --> 01:11:13,560
+is that it supports um very long inputs
+
+1627
+01:11:11,040 --> 01:11:15,679
+one to 10 million tokens it also
+
+1628
+01:11:13,560 --> 01:11:16,600
+supports image and video inputs and
+
+1629
+01:11:15,679 --> 01:11:20,239
+image
+
+1630
+01:11:16,600 --> 01:11:22,320
+outputs um I actually put a a video into
+
+1631
+01:11:20,239 --> 01:11:24,600
+it recently and the video recognition
+
+1632
+01:11:22,320 --> 01:11:27,159
+capabilities are pretty pretty nice so
+
+1633
+01:11:24,600 --> 01:11:29,280
+you can uh you can try that out if you
+
+1634
+01:11:27,159 --> 01:11:34,320
+want
+
+1635
+01:11:29,280 --> 01:11:36,640
+um and finally there's Claud it pla 3 it
+
+1636
+01:11:34,320 --> 01:11:39,280
+supports a context window of up to 200k
+
+1637
+01:11:36,640 --> 01:11:41,040
+also allows for processing images and
+
+1638
+01:11:39,280 --> 01:11:46,480
+overall has strong results competitive
+
+1639
+01:11:41,040 --> 01:11:49,880
+with gd4 so if you're looking for um if
+
+1640
+01:11:46,480 --> 01:11:51,480
+you're looking for models to use uh to
+
+1641
+01:11:49,880 --> 01:11:53,600
+try out better closed models you can
+
+1642
+01:11:51,480 --> 01:11:55,719
+definitely use these another thing I'm
+
+1643
+01:11:53,600 --> 01:11:58,239
+really excited about is how can we get
+
+1644
+01:11:55,719 --> 01:11:59,560
+like open models to you know demonstrate
+
+1645
+01:11:58,239 --> 01:12:01,320
+some of the interesting capabilities
+
+1646
+01:11:59,560 --> 01:12:02,840
+that we see in closed models so you know
+
+1647
+01:12:01,320 --> 01:12:07,120
+everybody can benefit and everybody
+
+1648
+01:12:02,840 --> 01:12:10,040
+knows uh you know uh the recipes to make
+
+1649
+01:12:07,120 --> 01:12:12,560
+models like this so I think that's
+
+1650
+01:12:10,040 --> 01:12:16,639
+mostly all I have for today another um
+
+1651
+01:12:12,560 --> 01:12:23,440
+another thing that is kind of neat
+
+1652
+01:12:16,639 --> 01:12:23,440
+is I just found this a little while ago
+
+1653
+01:12:28,800 --> 01:12:32,239
+but there is this uh
+
+1654
+01:12:33,320 --> 01:12:39,239
+interface uh called the god mode that
+
+1655
+01:12:36,880 --> 01:12:41,960
+allows you to put all of the chat apps
+
+1656
+01:12:39,239 --> 01:12:45,840
+next to each other and write the same
+
+1657
+01:12:41,960 --> 01:12:47,080
+chat query into them and uh and get the
+
+1658
+01:12:45,840 --> 01:12:48,719
+result from all of them so you can
+
+1659
+01:12:47,080 --> 01:12:51,080
+actually compare all of them in kind of
+
+1660
+01:12:48,719 --> 01:12:52,840
+an interactive settings so if you want
+
+1661
+01:12:51,080 --> 01:12:54,800
+to look at all especially all of the
+
+1662
+01:12:52,840 --> 01:12:56,679
+closed models open models it's you know
+
+1663
+01:12:54,800 --> 01:12:58,239
+not too are to do it yourself but if you
+
+1664
+01:12:56,679 --> 01:12:59,840
+want to try all of the Clos models
+
+1665
+01:12:58,239 --> 01:13:01,800
+together you can do that and like log
+
+1666
+01:12:59,840 --> 01:13:03,960
+into all of your accounts and then press
+
+1667
+01:13:01,800 --> 01:13:05,320
+go on aquery and see how they all this F
+
+1668
+01:13:03,960 --> 01:13:07,960
+so
+
+1669
+01:13:05,320 --> 01:13:09,800
+um that might be a good way to compare
+
+1670
+01:13:07,960 --> 01:13:12,000
+all of the models kind of qualitatively
+
+1671
+01:13:09,800 --> 01:13:14,679
+as opposed to
+
+1672
+01:13:12,000 --> 01:13:17,280
+qualitatively cool um that's all I have
+
+1673
+01:13:14,679 --> 01:13:19,440
+for today uh I don't know are there any
+
+1674
+01:13:17,280 --> 01:13:23,440
+questions or discussion or things like
+
+1675
+01:13:19,440 --> 01:13:23,440
+this yeah
+
+1676
+01:13:28,840 --> 01:13:35,679
+so a systematic way um the first thing
+
+1677
+01:13:32,760 --> 01:13:37,960
+you can do is look at the Benchmark
+
+1678
+01:13:35,679 --> 01:13:40,800
+results that have been published but
+
+1679
+01:13:37,960 --> 01:13:43,320
+actually I would like to give a caveat
+
+1680
+01:13:40,800 --> 01:13:43,320
+about
+
+1681
+01:13:45,199 --> 01:13:48,440
+this which
+
+1682
+01:13:50,000 --> 01:13:54,000
+is um
+
+1683
+01:14:22,960 --> 01:14:28,239
+so these are are the best bench marking
+
+1684
+01:14:25,600 --> 01:14:30,840
+results for the Gemini
+
+1685
+01:14:28,239 --> 01:14:33,440
+paper um
+
+1686
+01:14:30,840 --> 01:14:36,719
+and they have a table here um and
+
+1687
+01:14:33,440 --> 01:14:38,679
+basically what they kind of obviously to
+
+1688
+01:14:36,719 --> 01:14:41,679
+me wanted to demonstrate is that Gemini
+
+1689
+01:14:38,679 --> 01:14:44,760
+was the best model out of all the models
+
+1690
+01:14:41,679 --> 01:14:47,800
+um and so they have Gemini Pro and
+
+1691
+01:14:44,760 --> 01:14:50,040
+Gemini Ultra and they put Gemini Pro
+
+1692
+01:14:47,800 --> 01:14:52,639
+Ultra against gp4 and Gemini Pro against
+
+1693
+01:14:50,040 --> 01:14:56,360
+GPT 3.5 because they're you know
+
+1694
+01:14:52,639 --> 01:14:58,440
+comparable models um
+
+1695
+01:14:56,360 --> 01:15:01,880
+and they're yeah because they're
+
+1696
+01:14:58,440 --> 01:15:03,040
+comparable models basically and on
+
+1697
+01:15:01,880 --> 01:15:05,880
+things
+
+1698
+01:15:03,040 --> 01:15:07,400
+like um and they demonstrate that
+
+1699
+01:15:05,880 --> 01:15:08,199
+basically they're better in all all of
+
+1700
+01:15:07,400 --> 01:15:10,520
+these
+
+1701
+01:15:08,199 --> 01:15:14,760
+situations however there's a few details
+
+1702
+01:15:10,520 --> 01:15:17,120
+the first detail is um that the method
+
+1703
+01:15:14,760 --> 01:15:20,199
+that they're using to prompt the model
+
+1704
+01:15:17,120 --> 01:15:22,120
+is different here so we have like 94.4
+
+1705
+01:15:20,199 --> 01:15:23,560
+versus 92 but the method they're using
+
+1706
+01:15:22,120 --> 01:15:25,520
+to prompt the model is different they're
+
+1707
+01:15:23,560 --> 01:15:29,159
+using they're
+
+1708
+01:15:25,520 --> 01:15:33,320
+32 and then basically uh getting the
+
+1709
+01:15:29,159 --> 01:15:36,320
+best from 32 and then another thing
+
+1710
+01:15:33,320 --> 01:15:41,360
+is if we look at this Human ofal
+
+1711
+01:15:36,320 --> 01:15:44,120
+Performance here um they reported their
+
+1712
+01:15:41,360 --> 01:15:47,000
+Human ofel Performance then they pulled
+
+1713
+01:15:44,120 --> 01:15:49,400
+the number from the original gp4 paper
+
+1714
+01:15:47,000 --> 01:15:53,159
+and compared to the number from the gp4
+
+1715
+01:15:49,400 --> 01:15:54,639
+paper but all of these um you know apis
+
+1716
+01:15:53,159 --> 01:15:57,719
+are constantly changing they're getting
+
+1717
+01:15:54,639 --> 01:15:59,480
+better and better so we went um I I was
+
+1718
+01:15:57,719 --> 01:16:01,400
+very excited when Gemini first came out
+
+1719
+01:15:59,480 --> 01:16:03,120
+and we actually wrote a paper where we
+
+1720
+01:16:01,400 --> 01:16:05,320
+tried to look deeper into the
+
+1721
+01:16:03,120 --> 01:16:08,000
+performance and what we actually found
+
+1722
+01:16:05,320 --> 01:16:10,199
+is comparing Gemini Pro and GPT 3.5
+
+1723
+01:16:08,000 --> 01:16:12,719
+turbo which should be comparable we
+
+1724
+01:16:10,199 --> 01:16:16,120
+found that actually GPT 3.5 turbo did a
+
+1725
+01:16:12,719 --> 01:16:19,280
+little bit better um in in most cases
+
+1726
+01:16:16,120 --> 01:16:20,920
+although not all cases and one of the
+
+1727
+01:16:19,280 --> 01:16:24,000
+things we noticed in particular is like
+
+1728
+01:16:20,920 --> 01:16:27,960
+human ofel GPD 3.5 had gotten like much
+
+1729
+01:16:24,000 --> 01:16:29,760
+much better over the course of uh like
+
+1730
+01:16:27,960 --> 01:16:31,639
+the time between the original paper was
+
+1731
+01:16:29,760 --> 01:16:34,120
+reported it had gone up by almost 30
+
+1732
+01:16:31,639 --> 01:16:35,760
+points and also in a few cases we had
+
+1733
+01:16:34,120 --> 01:16:37,480
+like a little bit of trouble reproducing
+
+1734
+01:16:35,760 --> 01:16:39,280
+the Gemini Pro results just because they
+
+1735
+01:16:37,480 --> 01:16:40,360
+had like safety filters and other stuff
+
+1736
+01:16:39,280 --> 01:16:42,520
+like that that we had to get around
+
+1737
+01:16:40,360 --> 01:16:45,280
+before we got the results so it's not
+
+1738
+01:16:42,520 --> 01:16:49,560
+necessarily the case that you can
+
+1739
+01:16:45,280 --> 01:16:52,639
+completely take the um that you can
+
+1740
+01:16:49,560 --> 01:16:55,560
+completely take the results on face
+
+1741
+01:16:52,639 --> 01:16:57,040
+value actually as a first St I would
+
+1742
+01:16:55,560 --> 01:17:00,080
+suggest just trying to chat with the
+
+1743
+01:16:57,040 --> 01:17:03,719
+model um which is also why I introduced
+
+1744
+01:17:00,080 --> 01:17:06,679
+the like quote unquote god mode uh like
+
+1745
+01:17:03,719 --> 01:17:09,159
+browser because like you can kind of
+
+1746
+01:17:06,679 --> 01:17:10,639
+tell when it like when something's way
+
+1747
+01:17:09,159 --> 01:17:14,320
+better than another one just by the
+
+1748
+01:17:10,639 --> 01:17:17,159
+respones ites um separately if you want
+
+1749
+01:17:14,320 --> 01:17:17,159
+to do it much more
+
+1750
+01:17:20,199 --> 01:17:23,840
+systematically there are really nice
+
+1751
+01:17:22,360 --> 01:17:25,400
+tools for evaluation I think I might
+
+1752
+01:17:23,840 --> 01:17:26,960
+have talked about this before but if I
+
+1753
+01:17:25,400 --> 01:17:29,280
+haven't then you should definitely take
+
+1754
+01:17:26,960 --> 01:17:31,880
+a look at this there's the alther
+
+1755
+01:17:29,280 --> 01:17:34,040
+evaluation harness and the alther
+
+1756
+01:17:31,880 --> 01:17:35,679
+evaluation harness makes it really easy
+
+1757
+01:17:34,040 --> 01:17:37,600
+to evaluate for example hugging face
+
+1758
+01:17:35,679 --> 01:17:39,040
+models against many many different tasks
+
+1759
+01:17:37,600 --> 01:17:41,360
+so you can just pick which task you want
+
+1760
+01:17:39,040 --> 01:17:43,719
+to evaluate against pick the model name
+
+1761
+01:17:41,360 --> 01:17:47,400
+and and go and you can get evaluation
+
+1762
+01:17:43,719 --> 01:17:51,960
+results um that won't necessarily work
+
+1763
+01:17:47,400 --> 01:17:53,960
+for close models um but if you look for
+
+1764
+01:17:51,960 --> 01:17:55,480
+Uther language model evaluation harness
+
+1765
+01:17:53,960 --> 01:17:58,800
+that's maybe the easiest way to run
+
+1766
+01:17:55,480 --> 01:17:58,800
+evaluations or s for
+
+1767
+01:17:59,239 --> 01:18:05,239
+L Cool okay um so we're we're at time
+
+1768
+01:18:02,960 --> 01:18:07,480
+now uh but I'd be happy to answer a few
+
+1769
+01:18:05,239 --> 01:18:10,639
+questions if anybody else has any so
+
+1770
+01:18:07,480 --> 01:18:10,639
+thank you
\ No newline at end of file