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- How language models workAn explanation without the math: how a language model guesses its way forward one word at a time, what attention really is, and why it can sound convincing even when it is wrong.Foundations12 min
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%# ======================= ======================= ======================= ## @# # # | | | | ## %# # meeting-notes # | pptx | | docx | ## @# # # | | ## %# # # | | | | ## @# # # | | | | ## %# # # | | | | ## @# # # | | | | ## %# ####################### ####################| |################# ## @# | | ## %# ==#===#===#===#===#===# | |==#===#===#===#== ## @# # # | | | ## %# # xlsx # | | pdf | ## @# # # | ## %# # # | ## @# # # meeting-notes/ | ## %# # # | ## LAG 2 ved utlosning @# # # | ## %# =-=-=-=-=-=-=-=-=-=-=-= =-=-=-=-=-=-=-=-=-= ## @# ## %# ======================= ====== =================== ## @# # # | | ## %# # commit # | stilguide | | budsjett | ## @# # # | | | | ## %# # # | | | | ## @# # # | | | | ## %# # # | | | | ## @# # # | | | | ## %# ####################### ####################### ####################### ## LAG 3 ved behov @# ## %# #=#=#=#=#=#=#=#=#=#=#=# =#=#=#=#=#=#=#=#=#=#=#= #=#=#=#=#=#=#=#=#=#=#=# ## @# # # | | | | ## %# # api-docs # | referat | | valider | ## @# # # | | | | ## %# # # | | | | ## @# # # | | | | ## %# # # | | | | ## @# # # | | | | ## %# =-=-=-=-=-=-=-=-=-=-=-= -=-=-=-=-=-=-=-=-=-=-=- =-=-=-=-=-=-=-=-=-=-=-= ## @# ## %################################################################################ #=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#+ + |--------------------------------------------------------| --- name | | | LAG 1 alltid i kontekst | | | | | |--------------------------------------------------------| @%@%@%@%@%@%@%@%@%@%@%@ #|||||||:|||:|||:|||||% %:| |||||% #|||||||||||:|||:|||||% %||SKILL.md|||||||||||% #|||||||:|||:|||:|||||% %:||||||||||||||||||||% #|||||||||||:|||:|||||% %#%#%#%#%#%#%#%#%#%#%#% + + - How tokens work in language modelsWhy can’t ChatGPT count the letters in “strawberry”? The answer is tokens: the pieces of text the model actually reads, never the letters themselves.Foundations11 min
53,16768,1900,972,120674,19112,2541,1705,86818,19177,7430,158429,11,123126,93775,2085,6323,10726,154714,56976,2541,1796,12114,613,6964,59675,1305,1452,1476,7086,1037,2541,3 2662,24491,50275,1452,1476,650,3944,411,1745 ,59576,264,13,3983,991,638,3148,2807,6964,155515,59079,11,55719,2085,64714,11,1111,334,163436,395,44883,87936,599,411,6212,3825 11,1796,101740,36784,14735,322, 363,2085,20341,12,2243,8068,5806 22823,41026,1401,11,26134,29 1,18173,11,49921,1796,150794, 1347,1475,91392,15570,13,1 7 4,3453,2674,49138,102300,11, 27603,19177,158502,5770,13 1705,86818,19177,7430,15842 1452,1476,7086,1037,2541,345 ,3944,411,17458,59576,264,13,3 44883,87936,599,411,6212,38258, 3 322,7466,1072,13965,19474,11,163 154961,11,178001,805,76807,11,22823, 6 34,292,1401,2085,49764,145982,85620,3 575,7967,1900,334,3535,5172,1347,1475,91392,15570,13,1754,1066,48124,169082,259,16023,17764,3453,2674,49138,102300,11,1014,1291,1401,1452,117302,2085,2750,259,11,2085,1625,================================================================================================================================================================================ | | | | | | | | | | | | | | | | | | | | | "strawberry" -> gpt-4o -> 3 tokens | | --- =- - | | --===== x\#\\\==--- | | --- :\\x \xx | x\\ \\\\ =- | | \ \: | \:\ :: | | | | | . | | | | | | ' | | 302 1618 19772 | | | | . | | | | | | ' | | how many r's? | | | | . | | | | | | ' | | the model never sees the letters, | | only the token ids. | | . | | | | | | ' | | [16876] -> [ -0.332, -0.114, 0.640, 0.045, 0.854, ... 768 ] | | | | . | | | | | | | | | | | | | | | | | | | | | | | | | ================================================================================================================================================================================/ | | | | ##% | %## -=-==#%## . . . . +-----+ | raw | +-----+ ' ' ' ' ' . . . . ' ' ' ' ' . . . . s t a w b e y ' ' ' ' ' . . . . ' ' ' . . ' ' .+ + @% @% |# \\ # #%@% @@@% #### \ %%#@@%%@@@@@# |%%#xx\\ ### @%#%@ %#@@% #%#|x xx\ +----+ +-------+ xx#@@#%@@@%#%%@#%#@#|x##x\ | st | | berry | x%%%#%@@%#####@%##%#|x##\\x +----+ +-------+ #% ##@@@ ##%## @#%#@ |x#x\ x x#x##@@%#%%@%@##%%##%#|x##\\x xx%@@###@@@@@@@#%#%|%#||##x\x x%%### @@@%% @%##% |##|| #x\\ x####%@@%%###%%@#%%%|%##|xx#\\ //##@%%########%%#%#|#%#|\xxx\ x#%% ##%%@ @@%#% #%%|# %#x\\ x xxxx##@@@%@%@@#|@|%#||####x\\\ x#%%@%######@@#%#%#|||||#x#\\ ####x# %#%## @#%#% ||||| \\xx r r r /x##%%@@@@@%##@#%%#|||||xxx\ ^ ^ ^ ##%#######@%#%#%%######xx\x xx ####% ##@%# @###% |\\\\ x#%%%@%@@%x@|@###%#||#xx\ xxxx#x###%%x@%#|#%||#\\\x xx# %%%## %#||| #|#|x \ ######@%#%###|###|#\x\ /xx###x#%#%@%##|#|\x\ x### @%#%# ###|| #x\ /xx#x#%x##|%|||#x\ x##%%#%##%###||\\ /xxx #%%|| ||\\ x%%###x%##||\x /xx%#%|#|#|\ x%#% %|#|| /x%%##||x x%%###|x x### | + +:\ x\:: :\ \ : x\:\... \:\... \\:: : \\\.:..: \\\:\... x \.\ : x\:\.:..: \\:\.: : \\\: .:.. x:\:....: \\:\:..: \ \.... \\\:::... \\.\: . \:\:. : \\\:... \\:..: : :\:. :.. \::.:: x\:...: \\ . :: :\:. \\... x :.:: \\:. :\: . \... x\. ::. \.. - Hyperparameters in the prompting processTemperature and Top P are usually explained as “randomness”. What they actually do to the words a model chooses between, and why temperature 0 doesn’t always give the best answers.Foundations9 min
p=0.742 z=1.14 top_p=0. p=0.6 p=exp(z/T)/Σexp(z/T) T=0.6 p=0.182 p=0.16 top_p=0.6 p=0.002 T=0.6 T=0.6 T=0.6 T=0.6 T= T=0.6 p=0.332 z=0.62 T= . = (z/T)/Σexp(z/T) top_p=0.6 T=1.0 top_p=0.6 T=0.6 p=0.246 T=1.0 top_p=0.6 p=0.748 top_p=0.6 T=1.0 T=0.6 Σp≥0.6 T=1.0 T=0. 0. 0.6 T=1.0 p=exp(z/T)/Σexp(z/T) p=0.638 T=0.6 T=0.6 p=exp(z/T)/Σexp(z/T) p=exp(z/T)/Σexp(z/T) top_p=0 T=0.6 top_p=0.6 T=1.0 T . top_p=0.6 p=0.782 p=0 z=1.6 6 p_p=0.6 z=-1.41 z=-1.24 top_p=0.6 z=0.28 Σp≥0.6 T=1 p=0.346 T=0.6 T=1.0 Σp≥ . to p=0.6 Σp≥0.6 T=1.0 z=0.74 T=0.6 z=-2.04 T=0.6 top_p=0.6 T=0.6 top_p=0.6 top_p=0.6 p=exp(z/T)/Σ T=1.0 T=1.0 z=2.14 p=ex 0 Σp≥0.6 T=0.6 z=-0.49 T=0.6 T=0.6 T=1.0 top_p=0.6 Σp≥0.6 Σp≥0.6 T=0.6 z=-0.04 T=0.6 T=1.0 T T=1.0 z=-2.14 T=1.0 top 6 T z/ /Σexp(z/T) T=0.6 top_p=0.6 z=0.65 p=exp(z/T)/Σexp(z/T) T=1.0 T=1.0 T=1.0 Σp≥0.6 Σp≥0.6 z=-0.27 p=0.015 z=1.85 top_p=0.6 = 65 z=-2.6 z=1.67 T=0.6 top_p=0.6 top_p=0.6 p=exp(z/T)/Σexp(z/T) p=exp(z/T)/Σexp(z/T) p=exp(z/T)/Σexp p=exp(z/T)/Σexp(z/T) p=ex .87 z=2.37 Σp≥0.6 p=exp(z/T)/Σexp(z/T) p=exp(z/T)/Σexp(z/T) T=1.0 z=-2.65 p=exp(z/T)/Σexp(z/T) Σp≥.:. .:..::.:|: ::. :: .:. ::. ::..|...:...: . .:. .|..:|.:|: ::. ::. .:. ::. ::..|...: .: .:. .:..::.: :: .:. ::. :: . .:. .|..:| . .:. ::. :: .:. .:..:: .:. ::. :: . .:. .|..:|.: ::. .:. ..|.. : .: . :: . |...:...: . . |: ::. ::. . |...: .: . |: ::. :: .:. ..|...:...: . .:. .| .:|.:|: ::. ::. . . .: .:. .:..:: .:. ::. :: . .:. .|..:| . .:. ::. :: .:. .:..:: .:. ::. ::.. .: . .:. .:|: :. ::. . | .: . |: ::. :: . |...:...: . . |: ::. ::. . |...: .: .:. .:|: ::. :: .:. :: ::.. .: . . . .:. ::. :: .:. .:..:: .:. ::. :: . .:. .|..:| . .:. .. .: . |: :. :: . ..: . . |: ::. ::. . |...: .: . |: ::. :: .:. ..|...:...: . .:. .| .:|.:|: ::. ::.============================================ =================================================================================================================================== | | | | | | | | | | | | | "I'm glad you're ___" | | | | | | | | 46.3% | | | | | | | | //===\. | | | xx -------------------------------------------------- 37.0% | | ||| | | #|| | | ||| | | | x/:: | | \\-=-x: ---=---=- 6.5% | | | | /x=--\\ | | | xx\| | | ||: | | ||| #=## 3.0% | | |:| | | | ///| | | \\=--// | | x/---\\ | | | x\\. === 2.0% | | ||: | | ||: | | ||: | | | #/:: | | \\-=-:: -- 1.5% | | //==-\\ | | : x\\. | | ||| | | ||| | | ||: -= 1.2% | | | #xx: | | /x=-=\: \\==-:: | | | xx\: | | || | | #|| # 1.0% | | ||: | | | x//| | | \x==-/: | | | | | | | | | ================================================================================================================================================================================| | %@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@ #=#=#=#=#=#=#=#=#=#=#=#=#=#=#=# ## @# %| %# ## @# %| %# ## @# %| %# ## @# %| %# ## @# %| %# ## @# %| : %# ## xxxx=x @# interested %| #|%%%%x# %# ## |#@###|# @# %| |##%%#xx %# ## \%x%=# @# %| %# okay ## : @# %| interested %# ## #x%#%x @# %| #|###=#\ %# ## #%#%@%%| @# following %| |#%#%#%/ %# ## ####=# @# %| %# ## : @# %| #xxx#\ okay %# excited ## |%%%@=## @# %| |%%#|#|# %# ## #|#%%%xx @# %| \x##=x %# ## @# listening %| : %# ## following x##x=x @# %| |##x%##x %# ## #%#%@%%| @# %| ##%%##xx %# here ## #x#x=x @# %| %# ## xxx### : @# %| ||%#%=xx excited %# ## |#@###|# @# safe %| ####%%xx %# ## x%x#=# @# %| %# ## : @# %#%%%#%%here%#%%%#%%%#%%%#%%%#% ###############################+ + /==- x###=xx\ |%####xx|| ####@#%||| concerned @@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@%@@@ |#%%%%#x|| ##x%-#// \-#= concerned + +/ \: | \. | | | | /. x // - Prompt engineering: It’s not about instructions, it’s about contextWhy language models don’t follow instructions the way a computer follows code, and why good prompting is about building the right context.Foundations8 min
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