#version: 0.2 Ġ Ġ ĠĠ ĠĠ ĠĠ Ġ Ċ ĠĠĠĠ i n o n e n e r a t o r e l ĊĠĠĠĠ ĠĠĠ Ġ t e s ĊĠĠĠĠ ĠĠĠĠ Ġ s Ġ = a l u t i on e d a s Ċ ĠĠĠ r e c h o d a n ĊĠĠĠĠĠĠĠĠ ĠĠĠ Ġ i r o s t a r Ġ " h e i t el f Ġ d in g i d e t Ġ c i g Ġ f Ġ p i c m p l e ĊĠĠĠĠĠĠĠĠ ĠĠĠĠ Ġ m en t Ġ in s e l o i z Ġ n p ut p e a d Ġs elf Ġt he od el m a Ġ o e x Ġt o Ġ b g e r a c t ĊĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ l a u r Ġ a on f t o onf ig Ġ w p t Ġ ( en s u l c e " , u m ) : iz e at t Ġd e or ch " : i m u n a m as s e m Ġ re on e h t q u Ġ l ( ) c k an d er s or m i s at ion Ġi s es t Ċ ĊĠĠĠĠĠĠĠ Ġ { at e o l Ġi f o t Ġ - ma ge s ize Ġ * k en ut put Ġ # Ġ ` c od p o Ġ N d e ar g r es / / Ċ ĊĠĠĠ u s Ġf or i l Ġ T Ġ [ s elf u e d en p ro ent ion ex t i f Ġ h ro m ens or as k ã ģ Ġo f or t id den at ch t h Ġde f m odel att ention in t ĊĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ Ġm odel y pe em b p s ĠN one ra in c onfig es s ur n Ġ v i st ig ht pt ion it h emb ed Ġ g er t Ġ ) ul t t urn r an k e u p Ġ A i p la y Ġs t Ġ and a b " ) t orch l ass at es ch e v e c o cod er Ġre turn ic t arg s t ype _ _ mp ort Ġn ot in put Ġt orch " " - - al l d ing Ġ O c ess ption al st ates h a ] , m ask ic e am e f orm Ġ } Ġt h Ġ C Ġ L ` ` in e . . v al f rom ã ĥ l en ran s Ġin put st r h idden ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ c on Ġ S iz er a mp Ġ lo id s ke y Ġ I Ġc on r ue t rain to ken Ġ el i r ( " v ice ã Ĥ > > Ġp ro Ġc onfig Ġ ì it ion p re en er e ight he ad mp t ĠĠĠĠ ĠĠĠĠ o s Ġ or c lass o utput : : T ensor g g Ġo utput i mage lo ck = " c al p y Ġ r ha pe Ġi mport la b f a ) , el s Ġn um Ġ ex u b d at Ġw ith as t Ġb e u se Ġi mage lay er Ġm a i x at h rans form p a Ċ Ċ Ġ P ) . e at n um " ] Ġ e r i e ct d im en ce ht t : // o w o c Ġc o e w d ict g th ass ert lo at M odel Ġ F t ext ĊĠĠĠĠ Ġ Ġ en p p as et ab le Ġ + d er k ens n ame an t al se cess or i le Ġ un [ " Ġ( ` w args i al Ġ D e p Ġto ken Ġel se l y u re en se Ġh idden ar d htt ps s s k wargs ĠO ptional Ġ" "" t est embed ding an ce Ġf rom ` , gg ing ent s ic ense if f g es in d Ġ res as e w eight od u Ġs e ar am b atch ) ) len gth # # Ġin t Ø § a ge de vice id e en d T rue o ol an n Ġ R à ¤ u es Ġ on e ps Ġ as Ġ M ip el Ġl et f t Ġ y att n po s in it ĉ ĉ c a Ġ attention v er Ġ= = che ck od e ad d o m Ġ _ Ġ ë odu le ì Ŀ ĠT he p ath ct ion s hape train ed amp le i ve a ult pe c Ġdef ault n orm f f Ù Ħ Ġ V o u Ġ use a ck input s u gging pro mpt Ġ W ipel ine Ġ B i o Ġ us b lock embed s Ġ* * o ptional `` ` í ķ e g Ġt est Ġ ' ĠL icense Ġd at Ġ x * * i se to kens f or Ġ- > lay ers it s p er ma ges ar t C onfig al ue -- -- g et r or w ard ransform ers u d o ut l i iff us cal e >> > Ġst r f eat po int t ensor embedding s ch ann ut o .. . Ġt ext val ues Ġp re fa ce ur es Ġth at Ġ // ct or head s is ion at or ma x en coder i as co m Ġb atch a pt ë ĭ Ġw h ĠĠĠĠ ĠĠĠ Ġ qu pre trained st ate pe ct it y amp l Ġ an r ed ch ed lo g j ect Ġco mp il l Ġ E lo ad ] ( Ġ U ] ] f loat ã Ģ dat a Ġth is ow n a ve v a lab els t im Ċ ĊĠĠĠĠĠĠĠĠĠĠĠ s et â Ķ qu ence i mport i v Ġ G Ġg ener g ener ugging face ers ion dat aset T o t a F or Ġinput s Ċ Ġ ) ; er n if ic Ġi t chann els Ġ @ c l Ġn n O N Ġ k d type Ġma x Ġb y Ù ħ ad apt Ġ assert ar y c or Ġ | Ġ H . _ h uggingface pos ition m ent lo w d ir con v g u Ġa re f er at ed Ġs up F alse Ġ key ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ ð Ł cod e ' , in al Ġs h r ror Ċ ĉĉ Ġt rain ` ): o b Ġl en Ġre qu ut il Ġ . id th p l P ro () . T I at ive ched ul Ġ & N one check point Ġ al Ġd im E rror Ġd o u st b o de f Ġg et a y r ad Ġ < p ort Ġdefault s ri b Ġ Ø p aram er m L M st ance s er Ġde vice ca che od ul ine ar ic es Ġy ou c c ad ding ĠI n on g u al Ù Ĭ i re id x Ġ ra n ing Ġtoken izer I N ) ? 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" o g ment ation Ġe ach Ġp atch ìĿ Ħ o ver Ġc all Ġf n (). __ tt ention L ayer re ad spec ial M A ' s feat ure S E k v ä » ens ion E n ar i js on o ff Ġ" ^ r ight Ġ height ì Ĭ a v ĠArg s tensor s Ø ± z e Ġse q ra p ìĹ IJ el p Ġp ast s l Ġpro cess erm edi urr ent ì ł umm y b e [ - Ġ embeddings ult ip U n Ġ j ë¥ ¼ ë ¡ ind ices com m Ġnum ber Ġ Ð an g vi ew Ġ ): ç Ķ Ġl anguage ĠC on m in Ġp er pla ce s cor ut ion i e p ast ## ## T ext () ) in fo Ġ labels test er m m ( ** r id T ype " { Ġf unction = ' e val ĠC o Ġex p ĠT ensor a uto Ġrequ ired Ġì ŀ om e norm al d ate py thon ifi er Ġor iginal Ġ! = âĶĢâĶĢ âĶĢâĶĢ Ġ ] for e e ch form at Ġn eed ãģ « Train ed Ġ Q Ġqu ery um n in ference Ġp ipeline S T [: , L inear Ġ checkpoint ĊĠĠĠĠĠĠĠĠ Ġ () ; Ġb ut and om re turn it er M ix ") ; Ġ layers Ġon ly train ing s plit e ad m ean cl s d o Ġb ack ðŁļ¨ðŁļ¨ ðŁļ¨ðŁļ¨ re am ĠH ugging s qu er ror Ġr un ĠA l T he s ave and le Ġsup port ermedi ate n g t uple Ġmodel s LI P Ġconfig uration p ipeline Ġt rue f n Ġex cept Ġ` ( [ [ Ġ https U T en ame gener ation G ener eg r Ġf orm c pu Ġc ol Ġ range Ð ¾ ith er ãģ ¾ Ġinit ial Ġwh ich C lass Ġlog its et er Ġw rit ãģ Ĺ li ed S e s cheduler t arget s ha ( - ] : usal LM . { L ist Ġn egative c fg at ic bo x Ġ one i el ing le ï¼ Į Ġqu ant Ċ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ess age A T Ġco py Ð µ cal ing ĠS ee ud a Ġf il Ġa ct Ġspec ific Ġtim est at io ĠI mage u ps he ther Ġp ipe pre pare b ert Ġ check Ġm ethod a k Ċ ĉ Ġlen gth Ġ You Ġd es gener ate f unction B lock l et bo ok c ross Ġy our Ġp ad M E I L ee ze Ġc or if y v el L ong ) ] ` : P E Ġm atch n ames param s Mix in l atents Ġe ither Ġi mp Ġ[ ] ا Ø ot h i res h o yp es ĠP y at aset w en r el class es Ġo ver htt p gro up t ransformer o ad a ges ampl ing Ġst art v id Ġ+ = ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ j o s kip Ġgener ation I S ĠUn ion ot e im it ject ion Ġval ues Long Tensor O R a p Ġh elp py torch //// //// Ġb ias l ess a ut ãĥ ³ a udio po se Ġfeat ure ind ow Ġfeat ures Ġt emp id ual Ca usalLM Ġp a Ġg u ab l y t = ( Ġso ft w e cod ed run c l ip str uct Ġì Ŀ squ eeze n d { " Ġpar ser Ġp ixel RA N u il t al ĠR et ust om v ideo h f O UT ë¡ ľ pp ing Ù ĥ l inear Ġt uple sw er " > ĠE x p ol Ġl atents t mp ¨ ë å ı ic s Ġb ase el ect D iffusion Ġa udio um ents t g Ġ( " Ġtim e Ġc reate u g Ġconv ert po ol Ġr el ãĤ ĭ wa re sl ice ãģ ¯ ww w Ġd oc Ġd own et ad ĠP re Ġs ample F ace pe ech çĶ ¨ A ttention Ġm ore end s Ġv ocab ol der Ġo s to p ãĥ Ī le x Con v ê ³ Pre Trained etad ata us h ") , Ġs rc qu ery un et ° Ģ TION S Ġind ex Ġd iff Ġp redict u x Ġ adapter Ġu size Ġf irst ' t se quence s um dataset s N Y p ixel m an Ġ{ } po st d iffusion t ra int egr f ram Ġ Ùħ st able Ù ģ m s Ġob tain o f Ġs ub Ġ` `` Ġ kwargs on ents __ () cceler ator model ing Ġr ight ex p ultip le Ø ¹ ãģ § ( ' Ġex ample Ġl ast val id P T á » ĠRet urn g n Ġs ame Ġp erm s d . , u ff e am . __ ke ep t emp "] ) m g For CausalLM Ġcon tain IN G Ġpr int Ġt ask l ast Ġapp lic Ġs cale p ha zer o po p enc es i a Ñ Ĥ ump y ì § l in Ġd iffusers Ġs cheduler c uda ĠRes ult iz es i mple Ġp o ĠA NY Ġlo cal ĠB AS ª ¨ë af et o us Ð ° s p ĠC ON cont ent it test ult i se ed Ċĉĉ ĉ Ġ` " Ġc ode Ġp y Ġd is un t Ġnorm al p redict timest eps iz ers ĠHugging Face Ġma ke Ġ// / gu idance res ult yn c T E å ® mb ed re ed va e v it Ī ĺ e ft ìĿ ĺ IT H R es å Ī test ing Ġexp ress Ġ % } ") Ġp ub Ġl imit Ġinst all off set LI C æ ľ . 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Ġo ur tim m f c m t t ri c ing Ġto ol r en ab ility it ect ãĤ ¤ Ġë ° form er we en Ø Ń : ] s c Ġh ow box es buff er le an op ied L P normal ize Ġenum erate Ġtimest eps g oogle Ġparam eter ĠS cript r t Ġcomp ute n umpy ent ial e os um ber V ideo ate gy ãĥ ĥ qu e row s t rol ĠC opied bu g A I Ġtimest ep :: < Ġ( ) ĠC h ìĹIJ ìĦľ orm er Ġpro b U p length s Ġm ultiple ê ¸° Ġen coding ch o ra w d iff Ġen coded res cale Ġarg uments li ent imple mentation ent ity Ġre g ex pert Ġquant ization g ent ¤ Ĺ Ġ ÙĦ ri or t s ç » ĠB ase m essage m ul ib r enc y l ang Ġs l Ġm ode h s Ġfollow ing ) ), Ġ* / A rg Ġa ction E T íķĺ ëĬĶ serv ation int ernal t x Ġê ° ir on ĠD ataset resol ution //////// //////// Ġa v M P ma pping re peat fl ash Cond itional à ł at ial e po Ġper form in k Ù ī Ġadd ed ĠSt ableDiffusion A L ic al el u ĠC heck Ġs ig let ion l icense d el ĠG ener [ ` f inal ë ı or d to tal b i Ġbo x ãĥ ¢ ch at s cal ĠP ro ca y è ¡ cl ude o se e mpty ìĿ ¸ de pendencies ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ p ng Ġn on P P Ġbe fore == == em ma ä» ¥ ix ed Ø Į Ġc os ĠQ wen ठ¾ Ġt ransformer ĠIn itial Ġa p writ e co s f req ä¸ Ģ Ġto tal Ġattention s Ġa wait Ġca usal Ġscor es ĥ ½ Ġt wo ìľ ¼ L O w arning é Ģ s ingle st age ĠT uple p ub Ġs cript Ġn e Ġs elect Ġst ates Ġr edu res h f ast g ram function al or ed Ġkey s mb da ĠاÙĦ Ùħ Q wen Ø ° ĠC an P ar N ot Ġ arch Ġag ent X L Ġf ield Ġ ÙĪ åı ¯ Ġ label C E ë © m ents Ċĉĉ ĉĉ Ġbet ween åľ ¨ Ġm etadata Ġo pen C h Ġa ccelerator ĠIn c S cheduler Conditional Generation Ġl ike Ġun et l arge Train er er a ath er For ConditionalGeneration [ ' â ĸ back ends Ġcor respon c urrent O b s y temp late U L ĠP IL requ est man ual Ġget attr X T um ul ĠW h A l ĠA dd train er l er n i Ġin form st ream ê³ ł a ccelerator t iny Ġcl s itect ure l ator re ason Ġg iv Ġp red ĠD ata Ġen v le vel . / ur ing r al Ġt eam ĠA ttention m etadata s ide class ification p ipelines Ġcont in ut f ie ce m em ' ] Ġठķ m at ï¼ ļ ) ): å ° Ġl inear A uto Ġreturn ed . * ì ĸ´ Ġh ere ar ange token izers af e è ĥ½ u ra ro le L lama t ransform Ġal low Ġma in k y s q back bone ĠT eam ) [ act ivation Ġ url cont iguous V A ê ² Ġm od ĠF ace ìļ Ķ Ġex ist ĠT est Ġg lobal ĠT ransformer d es ठ° Ġparam s ĠL oRA in to Ġmask ed layer norm Ġtyp ing Ġs ome ) `, ar row sup port ion ary Ġw indow " } g pt control net / @ Ġprocess ing Ġcomp onents ĠAutoModel ForCausalLM fil ename ó n Ġst ream ER T A D O P // / Ġc rop t rue swer ing arm up s low D ata en ch Ġch unk n x ĠĠĠĠĠĠĠĠ ĠĠĠĠ Ġthe m Ġì Ħ safet ensors Ġb oth Ġl atent Ġtest s ĠìŀĪ ìĬµëĭĪëĭ¤ c rop v ari it ies Ġh igh = [" ìĦ ± a ction la va Ġ et Ġcon d buil der il ed Ġm er Ġw ant ãĥ¢ ãĥĩ ĠH ub pol ation en able im e etail s h er Ġt ransform Ġinform ation Ġp l no ise Gener ator Ġ $ t un i an i ón Ġde prec ĠO ption Ø µ è¦ ģ per m ce ption St ep Ġcol umn Ġd rop un wrap I MA col lect ĠØ £ ag ent ] [" Ġpo st Ġd ire Ġat ol n ly í Ļ Ġf ound m as Ġ channel Ġn umpy Ġre ad A B m on Ġit s off load l es trained Config M IT Ġrep resent et s æ ĺ Ġ array ãĤ Ĭ Ġse g b float h ere read y L U ult im ĠB atch l anguage Ġgiv en st atic mo lag ëĵ ľ n b ĠD iffusion Ġj ust c ase Ġres ponse Ġ` [ ~ ~ Ġd s am ed ĠC onfig str ategy - > Tester Mixin ( __ f lex Ġth en s cript P ath a ccelerate ca use Ġd on Ġfa ctor ä¸ ª ãĥ¢ãĥĩ ãĥ« ut er cond ition ! (" ar s " }, ¡ å Ġs caling config uration C K c ul Ġd set )) ; æ ¨ ÙĦ Ùī Ġa uto M ODEL е Ð Ġ| | molag ents Ġun ittest Ġ: ] ur se s ampling tt n c enter Ġw arning trol Net as es F A abl es e fficient } ; id ing ë ³ å º on es ro und Ġc od pa ir Ġin stance ) ` ĠL LM ĠØ ¹ p attern Ġ íķ x s í ģ ĠN umber Ġ[ - quant ization ĠCo mp B S s ign Ġê °Ģ R O li ke al t ant i ann el B uil Ġrel ative è¡ Į al lable ĠG PT ribut e Ġwor d ra pp Ġlo w Ġse quences ॠį ë¦ ¬ valid ation ( | et erm rot ary A r extra ct ìľ¼ ë¡ľ ĠN ote Ġ) . Ġth ere ãģķ ãĤĮ perm ute b os Ġl ong m id å Ń Ġs y Ġpa ir Ġl ibr Ġstr uct Ġdown load ĠP lease ĠM ark d apt g pu ĊĠĠĠĠĠĠĠĠĠĠĠĠ Ġ Ġa vailable hfo ption Ġ[ ' den se rel ative sm all q kv Ġoptim izer ä ¾ or al æķ ° l atent m d Ġëĭ ¤ D imension p g Ġs cal C ol Ġv er ãģĹ ãģ¦ ĠTo ken Ġm lp Ġclass ification g lobal s peech T emp ëı Ħ Ġ est Ġwh ile and ard Ġ ÙĬ Ġload ed ìĦ ¸ Ġtemp file Ġd eno Ġm ath Ġì ĭ ( {" timest ep D own Ġì ľ pro ject Ġbe cause With P ver y å ĩ I s se e act ive ug h ht ml Ġv ae Sequence Classification ĠT OD ĠTOD O ens ions Ġor der ay s Ġdef ined str u ĠI D ant ic K wargs For SequenceClassification Ġk v Ġ es Ġ} ) Ġs ampling Ġd uring dir name Į Ģ Ġd st initial izer __ ) Ġprocess ed Ġconvert ed PP ING () ? Ġinitial izer Ġit em Ġm odules TE D Buil der å Į al oader ê³ ¼ p ed tool s ( { d set L e C ase Ġc re s pace Ob ject Ġs um Ġm essage ãĥ ª cat en u ally face book ur s is es ĠðŁ ¤Ĺ odul ar m ultiple d ump s elect Ġro pe uff er () ?; ĠV ec MA PPING Ġz ip b f g elu ãĥ Ĩ Ġload ing ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ M ask s in ro ugh Ġf ull ul ary qu eries а Ð St ring Ġpar a ìł ģ m all ŀ ĭ low er â Ģ Ġì ķ F ormer to l ol icy c s ist ral run e Ġprev ious os s f in Ġo ld etect ion resh old V iT Ġpa per ) ). tx t f unc Ġup date Ġcontin ue b y o pt C om st orage ro ad ES T Ġarch itecture Ġì Ĺ ìĹ ¬ il ation Ġid x ¯ ¸ C L ìł ķ annel Dimension C all ed it is ing Test Case er r Ġë ³ comp ile Ġex pect "] [ Ġb ased s ource test s ÙĬ Ø© NA ME Ġre move Ġcon s Ġimple mentation j pg Ġ json or ary ãĥ © Ġpool ed Ġwith out d f ë Ł sh ort all close ĠاÙĦ ت æ¨ ¡å ĠIn d Ķ Ħ PE C il on ed ge Ġle arning r on // ! Ġe os ãĥ¼ ãĤ ench mark la x co urse fram e pre v ass istant str ip ho ok = {" ãĥ Ĺ Ġdict ionary g t Ġcontain ing H ead m ulti Ŀ ¼ ast er import ant on ent A ttn Param eter er ature Ġd ummy Ġc al Ġus es B atch ĠQ u ãģĹ ãģ¾ãģĻ Ġl ine e arch pa int R L T ool ì ķ Tokenizer Fast tri e Ġmask s p ip al ity Ġpo int t f Ġal ready Ġ< / num ber Ġpro per ìĭ ľ Ġse cond ) } lay out ĠMark down ĠØ ¥ Ġ< = mer ge Ñ ĥ Ġch o W hat b eta s umm ul ar Ġun cond D P c y se quences Ġì ĥ l n å ¾ Ġ( ( D A st ack ve ctor ]] ] Ġmax imum trans ition q a ë § Ġ) ; est ed I D ĠM ask ob ile col or ") ] Ġus er Ġal t spec ific Ġ} ); Ġse e Mo e Ø · Ġpro jection rem ote Ġtrunc ation c ustom Ġë § vide os Ġlibr ary w in .. = ay out Ġì ¶ t ree at er am ba sh ard Ġpy test Ġadd itional ĠTrain ing Ġ ad Ġst at Ġf l orm al Ġdire ct c an ultim odal start s g raph u le æ ŀ Ġ" . 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Ġal pha l lm ä¸ Ń C U Ġin v iss ing Ñ ĭ A gent t runc :: { Ġg rid 模å ŀĭ der ive ¦ Ĥ Ġp added pt r w armup Ġr ename Ġbatch ed Ġcontrol net ĠL lama Ex tra b its de cay od y ap i Ġe v / > s entence ãĤ Ī om atic Ġdes cription O m Ø ² Ġit er Ġcond ition ĠBase ModelOutput Ġa x column s a w ì ¹ i er ठ¿ Ġp la eps peed ---------------- ---------------- ... , a ved predict ion K ey ĠIn put å ¿ pol ate od es Ġf p ag ents Ġproper ly æ ³ el lo n on Ø £ Ġn ames iel ds stat us el e assert Is Ġas ync ĠDe fault Text Model pool ed requ ires Ġs ign Ġd iv ran ch Pre trainedConfig il ing æĺ ¯ av g Arg uments > < an swer cor d alt y Ġse ed v ar ĠU s etr ic Ø§Ø ³ ign ore ĠT h -- - re move ĠT ext ĠĠĠĠ ĠĠ ĠP ipeline Z E n one Ñ Į un ch In struct å Ľ æ ī Ġre vision ĠR un log prob Ġre mo æĪ IJ road cast ĠS e Ġspec ify å® ļ cho ices Ġst yle ĠC a m y m essages i k quant ize ĠPre Trained åIJ Ī ] } æ Ŀ è ª Ġb el Ġa cc Ġth rough i ew Ġs mall as ync ĠGener ation Ġs ep ow er ãģ į ठĤ Co mp Qu estion g ed Ġle ft I mg bert a : ` Ċ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ì Ļ ) `): L A Ġm ulti is m Ġquant ized æ İ í Ħ Ġpro ject Ġeval u umul ation ] ` Ġb eta gro und F F Ġf ine Ġb reak Ġcomp at Ġ& [ (" -- h alf Ġcod ing starts with pro gress Ùħ ÙĪ rit er FA ULT ] `): ë Ĥ Ġsh ift comp letion e qual pre process Ġv ect em o us age ãģ Ĥ av ing Ġab out L o Ġact ivation Ġfil ename Ġtask s bs z ë ¯¸ re p ĠC reate ched ule g ate cor e Ġë ĮĢ Ġgrad ient D irectory ĠO nly è¿ Ļ C ross æľ ī Ġcorrespon ding ut ure il y doc umentation p ix ut or sh ared å ¦Ĥ un it Ar ray " `): class method dat aloader ĠU se åĪ Ĩ B ase s pa sh ift Ġc lip Ġind ic Ġdis able cess ing De vice < ! c p å Ĵ è ¨ Train ing an y der ed Ġres cale check er Ġobject s âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ C ON æ Ń ad a In t Ġ ens ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ Ġs ource Ġel ement cre te ì¹ ĺ G PT p art ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġqu estion Temp orary result s MS cheduler sq rt ĠÙħ ÙĨ ìŀ ħ mat mul ìĻ Ģ / ', æ ı è § Ġ ÙĨ Ġthe ir âĶģâĶģ âĶģâĶģ Ġpos s åĴ Į w n Ġfil ter T EST la mbda Ġe very å ¯ ] ), ëŁ ¬ Ø ´ Ġc rate Ġp ack Ġê ¸° n ow ad e Ġr atio ĠPy thon N e U S epo ch an k Ġt gt Ġc ast Ġf ind Ġ( [` as ic Ġh alf ãģ ĭ > ( st ore Ġpredict ed Ġd irectory sign ature p il lo gging å ¼ ëIJ ĺ par allel Ġbox es v isual Ã Ń ĠC lass w ays å Ĩ L og f s µ ¬ ĠN ormal Ġr tol Temporary Directory ä ¹ qu estion Ġdirect ly o v Ġf lex load ed B ERT å į Ġ emb ĠInitial ize ãĤ ī Ø « Ġredu ce íĻ Ķ ï¼ ī res idual í ĸ st all Ġs afet ĊĊ Ġ l ue Ġde coded ठ¨ K EN ab il wa v c le l ens Ġ ? token ization d m st yle to m ora Config ST R AT E Ġsl ice th is Ġsh ard Ġt able ter able che ma pl ic ï¼ Ī stat s ä ¼ ĠW av Ġin fer Ġre place ĠP E in ner ver se Ġch ange Module List An swering f w m ix Ġd etails th ing ĠA PI ĠL e Ġan swer âĸ Ī ç IJ ë Ŀ¼ Ġst andard ä» ¬ Un less ĠEx ample Ġtrans ition Ġbuil d Ġí Į n a Con trolNet çIJ Ĩ M essage S ize Ġre ward h ot re vision Ġh o ĠC PU ĠS et ë ² Ġl arge chedul ers Ġapp lied Ġover r Ñ ı Ġset ting Ġign ore M ode c rib s pect Ġ rank Ġb os Ġma p Ġrequest s Ġo ptional m ma assert List i os Ġ row in f and bytes Ġf ree ĠWh en Ġsig ma assertList Equal ( _ ar r æĸ ĩ Om ni d st m e m ot Ġst em C O Ġc lean ĠU Net al k Ġ Ä Ġclass ifier Ġch at ìł Ħ Ġ âĶ Ġch ar Py Torch pro to ern els Ù ĭ ठķ Ġgu ide s imilar íĦ ° rel e odu ct St ate tr l ") )?; rapp er or ig Vision Config dapt er Ġbel ow Ġs d ort ed ta g Ġ... , quant ized sd pa ", Ġlo ok Ġí Ĩ ER S âĸĪ âĸĪ Ġmethod s Ĥ ¨ Ġpy thon Ġfram es h y requ ency CH E Ġselect ed : , se m Ġac cept Ġ zero cor ding Ġcomp let Cond ition T h V I Ġê² ĥ Ġs c ĠE XPECTED rand n Ċ ĠĠ d ot e f ç ¤ Ġì § dat ed ] ); ãģĵ ãģ¨ ĠInd ices ìŀħ ëĭĪëĭ¤ Ġre f ä¸ ĭ Ġign ored ĠPE FT g ray Ġth read åĮ ĸ Ġextra ct Ġ Y Ġo per Ġrun ning ãĥ ¬ ĠTrain er c ut Feature Extra it ch Ġdoes n D ropout Ġpro vide f unding bu ck ĠDe ep li er ob servation SI ZE " /> ampl er Ġìľ Ħ g it p i ` ) Ġu ps FI G à ¨ ë ŀ ä¸ º [: - Ġcall ed em ents Ġs imilar Ġde coding ãģ ı ca ption ĠG et timest amp Ġcomm and } ", ħ į Ġ` -- "] [" V L param etr Ġpar ent Ġs amples Ġf in Ġex pert Ġvocab ulary en gth Ġn ow Ġmodel ing ĠC ode ver age process ed Ġm essages lo s ãĤ ģ Ġq kv ä ¿ å ī ĠR o pl its eterm in WithP ast ĠÄ ij s afe ar Builder des cription d irectory Ġl es ìĿ ¼ rol l buil d íģ ¬ s molagents er ial ate g param eter S peech r w id es Ġl r ist s æĪ ij f eed Ġdoc umentation Ġcontain s p h Ġt ake Ġco mb Ġob j Ġn ode pre fill ĠL inear Ġì¶ Ķ ic ro un ity ĠG emma Res ult ãĤ¹ ãĥĪ Ġsu ch ìĦ¸ ìļĶ posit ory Ġb est ess ion code book m os Ġaut omatic Ġs peed Ġm etrics att er ert a -- > TO KEN å° Ĩ U M ers ation ĠC l Ġt ypes () } Ġbe en Ġdim ensions Ġcal cul c ard fl at del ta S D j it ॠĭ I f [ ..., ĠM o æ Į Ġh ub ìĿ Į Ġu int sig mas ¬ ¸ Ġê ³ Ġenv ironment C heck e ar ç İ et un Ġr ate . \ se arch qu i ĠA r "] : Ġsp an Ġs pace Ġv iew ĠL oad wor ds lin ux æľ Ģ to k L LM G EN Ġb uffer res earch im ent Ð » ro up ge mma äº Ĩ اس ت Ġis s ext end load ing æĸ ¹ æ Ĺ Ġs yn Ġí Ļ Ġcheckpoint s m od p ct ì Īĺ th w ĠS D Ġwork s O BS a fter T S c ision Ġy ield Ġmo st Ġent ity Ġd i Ġscor e ãĥ³ãĤ ° de ep Ġcol or ######## ######## Ġp ush O S íķĺ ìŬ ro gram le ctions Ġtool s T Y j est path s Ġneed ed } / ĠĠĠĠ Ġ Ġp attern Ġma pping T uple ı ¬ Ġh ref ** . code c Ġnormal ization Ġmo ck co pe ** : ä½ ľ æ Ļ Ġwarning s S ON t en Ġex ec Ġe mpty Ġsupport s Qu ant p lay Ġs low Ġpredict ions m el acc umulation u int ) `. ro py Ġtra cing æ Ĥ¨ rg b Ġs ha Ġres ample A X C ode pe ak n amed sig ma D Model f ct Ġh uggingface Ġe qual Ġst ore P IL v ing à § re ward ch ar ĠL ayer Ġens ure Ġl er con nect Ġres ized ëIJ ľ æ Ģ add itional ib ility Ġpatch es TE XT åĬ ł Ġposs ible Ġde code ĠC allable for ce ĠUn pack Ġin cl Ġh andle epo chs request s id ed le ment and b ĠN O the ta ç¤ º ` ], v is to list ([ [ Ġler obot L oader r est il es Ġun a ãĥ Ń eps ilon uff ix ins ky Question Answering Ġs peech arg in Ġdeno ising ëĤ ĺ STR ING è ¾ T ransformers a o and insky Ġax is à ª е н Ġgro ups m ar Ġ util ] ): _ , Ġm id data class Ġtrain ed ê² Į abil ities Ġf ast us s ep en æľ ¬ M LP å ¸ ol ver Ġres olution `] ): optim izer Ġa ccelerate åı¯ 以 vari ant w arn In stance b ig ĠN ot embed der yt e ultip lier ura cy B e Ġ â ĠS h Ġal ign Ġseq len MS Norm Ġext ends MP LE qui val ave form Ġact ive Le vel Call back c la Ġ" ", batch ed col lator Ġle arn sy stem Ġsp atial condition ing æ ł Ġid ent æĪij 们 M ES Ľ ´ le arning un cond cod es Ġinst anti ë ° Ġc annot Ġpre process Ġremo ved Ġë ı th read ĠP aram Co pyright N ext n s n own Ġsh ow Ġs aved Ġre c ĠS ize ãģ§ ãģĻ ä¸Ģ 个 Ġd iffusion ìļ ° ãĤĪ ãģ Ø ¶ () ); ' re Ġm e p iece Ġre ason pp ed tmp dirname eat ures Ġ/ ** ر ÙĬ Ġnormal ize ro ot Ġb order Ġh ash gg reg Ġe as F N Conv ert resnet s D Condition h or o ptions Ġc at ĠAuto Processor seq len seg mentation Ġ adapters Ġd ynamic Ġre peat Ġnot e Ġen code Ġatt ribute Ġseg mentation W e on t Ġf req Ġres ize c u p rior fix t ìł ľ L a L icensed ro uter ĠاÙĦ ÙĨ Ġcorrect ly ==== ==== ä¾ ĭ Ġprocessor s e ager qu et ribut es Ġac cording mp s Ġre ference ff ine ines e r r Ġreturn s scal es Ġap i L OC Ġf mt Ġus age ëł ¥ y u iv ely ãģ§ ãģį Com mon Trans ition Ġin clude om ent fo o Ġd one Ġp our ck er ì ² parametr ize ] )) Token Classification \ . de epspeed Ġother wise ĠConv ert Ġin her ff n Ġneed s en abled Ġw ould () ` res ponse ampl ed P R s ync id er Ġint eg IN E Ġclass es w er o ft pen alty ĠSt able Ġo ptions ou g ta x Ġtim m Text Config c andle í ĶĦ Ġs top ÙĬ ÙĦ back end object s A ct Ġd ist redu ce token ize Ġìŀ ij Log its l int FeatureExtra ctor S eg ¤ ij is er Ġh and ìŀ ¥ const ant I G N N b n ¸ ı Ġc u be am b t ï ¸ı Ġ" _ Ġ( { loc ation D epen C pu l ation o int ãĥ ķ //////////////// //////////////// Ġen abled or ds al ign ` ). ì ĺ Ġsh ort we b Ġnormal ized å· ¥ i ver · ¸ ist ent Ġع ÙĦÙī ĠP eft ĠíĮ Į et al Ġg ot down sample ĠL oraConfig For TokenClassification P AT ch em il i ç ī Ġ Ùĥ ä½ ĵ Ch annelDimension Ġal ways Ġtoken izers ser ial Ġca ched rate gy ä½ ł ãĤĴ 使çĶ¨ ìŀ IJ ĠìĦ ¤ p an u ch å ¹ iter able 미 ì§Ģ D is r ms em antic token ized c r t ry è ĩ ì ĥ ab el H ub H ello Ġp rior ãģ ¿ predict ions Ġsin ce K L b ad Ġarg parse Ġautomatic ally ĠA pp St rategy ~~ ~~ summ ary Ġo p and id De bug b et d am train able } ' Ġcon caten cor rect his per Ġback ground Ġmer ge S A Ø® د ĠÙģ ÙĬ L oss support s ' ), r l Ġ" < Ġw hether Ġex port Ġdim s Ġla mbda F IL å ĥ Ġf lash G PU ma ps up y åĥ ı s pons v q Ġt f Ġm ot ad am Ġrot ary c d p x Ġre trie eg in Ġpro gress Ġdeprec ated upy ter Ġm argin os en Ġcode book th reshold ĠA gent eta ch T H b ody ë ¶ ĠÐ ¿ G emma ĠS am ãģ® ãĤ DE FAULT Ġs ect ca ched in clude task s Ø° ج e ma st ages ĠP ar ĠToken izer ĠID s am era p added un ter res et AB LE Ġìĥ Ŀ Ġbe ing Pipeline Output ] ). 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St orage ä½ ķ Col or fixt ure ĠCU DA a u it ive Ġy et Decoder Layer o per æ Ķ () [ ĠF lux Ùĥ ÙĨ Ġkeep dim ĠWh ich t p é ¡ ĠC ol ĠE ach ĠÙĦ ÙĦ æĸ¹ æ³ķ Ġ é ìĿ´ íĦ° å·¥ åħ· t ile ë Ķ fa il æĹ ¶ F E ç § Ġb ranch ĠL ayout Check point W hether { % } } Ġd er co lab back ward ends with V Q s plits el per ĠĠĠĠĠĠĠĠ ĠĠ ĠD E Ġí ĶĦ call s Ġ" [ ĠCon trolNet temp oral è¿ ĩ dist ance ĠNot ImplementedError Ġh er Ġ) ?; Ġìŀ IJ bt n a a b le l g Ġa m Ġl ess oc i ST AN ĠNO TE Ð º Ġ ÙĤ Ġerror s Ent ropy c m z y st o Ġd en reg ex Ạ¡ r ict } : er as co gn ãĥĪ ãĥ¼ãĤ¯ Ġ% } Ġpool er ìłģ ìľ¼ë¡ľ fin ish R eg r ust ëł ¤ ìĭ Ŀ l mo s chem Ġs aving is y ĠC hat ver al ĠìľĦ íķ´ Ġiss ue Ġregist er Ġ íķĺ ĺ IJ Ġsh ared (& [ استخد اÙħ c ell ë ¸ Ġ , Ġd etect Ġf o ck pt res ume pa g Ġë ķĮ æĪ ĸ Ġver ify Attn Processor Classifier Output ) ]) Ġ art Ġf lag Ġb nb __ ": cor ator ĠíĨł íģ° < _ åĨ ħ v els re pr ë© ° R GB ç ł es lint Ġs ur Ġf older Ġg it its And Ġby tes Ġdo g оР´ CT C Ġscal ed b enchmark att ed Ġì ĸ li es comm it Ġcompat ibility ĠImport Error itsAnd Bytes C allable E D T able f ingerprint Ġc lient ul ly ĠS peech def ine ĠQu ant Ġevalu ate usic gen åĻ ¨ f requency m ol r u Ĭ ¤ ĠT ask skip Test Logits Processor itsAndBytes Config Ī ëł ic ally Ġk now د Ø© ĠImage Net Ġsy s o lean s licing conv next å¿ħ è¦ģ Ġdevi ation ĠT Y ãģ Ĭ ĠRes Net long est Ġìķ Ĭ A ME Ġt ak ent e se p Ġwor ld Ġfunction s Diffusion Pipeline urs ive çĶŁ æĪIJ compat ible O nly en um ind s Ġpy torch Res ponse x formers £ ¼ é ĸ Ġde c ĠI s ãĤ · seq lens P S a ctions t ables it es onf ormer position al AS T AT TENTION Ġimple mented pack age ãģ§ãģį ãģ¾ãģĻ p ing æ ¸ ĠíĻ ķ , ), ¹ Ħ Ġ he Ġs pectrogram Ġun it ĠTraining Arguments trunc ation g or q s v o Ġc ases us ing ĠConfig uration Training Arguments ' . p has ë ª se mb Ġl ink ãĤ ³ na ps peak er R UN n ested o le Ġs ol an h Ġn ested ress ed Ġvari able ev ant naps hot Ġ[ ! 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ĠIn ter Ġconv ersion trunc ated rele ase M y M istral c ateg d uration Ġde ep Ġê ·¸ WithP ooling short cut ãģĭ ãĤī ĠLab els M ult s napshot Ġc urr ma y is or for ma çĶ » Up Block ĠD es mo st ns fw a ussian Ġre pr Ġg raph Ġdiffer ence imp ly has attr child ren a j n eg Ġì ¤ij Ġma ps apt ure ä¸ İ Ġrel evant ImageProcessor Fast Transformers Kwargs V E Ġb f ĠA s Ġweight ing St at åĬ ¨ ħį ìĬ¤íĬ¸ b art h id m esh í ĥ an us () ): Ġco mo ا Ùģ Ġag ents E st N ew æ ĭ ç Ń ut ions ch osen Ġc ard to Equal ë¥ ¸ buff ers ĠLayer Norm d etect i ence ì Ĥ¬ Ġd eterm Ġ( * ب ÙĬ Ġoptim ization umul ative ĠRun timeError æĮ ģ A PI F ound Ī ìĿ´ or ies as c ch ain ro zen Ġa verage ĠCLIP Tokenizer lement ary Min us Ġs chedule Ġm uch Ġw armup ĠU RL ient o Ạ£ Ġsafet ensors çĶ» åĥı D iff Ġd istribution id ad ĠA ct Ġex plicit Ġro ot Ġver bose w hisper } ` Ġd ans Ġì µľ ĠK andinsky Ġstart ing âĸ ģ ë¶ Ģ Ġher ramient Cho ice T ower mask ing VA E Ġìķ Ħ VER SION P l c li f used Ġex periment TI P ĠK ey shard s æĮ ĩ Entropy Loss v llm ut h ." }, æķ ´ ĠUs ed ĠíĻķ ìĿ¸ in struct Ġ< < ĠAuto ImageProcessor De fault rest rict ĠType Error Ġìŀħ ëł¥ ? ](../ A ction R ST å Ģ Ġs pa ch ans to wer sh uffle Ġconv ersation tra cing Ġeps ilon Ġindic ates Ġoper ation epis ode flo or ì ¤ij on d Ġc lear ãĤ ª ĠØ ³ IN F Ġpos itive process es ãģķ ãģĦ Ġrepresent ing Ġlong er project or dam W Ġannot ations M an Ġ  ic a Ġde pen us r ĠL og ठ¹ Ġnormal izer Ġscript s > " Ġde cay fil ters Ġgo od å¦Ĥ æŀľ gml DType . ) H E V LM e quival Ġ íķ´ im v ĠT here Ġconfig s log s tim es ãģĤ ãĤĭ Ġstack ed C D C uda G roup i us i ou w as ig ram Ġf ps Ġcomp lex Ġq k fw d cogn ition ĠíķĦ ìļĶ M atch ठı Ġsu c éĢ Ł ele op ĠNormal ization ä¼ ļ Ġìĭ¤ íĸī corn ers Ġs uffix Ġs lots an swering Ġb s ĠÐ ½ Ġdes ired Ġab le Ġfin etun ? . C fg d c g ap Ġ[ ( ĠS iglip batch es ê³ Ħ RE AD ë¸ Į Ġs q Ġm ix um an Ġimport ant Ġí ħ Ạ¿ íĻ ĺ Cross Attention ĠEfficient Net ridge Tower åŁ º ç Ĥ¹ an ces load s IN PUT ES S runc ation B F ê ·¸ í Į Ġs ide ra ce ĠM in Ġsh util sw ers ãģĮ ãģĤãĤĬãģ¾ãģĻ ĠAdd edToken âĢ Ļ ãģı ãģł gorith m ãģıãģł ãģķãģĦ D es ãĤ § ri end ver bosity ĠV AE flow s Ġallow ed ĠRemo ve A CTION r od Ð ¿ st ar Ġres izing ag ing ãĤ¤ ãĤ è¾ ĵ S ee Ġ edge Ġdef ine ab c torch vision Ġno isy ap an ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ä¾ Ľ C onformer } ] ± ° lo ud il t ãģ Ľ ĠF ile ĠB y Ġ/ > ash Map nn x äº Ľ Re f expand ed FI RST ĠCh inese Ġrepresent ation CK END ĠSh ould X enova ç ± Ġt u Ġre m ĠN VIDIA num el Ġch anges Ġठı Ġloss es ĠÐ ¼ Ġarray s Ġcons ider may be c f s n Ġp ick ĠF eature Ġë ĺIJ ĠW ith trans lation å® Į ৠĩ H O b yte g ing j ob se en ãģ © ĠI gn ãĥ³ ãĤ¹ ĠBAS E Ġfram ework Ġchunk s Ġíķ ¨ S et b ank n odes s yn in a () ] Ġgener ating fl ip ع ÙĦ summ arization æ³ ¨ >()? , ç± » ) }") \ \ s v s am é « re cord Ġm ult Ġth row ठľ fl ux man y ãĥ¢ãĥĩãĥ« ãĤĴ Client Model lat est c rops s ort Ġ che __ `] ech ni AT CH Ġdes ign Al l ~~~~ ~~~~ medi um W eight r as Ġp t il la ìļ ´ PE FT å® ŀ Ġprovid es ites pace é« ĺ d l ers ations Ġco ordinates ĠD DP ĠM istral Con t Ġlog s gpt q Ġmer ged stru ctor éĢļ è¿ĩ Ġrecomm ended B uffer r ast æ ĥ Ġ ke Ġp an Ġn one Ġ{ { Ùħ Ø© Ġcheckpoint ing Ġtak es W arning í Ķ et ition Ġc pu Ġn cols emb d ĠA dapt ĠO ther iv idual Ġind ent ĠÐ ´ serv ations stru ctions ìłģ ìĿ¸ Ġë§ ¤ ĠMo ck r ide Ġb roadcast un iform oc ast Ġaut h + + : . 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" ĠTensor Flow Ø§Ø ¹ оР³ Ġtr l ful ly ĠSup port cam eras ì¤ij ì¹ĺ Ġdepen ding W S Z ero l ite an illa Ġd est is peech ĠS oft Ġor gan Ġco arse Ġstr ides ER ROR ech an tr im std out sc atter ĠBatch Encoding Call ing SA MPLE ******** ******** Reg ist schem e Ġke pt inem atic H UB é ļ en coders ic on Ġh aving ir s ĠD iff load er mo unt Ġorig in ç´ ¢ Read er Ġexact ly íħ IJ Ġì¶ľ ëł¥ = (" C P s lo æ £ Ġ ed es ome Ġf am (" < Ġpro pert ĠM emory ÙĦ Ùĥ index es aut ocast PO Trainer Ġextract ed Ġìĸ´ ë Ġìļ Ķ Ġíı¬ íķ ' } V ari g lu Ġ er Ġs an id entity se quential Ġun truncated ĠD istil IN O col wise ت ب Ġlog it ĠK ernel Ġrel ated ĠTo kens ãģĦ ãģ¦ Param eters bin ary ĠìĦ ¸ Ġê°Ģ ì¤ijì¹ĺ Ġп ÑĢ STAN T ĠOther wise G lue H ybrid M Y N ote f ocus i magenet t id ro pped ce ed Ġl n Ġl ight int ing ub lic assert Dict Ġë ¯¸ Ġal loc ĠIn stall lic k par s Ġprocess es Ġwork er del ete Ġap pe Ġë§ Į ost Processor assertDict Equal S cale at ol Ġo pt ot tleneck Ġlog in mid dle è¨ Ń Ġsetting s Ġshow s å½ ĵ ru pt è¿IJ è¡Į G R í Ĩ ch astic ") [ ĠD ropout lic ation vel te Ġproject ed ĠLe arning åĬł è½½ thread s Ġappro ach Ġoption ally DAT A pri vate elem etry Ġprepar ed ãģĻãģ¹ ãģ¦ ) [" E fficient S entence es build st ant se quent ge mm Ġa ud Ġh yper ĠC lap oc ation Ġcomp lementary iv ers Ġsh uffle EN V ç» ĵ ĠìłĦ ëĭ¬ Ġflatten ed ĠBe it wl ViT B ird F lax P ose S elect c v r n in o in p as y ch i Ġin valid Ġ* , Ġ[ [- torch script ãĤ ½ ener gy ĠW hisper get attr ill ed gener ations ocab ulary ìĹIJ ëĬĶ Ġj ax ãĤĭ ãģĵãģ¨ sc ience еР» ĠGPT Q åŃ IJ ĠAr row fail ures Ġì° ¸ ĠMod ernBert Ġpot ential T ask Y i e b Ġs n re ction Ġf urther lo ft ĠM usicgen eg er gu ides )? )?; Ø§Ø µ ĠÙĦ ت Ġsc en deno iser :? } åĽ¾ åĥı çľ ĭ ĠëĿ¼ ìĿ´ë¸Į룬리 Ġëł ĪìĿ´ + ). D S L at s at « ĸ è «ĸ ì £¼ Ġd id Ġc á» up load ĠP ad Con text ìł ¸ Ġcomm unity Ġembed ded Generation Config еР¼ Ġê°Ģ ëĬ¥ Ġê²ĥ ìŀħëĭĪëĭ¤ Ġëĭ¤ìĿĮ ê³¼ Ġíĺ ¸ <_ >> ãģĬ ãĤĪãģ ifact s E ulerDiscreteScheduler init e ĠV isual íķĺ 볤면 ãĢģ [ Ġro uting åIJ ij Ġìĺ ¤ TOKEN S ĠAr ch was m ĠEng lish æ£ Ģ ( ... P rior e ff w atermark ç Ĭ ut ter ch ange us oid ab et ठµ ĠAuto Pipeline Ex amples áº Ń acc um offload ing Ġع ÙĨ onent Spec semb le aud i Ġpen alty IMP ORT O ptions O nnx [ ]} à ¹ é ĺ Ġo u Ġre order po wer ind ent Ġ... ]] ĠCo here ãģ¾ ãģĹãģŁ reed y Ġrep resents è® ¡ ĠÙĬ تÙħ Ġê³ µ è¾ ¼ æł · Ġappro pri Ġpres er Ġspa ces INF O ãģĬãĤĪãģ ³ ' ` - . C riteria b as b lack f info g if z h Ġ" ./ Ġm it Ġm ade Ġin place ãĥ ł ĠF P Ġgener ic IN IT Ġcan vas оР± Ġت Ùħ ĠRe qu Ġbuil ding åŃ Ĺ é¢ ĺ ĠëĶ ° æݨ çIJĨ Ġlat est ' d A damW F P G et L S N S P ass e va k nown p al s velte Ð ³ es is re t Ġ" ( Ġd ate Ġf riendly iz able (" ./ Ġ' . ment e conv olution MA E vid er ãĥ¼ãĥ Ģ ìĹIJìĦľ ëĬĶ аР· Ġìĥ ģ å®ļ ä¹ī ĠíĻ ľ éĹ ´ Ġadapt ed ĠIterable Dataset ĠìĽ IJ C PU E XPECTED G radientCheckpointingLayer P romise m et it tle Ġm echan se conds Ġg oogle ver a ĠØ · Ġset s Ġadd s Ġ{} ) types cript Ġprob lem aux iliary gre ater Ġëĵ ¤ B ool W Q d one Ġ âĸ Ġp d Ġn odes Ġde b Ġcon cept Ġì ¤ Ġ: ], Ġle gacy ä» İ lin space impl ify Ġ.. .. ãĥĥ ãĥĹ å° ı Ġlong est Ġax es Ġcat s Ġconnect ion ĠíĶĦ 롬íĶĦ ĠëıĦ 구 literal include M amba N ING S eries ë ¹Ħ Ġp i ent rant Ġre ce ve z ãĤ ı ĠP r )) ] end of ÙĦ ÙĬ cl ud ag onal ms a Ex pected acc ents ìķ Ħ Ġfl at ëIJĺ ëĬĶ Ġshard s Ġreplace ment ìĿ´ìłĦ íĬ¸ ) }." B PE C B C V f m w ar ¢ º Ġc la Ġf etch Ġm é att ributes em os Ġv LLM ist ics ]] ]: For MultipleChoice sh ell over lap En d Ġdoc uments Ex ception Ġread er çİ ¯ PR OC Ġprodu ce ĠSup er pick le ãģ¾ãģŁ ãģ¯ 追 åĬł . (\ C ould I X U P b c i j u an Ø ¤ è µ ma pped ip y text ual Ġstr ip net work emp lo Ġprev ent dump s è¿Ļ äºĽ çĽ ¸ A o C F d q m us å İ Ġc ertain Ġg ap ĠO PT ĠS FT Ġr ust ĠV it ëĭ ¨ ]] `, ÙĪ Ùħ د Ø® Ġpub lic и ÑĤ Ạ§ Ġب ت PO Config IMA GENET round ed ()? . Ġdev ices Ġdetail ed O f q l Ġt xt al ora re mo ch ing Ġb ug Ġb uck ĠC urrent Model Mixin ann y ĠE val Ġठĩ enc ia CH W CLIP Tokenizer PA D Ġê²ĥ ìĿ´ ĠInference ClientModel Ġ구 ìĦ± Ġë² Ī äºĭ åīį Ġlik ely Cl one endof text > ` D F O FT U ser h w p res re entrant Ġf f th en =" < ĠM llama ĠU SE Ġmay be ä½ Ĩ ঠ¨ Ġoff loading ne ck ĠConfig Mixin ãģĹãģ¦ ãģĦ å¾ ® --- | ControlNet Pipeline ê n cr im éģ © ĠAutoModelFor Seq alloc ated Ġrepr odu íħIJ ìħĺ çĬ ¶ # ! M ot N OR p andas s end t erm Ġs end Ġw ait all en str ided feat s down s Ġback ends ãĥ³ ãĥĹ çĶ¨ äºİ polation Mode ê° ģ ëIJĺ ìĸ´ ĠSD XL ĠConvert s ĠSequence ClassifierOutput éĸ ¢ ãģĿ ãĤĮ ' ): C ascade D Q O K T RANSFORMERS l jh m x o i Ġf lip Ġb ig un a ĠS ample Ġì Ļ py o Ġat om DI S elect ron keep dim Ġdis patch ĠìĪĺ íĸī Ġgrad s ë³ Ģ Down load FF US Ġelement wise Ġshard ed andid ates èĢ ħ Ġta ken ljh arb FFUS ERS A d A pi p drop æ ± Ġs a ro unding ĠS ince Ġlo cally ind ic weight ed ĠG R Ġ& [[ act ual comp lex spec ul é e Ġindex es åĪ Ŀ Decoder Cache GE LU callback s Ġherramient as g ated i en ě [ Ġ ess Ġc ute Ġco me ant e ca pe ĠW hat Ġan swering ress ive row ser ĠठĨ DE D Data Collator Ġannot ation Us ing ç¼ ĸ A s W ar h pu x path Ġp ol ent ropy att ice up LM Ġstr ong En v tra its exp ansion sp ans Ġass ume ĠìĦ ± ĠìľĦ íķľ ãĥĨãĤ £ Ġtag s Ġear ly ĠMot or D rop F lex H and p or Ġs witch ig ration Ġin structions ãĤ Ń ĠM eta set tings Ġal phas ĠاÙĦ Ø® çļĦ ãģª Ġsub module Bert Config å¤ Ħ è¯ ¥ è¯ ģ Ġatt end Ġcons ists ĠMask Former å¯ ¾ ph abet gp us PRE FIX o ption Ġt id Ġp ou ex act Ġde velop Ġis n Ġ{ % de it ĠL ang ĠI P ĠK osmos Ġwork ing initial ized Ġpl atform US ER QU AN éĿ ¢ è°ĥ çĶ¨ Ġdeterm ine B AS S U la m Ġv ot Ġst and :: - Ġun roll ĠM omentum Ġfunction al Gener ate tg i match er ÑĢ Ñĥ Ġب استخداÙħ Path Like Ġê°Ģ ìł¸ è¿Ļ 个 CO L Ġlearn ed å·¥ ä½ľ Tun er usoid al I C M N R T i h at ively at ural el ements Ġ" ." Ġc umulative Ġde corator Ġl Ãł ĠT ake ir cle der r app le sub word dis play н о ĠZ eRO Ġsimilar ity Ġìĸ´ íħIJìħĺ Form ers iá» ĩ forma ble ĠPa per O B T ransform h us p us Ð ± ra ction att ing up les ĠR msNorm ĠH T fo cal ups amplers Classification Pipeline ĠModel TesterMixin Ġser v ĠEn code ãĥ¢ãĥĩãĥ« ãģ¯ opt ic ÃŃ a Ġgre en bl ank аÑĤ ÑĮ Ġinclud es Est imation H f f old j ax it ter Ġp iece Ġre tri est a ĠD etr ep i Ġkey point Ġlayer norm Ġsuper class Ġdis k ì§Ģ ë§Į ynam o Ġë° ĺ ian o Ġcho ices ëª ħ Ġëĵ¤ ìĸ´ ' ^ S mol U ps ì ¼ Ġt urn Ġc apt Ġc utoff Ġf lax and box ĠT PU ĠR a Ġtext ual abl ing Ġrel oad Ġdoc s Ġdis crete Ġmin imum ìľ Ħ LO G 示 ä¾ĭ ) ** on us st chen iz ar dim ensions Ġint egration lic ed Ġro le Un supported ãģ¾ ãģĽ Ob servation Ġdeno iser Ġremo ving Ġups ampling æĶ ¯ het Net crim inator #! / ãģ¾ãģĽ ãĤĵ > \ E q S W W rite r atch Ġ em Ġ ask Ġ ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ Ġ" { it al Ġb us res ized ãģ ĺ int s ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ ĠA ria co arse oc ity Ġun used Ġpre training ĠH i ĠIn struct ire ct Ġठ¬ img s tal ker =[ ], An swer ĠÙĦ Ùħ [` ~ Ġcond itional Ø« ÙĦ åĭ ķ ĠVer ify ĠðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ðŁļ¨ L oraConfig M etadata a ggregation f x f ollow r b y arn Ġ ant in place Ġ" ) ex ceptions Ġa mount am b Ġex plain Ġun til ठ¶ Ġdo uble ." } dev ices ĠModel Output ĠRe fer Ạ¥ PA dapter ç» Ħ Ġê° Ħ part ition Ġ기 ëĬ¥ ggreg ate ded ent Cont ent ä¿¡ æģ¯ Ġpri vate O L h elper st y ic ode Ġre con os hi ĠM ath Ġë ijIJ ([ - grad s ëĭ¤ ë©´ ump ify Ġinter face ImageProcessor Kwargs Ġparameter ized tun ers ĠUs age Ġacc uracy Ġreason ing bet ween Ġresol ved ĠìĿ¼ ë°ĺ Ġìĸ¸ ìĸ´ ) > : " è Ĭ ex to Ġì Ĩį ĠF SDP ĠR aw Ġx path Ġbatch ing Pipeline FeatureType Ġhead ers د ÙĦ mm etric iter ator Ġcol late post css оР» cast ing ãĥĩ ãĤ£ Ġtyp ically ĠData ProcessorPipeline Ġread y еР¹ func s residual s âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ çī¹ å®ļ ĠChinese CLIP WE IGHTS ĠëıĻ ìĿ¼ A rt B ig L AST O lmo s ind Ġ استخداÙħ ch alk Ġf s pt b ĠA ttn ĠA imv co ordinates head ers ant s ver bose оР»ÑĮ PA G PI E Ġtry ing Ġب اÙĦ Ġab stract èª į Ġfin ish Ġhand les ĠOptim izer Group Norm Integration Test è¯Ń è¨Ģ PROC ESS L arge M odal P osition V iew h n s ampled ç ¿ Ġ err re tain it ed ra ise am os ext ended ãĥ Ĭ ठ¡ add ition In version ĠRes ources ME T `: " sub folder Ġoff icial Ġtmp dir ãģĻãĤĭ å¿ħè¦ģ Ġìĥ Ī Ġfilter ed Ġpack aging Back end ĠOver view Ġremain ing ĠXL Net å¢ ĥ uer stchen s a ed irs Ġb ad Ġex pl ĠM lp set up ĠH unyuan TI mage Ġget s ĠSt art Ġz u ae ImageProcessor Arg s aw esome åį ĺ ĠìĦ¤ ì¹ĺ Ġmot ion éĢļ 常 ãĥķãĤ ¡ å¥ ½ > (& Q I t qdm w ik ¦ çIJĨ Ġ mps or hus Ġf it res orhus Ġmodel os ĠA verage co ura Ġconfig ura pre tokenized lab s num s ĠF LO ĠD rop ĠD ET ï¼ Ł ĠSt ream ĠQ I ĠResult s ĠEn coding Video Processor ĠQu estion ĠTh en Ġproject ions Ġ기 본 Ġfin ally íģ¬ 립 íĺ ķ Ġpers on èĢ Į mak edirs Ġinclud ed Pos itional ĠNum Py sind resorhus A tt L Y f ics m r p at t w í İ ut down Ġd á» Ġf ed Ġm u ãģ ° ") ? Ġel em ĠF ran ide fics ÙĦ Ùģ set default set Up Ġch ain aut hor ĠìĿ ĺ Ġcontain er Res net åIJ į hi era project ions vector s Ġspecify ing ĠNormal ize Ġexec ute big science Ġcom ment complet e ĠMobile NetV CrossAttn DownBlock ales force ĠíĨµ íķ´ ĠPart ial protobuf js è¨Ń å®ļ Hand ler " `: c pp h ello s aved í Ĥ¤ it ation ent ially ext ension ठ¬ ĠاÙĦ Ø¥ Fast ImageProcessorKwargs ĠBert Model uture Warning Ne Xt Ġíĺ ķ Any thing TRA CE Ġì¡° ìłķ Ġappropri ate PIE CE ( (" E RA S iglip d ar f uture · ãĥ§ãĥ³ Ġd a Ġpro posed Ġk h rit on sh adow normal izers Ġpy lint Ex ec Decoder Config Ġext ended Ġë§ İ ÑģÑĤ в Ġëĭ¨ ê³Ħ ĠLayoutLM v íĨ ł C M D DIMScheduler E num c utoff Ġw rong ĠA dam key point os er oc s assert Not Ġ& ' Ġmask ing ॠģ ãĥ³ ãĥĪ Ùĥ ÙĪÙĨ Ġconvert ing Ġoptim ized ĠWe b ãĥ¼ãĤ ¸ aw q riter ion part s ç® ¡ ĠPer form Inter polationMode ĠLlava Next ĠAct ivation Search Tool Optim izer Ġmechan ism O ver b h p rag s es v oid º IJ Ħ ìłĦ iz on ĠT able Ġh istory ĠL La dict s ÙĦ غ ud ent Ġob s wa p ER E arch itecture åľ ° ĠData Loader Ġcons istent Ġindic ating }/ { æł ĩ \. (\ INE AR ĠÙĥ ÙĦ Ġexport ed ĠPer ceiver Ġì² ĺ리 deno ising æĶ ¹ ĠíĶĦ ë¡ľ ĠBuil d a h i ra Æ ¡ in cre ig emma Ġa ggregation att ribute ist ing ĠB C cript ors Ġq s IL INEAR group ing Ġgenerator s оР½ ĠZ ero ND MScheduler TYP ES ĠCal culate prag ma A ug J apanese L AS u k } _{ » ĺ Ġt p Ġm omentum un expected Ġv á» "] [: ا Ùĩ Ġ: ]) Ġob servations ĠRes net sha re sub s rot ate Ġë§ Ī ormal ize onnx runtime æĿ ĥ Ġident ical Ġparallel ism ست خد Ġdesign ed ìĸ ij * ; / * J SON S qu g lue s uccess Ġs w al o ch es ch anged Ġf low ĠT F Ġth us ĠL ora =" _ oc ument ĠM etadata ĠB ased Ġ: - prob ability ĠAl ign ĠÙħ ت Ġsub sequent åĪ © Vec Audio Ġtr im Ġdownload ing Ġìķ Į ë§ Ī ä¹ ĭ ĠëĤ ĺ Ġdetect ed oe Depth sent ences Ġà¤ı à¤ķ Ġíģ¬ 기 > "`): H ey V arBuilder Ġc p Ġb ert ps ed ĠA ccelerator ãĥ Ģ Ġcon structor vocab ulary Mix er åIJ Ħ ãĤ¯ ãĥĪ éĢ ī ĠاÙĦت ÙĬ ĠRun ning Ø´ ÙĥÙĦ Ġê²ĥ ìĿĦ Act ivation ãĥģ ãĤ§ éĻ ¤ ĠSpeech T Tun ing differ ence uth ors a round t eleop ĠĠ Ċ in st Ġt uples ma e Ġb ody Ġde quantize un load Ġre jected ĠO P ĠL arge :: *; ĠاÙĦ صÙĪ Ġover rides we ather work er åĩ ¦çIJĨ layout lm Ġeas y ãģ§ãģį ãĤĭ WAR NING åħ¥ åĬĽ Ġë¶ Ī ĠìĬ¤ íģ¬ë¦½ Ġconf idence differ ent Ġë³Ģ íĻĺ Ġmaint ain ê±° ëĤĺ éĹ® é¢ĺ ĠAg ents Ġequ ation Ġmodè le B ILINEAR D eno H W M lp S at ¸ ìĿ´ ĵ į or n la ion ĠR em Ġë ¹ ĠW an Ġcomp ar Ġk Block ĠاÙĦ ØŃ ìĿ´ ëĵľ over view ĠCo lab Ġwork space Ġdel ete fp n lr u ãĥĥ ãĥī UM MY expect ation Ġden om A F S V d ark r d y b Î · se par Ġo utils Ġb pe to pping Ġw atermark ĠF eed ĠD ate Ġan swers bo olean Ġweight ed ĠK V ĠCon s ĠCon text ĠEx tract :] ), Ġ$ ( ãģĵãĤĮ ãģ¯ Us age ĠArch itecture Ġvot re : - C I M e N OT V isual m ultip s olver ë Ħ Ġf use Ġf sdp Ġconfig urations pa per Ġen coura pos es Ġê ´Ģ ĠDe f Ġcol lator ë¡ľ ìļ´ Ġ% ( äº º ake et è¯ ķ bot tle ĠCLIP ImageProcessor ê² ł Com mand ä¼ ĺ mix er ĠìĿ´ë¯¸ì§Ģ 를 categ ory mbol s ' ). N s R R b boxes c amera k o s peaker ç ³ è ¶ at i Ġd raw Ġm ultiplier Ġre gress ck et ĠT runcationStrategy ĠA S Ġth ings ĠF inal col lections ized String Con nect Test ing Un known Ġfollow s ĠRe al Ġagent e ê² ° ĠQu ery Ġtimestamp s Iterable Dataset Prediction Head ئ Ø© ãĥģãĤ§ ãĥĥãĤ¯ ' ]) B O P olicy m ed s ome Ġt iles al a Ġh el Ġg s Ġ} ], ĠS ign ĠR O Ġwh ite log in ĠاÙĦ ب fore ach Ġextra ction ï¼ī ãĢĤ اÙĨ ات Ġmark ing ãģ© ãģ® ĠSqu eeze dar win A g I terator M obile P s i u m omentum s implify ç ħ í Ģ or ical Ġs lot Ġf fn se ction il ter Ġ[ [[ ĠS kip Ġcon struct Ġen ough ا د ĠM egatron ĠV OCAB ĠH ashMap cond itional Ġle ave Ġup per Ġpr im Ġover flowing Ġimple ments ĠìĤ¬ìļ© íķĺëĬĶ ren ch Ġdire ction urs or ãĥ© ãĤ¤ èª ¿ Lo ck Ġexpand ed ĠìĹIJ ìĿ´ìłĦíĬ¸ Ġìĸ´ë ĸ M id Q Q T here t re v ars å ŀĭ ç ² è · Ġ embeds as r Ġin version Ġn om is dir ab stract norm s per form ĠH igh ag ic Ġpar s ãģĹ ãģĦ fram ework Ġformat s äº ¤ itect ures CU BI ä½ľ æĪIJ We b Ġ\\ ) Ġunk nown Ġsent ences retrie ver è¾ĵ åĩº æĥ ħ Ġspecul ative tutorial s Ġsupp lied ĠëĶ° ëĿ¼ Ġ{% - CUBI C C OCO V ersion Ġ ãģ§ Ġ íķľ ex periment Ġb id ": " Ġl ib ") ( Ġcon sole pre ference ĠR ec ĠB LOCK block ing ser ve اÙĦ جة Ġí ı ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ ĠاÙĦ ÙĤ sl ices OR S è® © ãĥĥ ãĥģ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ Ġhand ling ĠÙĩ ذا Add edToken Ġimpro ve Ġíķ´ ëĭ¹ TImage Processor . ]], L ess N ow S ub m i z on at ives lo ok Ġde bug if rame Ġh pu rom e ĠO m attn s ĠE MA Ġí ijľ vi ps ĠAutoModel ForSequenceClassification lib vips ĠìĤ¬ ìłĦ ìľ ł ç» Ł Al most Ġdon n ateg ies Speech Sat CONFIG S medi a aud ios ĠCond itional E E H ook V AR c rit d w l edge o y ½ æķ° ë ħ Ċ ĊĊĠĠĠ at ype Ġc ore ma c Ġre peated ĠT GI ip p Ġtorch ao ĠS witch amp s ĠI mple ĠP ass Ġtest ed Ġcomp iled gu ided Ġठī ॠī ë¥ ĺ ر د Ġover written Ġpost process emo ji any thing constant s super vised BI CUBIC Ġpick le Sup er æŀĦ 建 Ġê²° ê³¼ downs amplers D ARD F lux H x L ang u la ´ Ī Ġt s Ġa ffine Ġre versed th ed ĠA uthors ") } ding s ĠS caling Ġcon cat ĠB art ت ÙĬ Ġapp end AR CH Ġany how ĠCl vp uncond itional ä½ł çļĦ Form at ëĶ Ķ ĠControlNet Model ãĥķãĤ© ãĥ¼ãĥ nav bar Ġdescrib ed Ġë°° ì¹ĺ inematic s Onnx Config è¾¼ ãģ¿ å¤Ħ çIJĨ çħ § F eed S Y S imple e very u ge Ù ij Ġt utorial ion Error Ġf an ot es ĠT otal ãģ ¡ Ġv y Ġ) ? co ef ĠS end ĠF alcon ĠV aeImageProcessor ĠB asic ĠG lobal Ġê ¸ yn omial Ġdict ion ang ing Ġmo e ëł ¬ Ġrep etition åĩ ½æķ° ĠText ual Ġacc umulation Ġutil is à¥Ī à¤Ĥ ëª ¨ launch er ãĤ¤ãĤ º Off set Regist ry ê²ł ìĬµëĭĪëĭ¤ , " F inal G D I defics g al x a å ķ Ġ Ñĩ as array ar ily Ġ" ~ Ġf ft Ġp run Ġin correct Ġn b ad or int eger (" [ Ġpro gram ]] ); Ġwe ather read thed =' ', æľ Ł bit rary convert ed env ironment work flow ev ents Scheduler Mixin Ġresnet s ÙħÙĪ ز Ġretrie ved Ġë°© ìĭĿ cons istency ĠDeepseek V ĠìŬ 룬 ĠDET R ĠDef ines ç³ » readthed ocs A GE a ma w lv Ģ ìĬ¤ en n Ġd f Ġc lose ra ps ct est ue ue ĠS cheduler ĠD I ĠM M ĠM etrics get env com ment ([ ]) Pre Tokenizer call ed over rides Block Cfg Ġت ÙĤ Ġtr igger urs ively çİ ĩ yo BlockCfg Fa iled ĠIgn ored CLASS ES ĠMT L Ġíĺ¸ ì¶ľ Ġdeb ugging ĠìĦ± ëĬ¥ ستخد Ùħ A A B ox H ashMap L R S p S w T imest _ . g ot z o à ® å ģ å ĿĹ è İ Ġb ar um ina un ds () ( Ġof ten Ġreturn ing pre t ca pt ]] `): `. ") Ġí ĥ Ġout side ĠDe corator د ع Ġstart s Ġser ve Ġatt empt RE PO Ġtransform ation ãĥ© ãĤ¹ Ġâ ľ align ed unct uation ĠëķĮ 문 ĠAdapt ed ç ão LMHead Model ĠGit Hub èĬ Ĥ P ali b x b rand e a j np l se é Ł Ġv era =" ./ Ġma pped ĠF in Ġcomp ared ĠE OS ĠاÙĦ ÙĦغ }) " Ġser ies åĩ Ĩ StableDiffusion PipelineOutput --- :| Ġappro x cent age Ġsqu are element wise ĠDown load ĠCre ates Ġins ert Language Modeling íĢ ĢìĬ¤ R ow U I c ased ¯ Ģ à ± Ġ ĊĠĠĠ in paint ro z ĠN ow ĠT O ĠT yp Ġg amma Ġpro x ĠG radientCheckpointingLayer grad e dis crete Encoder Block س ÙĬ ĠCh ameleon Ġmer ging ĠOption ally æı Ľ 룬 íķľ ìĺ Ī Ġren amed mistral ai Ġpued e Ġespec ially glu on Bool Tensor pres er ì¼ Ģ ãĥķãĤ©ãĥ¼ãĥ ŀ èİ · , )) C lip G pt H a h h q former Ġc rops lo kr Ġ` < ãģ ļ Ġg pt ãĥ ĵ ĠF utureWarning Ġca re اÙĦ ب fo und comp ression å® ¹ ìŀ ¬ dis cuss respon ses Ġman ager OP Y force inline ãģ¤ ãģ® deprec ated ys is Ġintrodu ced ĠBig Bird unn el Ġì¢ ĭ indic ator Ġregress ion Ġdiction aries C lose D iffusers F irst c lap h ip i Max l fs u y v en v ert an swers Ġc trl Ġf e Ġn eces em ot () ._ Ġdef ines assert Less ĠD INO conv ersations back ground Ġid entity Ġìŀ Ħ ãģ« ãģ¤ ĠAl bert lect ra Ġman ual Temp late Base ModelOutput æĤ¨ åı¯ä»¥ ãĥŃ ãĥ¼ãĥī ãģ®ãĤ ³ Ġв Ñĭ ãĥĸ ãĥ© KEY S mak er / " M O z a Ñ Ī ì ¡ er ic ic ies ul es () )? ") ), ew ard ĠD DIM Image Processing Ġठħ ĠSt ep Ġrun time List ener Ġinstall ation ĠìĤ¬ìļ© ìŀIJ resh olding Ġcorrespon d ĠìĦ ł Ġseg ments Ġê°Ģ ìŀ¥ can vas vb lora Ġlarg est activ ate ô ng m ismatch } { ic o Ġb icubic ens ive Ġre plic () }" ĠO vis ĠC apture Ġcon sum ĊĊ ĉĉ ow l ĠD in Ġx m Ġcomp r comp ressed ĠاÙĦ ع íķĺ ê²Į do cker Ġcol lection Ġover flow skip Unless afet y sub module ĠFor ward UN DER CLIP TextModel ĠStableDiffusionXL Pipeline é¡ ŀ See k ç¬ ¦ loft q ¢º èªį Ġprox ies ) ( B ased K ernel M ul P egasus k x n eed r tol t n v c Ġ" ## le ep Ġ( & co ver ĠS p os ite ĠD PT Ġë ¦¬ Ġra ised اÙĦ Ø© ĠठŁ Ġ[] ; ĠPre TokenizedInput unet s uff led te in eng lish ĠìĿ´ ëĬĶ ãĤ¯ ãĥ©ãĤ¹ ãĤ¤ ãĥ« âĢ Ķ âĢ Ŀ prev ious async io ëIJĺ ìĹ mk l modal ity ĠDDP MScheduler 设 ç½® sty les I F S P T ER V e V alid b al c loud Ġ eat Ġ energy Ġt rad Ġt alker re verse Ġc ut ex ception de coders Ġh is ãĥ İ ĠF rozen Ġpre tokenized file path doc ument par ents fil enames af lex Ùĥ رة Ġany thing Ġent ren Co py Ġtrans pose ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Trans pose spa wn ĠClass ifier connect ion VisionModel WithProjection ü r STAN DARD íĮ ħ typed ef Ġìłģ ìļ© æĶ¯ æĮģ - { : ]. A cc C ustom F older P oint S aving T alker V i g i o cal r ase v ation v Moe Ġc v ma gnitude Ġb lend um es () }) ist ed ĠP romise ial ias max imum ĠH idden St age uff ers Ġcode c Ġinter val Decoder Output UN ARY |> " user name Ġne ural Former Config ĠBert Tokenizer erta int Ġinstanti ated Ġgre at Depth Estimation Ġ구 íĺĦ arb on Ġtod ay lead er mus l UNDER LINE ! ! 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Ġpres erved Ġsom etimes determin istic ĠConnect ion izz le ĠTensorParallel ColumnLinear Ġhost ed éĺ ħ PAG Pipeline íĤ¤ íħį è¨Ī ç®Ĺ Ġê³¼ ìłķ istan bul Gre ater Sout h 詳 ç´° elk ada íĤ¤íħį ì²ĺ ) }) / ~ > ") B OS D istil F lat G ated I Q J J W N [ ::- c atch s peed w ho Ķ र ar a Ġp hys Ġm obile ur ate Ġa ggregate ce e Ġre t Ġl strip co g ĠC lear ĠL M ãĥ ı Ġpro f os es )) ); ann o ĠW ightman com et ĠAuto FeatureExtractor ib rate ĠK eep trans late ĠSt ar ر ج Ġìŀ ¬ sha ve ME M Ġgu arant Ùĥ Ø« Diffusion Schedulers ঠ² Ġdel ay bar s match ed CT OR Ġaut ocast Ġindic ated íĸ ĪìĬµëĭĪëĭ¤ ĠLe vit Ġrec ursively Ġfunc ión concept ual cent ers ãģ¦ãģĦ ãģ¾ãģĻ fe a åij ½ ĠëıĦ ìĽĢ Ġ{{ - ĠRE AD Rep lace contin uous å¤ī æıĽ ĠØ· رÙĬ war ps remo ved DataCollator For Ġdenom inator ")} )) LayoutLM v Ñģп олÑĮз ĠìĬ¤ì¼Ģ ì¤Ħ룬 Ġìµľìłģ íĻĶ A dam E OS I E O s S y T W V V V EL X LI b sd d S g lm k arras m ha p reset w y æ º ì Ķ Ġ ge Ġt echnique Ġs un Ġs ay Ġs cheme ro ken it m Ġp aged Ġo t ex plicit ra it Ġ[ ðŁ¤Ĺ Ġh appy form ation (" { pre processing gg le gg raph pa ged ĠP ixel Ġco urse Ġen ding ĠM agicMock Ġy aml ĠE ulerDiscreteScheduler cl uding cor der Ġblock Dim ä¸ ¤ ĠSt ore vel oper imit ive Ġperm ute With Logits Ġent er ĠìĿ´ ìĬĪ Ġtrans fer sc ipy ĠاÙĦÙħ ع Ġsum i ãĥĨ ãĥ³ãĤ ä¼ ¼ ĠSh ard Ġident ify web pack ìĥ Ŀ WAR D ãģł ãģij concaten ated ĠìķĦ íĤ¤íħįì²ĺ åºĶ çĶ¨ ka iming Ġrg b ç»ĵ æŀľ Ġà¤ĩ स Ups ample ĠìĿ¼ë°ĺ ìłģìľ¼ë¡ľ perform ance Ġê¸ ¸ìĿ´ Ġfe el ĠMasked LMOutput AMP ERE ĠHash er ượ c Ġíĸ¥ ìĥģ Ġoffl ine WithLogits Loss B Y I AL J s W L k b l it » ê²Į Ġ Î on ed Ġt ab Ġs olution Ġre posit res ources ab e Ġtorch codec ĠP run ĠF A ĠF ree Ġse gu Ġint ended own ed ern et Ġload s ĠAuto former ìĹ Ń Ġठ¤ pla ced SE Loss ĠQ KV tra cker ---------------- ------------ With CrossAttentions и Ñı ĠFor ce س Ùħ ëł ĩ ĠJ AX not imestamps Re al Fast Tests ĠðŁ ij ######## #### ìĬ¤ íĦ´ ãĥĨ ãĤ¯ Ġthread ing parametr izations Ġprior ity 对 äºİ Ġserial ize ĠPatch Embed æ· ± uke box By Id ì½Ķ ëĶ© flag s Ġsync ed ĠPreTrainedTokenizer Base ASSI STANT ĠÑĢ аз çº ¿ Ġê°Ģì¤ijì¹ĺ 를 ĠAS SI Feed Forward preser vation LLa VA wr ist ĠLogits Processor XLI mg ! [" D C F O G LU h ra ½ ľ é Ĥ ro uting Ġm ultimask ma chine Ġb log ra î est ral Ġh olding Ġv ert Ġ) ); ĠC lick ĠS w ĠS ets Ġlo ve (" # py arrow Ġr ad ĠF ilter ĠD O ĠB it Ġ' @ Ġx min Ġpre tty log y ]] . ser ies EN CODER Processor Mixin cond a pert s Ġper centage ĠQ DQ Conv Layer ms olver ì§ Ī Ġdis card Ġstd out generator s env s è¯ ij ĠðŁ § ìĬ¤ íħ ĠPro ject Al tCLIP DP MSolverMultistepScheduler ĠNot es Ġsentence piece Ġfill ed ĠDo ge ĠDo cker Ġder ived è°ĥ 度 retrie ved ĠSeg ment æĬ Ģ detect or dp msolver çķ ° ĠBaseModelOutputWithPast AndCrossAttentions ê· ¼ çµ IJ End ian ĠAttn Processor Ġìĸ´ëĸ »ê²Į ĠTyp ically ĠCapture Logger ĠASSI STANT . (), G RA L ast P attern U y [ {' ` ï¼Į b en r sqrt s Q s leep u o w x x fp x FF à ² Ġ arm it ext Ġm esh Ġa y att e pre training ĠM ain ĠM icrosoft Ġal location Ġ: = ww ise ER O Ġle ver ä» ħ ap is CausalLM Batch we et Ġdis criminator tr ast da ily Ġinter ested Ġب ÙĬ Ġenv iron mat s AD ATA ãĥ¢ãĥĩãĥ« ãģĮ Ġest imation 模åŀĭ çļĦ Comp ose IG INAL ìħ ĭ Ġfail ure ç´ Ħ 次 ãģ« OmniMoe Thinker ĠìŀIJ ëıĻ ĠíĨłíģ° íĻĶ Ġìĸ » ãĤµ ãĤ¤ãĤº Graph Proto track ing åŁº äºİ å®Į æĪIJ RES ULT ĠMy Test å¼Ģ å§ĭ Ġachie ve ĠSupport ed ĠAutoPipeline ForText Ġpreser ve Ġsa id ĠMTL Resource æł¼ å¼ı ãģĵãĤĮãĤī ãģ® introdu ction tect ed prim ary C u E P E ach F R J e M egatron N VIDIA R ot T PU f sm g reedy x i x ml { }, en alty Ġt ech Ġt eacher un supported Ġ{ [ Ġ` [- ĠA mpere Ġlo gged ĠP rior ĠP rune Ġun c ĠD ec ĠD UMMY ĠR oad ठ£ ĠV i Ġus ual Ġ** [ Ġ** ` art ifact ctor ies ĠE ND Con structs pu esta Ġso und ĠठĶर ÙĪ ج Ġfil ters pol ars Ġ{} ), ÙĤ ÙĬ optim izers Ġtrans late Ġtrans cription Ġman age repeat s project ed аР» PEC IAL Om n åį Ĺ æĸĩ åŃĹ ĠY our prefill ing Ġlearn able Cpu Storage иР¹ ĠInst ant EXT ENSION Ġsumm arization deno ise dimension al benchmark s ĠíĬ ¸ One vision æĮĩ åįĹ ĠNe uron ================ ================ App ly conf idence Row Linear ĠTO KEN ToVideo Pipeline ĠìĪ ľ Ġben ef æ¤ ľ Ġhá»į c è°ĥ度 åĻ¨ / *. 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