#version: 0.2 Ġ Ġ ĠĠ ĠĠ ĠĠ Ġ Ċ ĠĠĠĠ i n e n o n e r a t o r e l Ġ t ĊĠĠĠĠ ĠĠĠ ĊĠĠĠĠ ĠĠĠĠ e s Ġ s u t Ġ = a s a l i on e d o d e t Ċ ĠĠĠ a r h e a n ĊĠĠĠĠĠĠĠĠ ĠĠĠ Ġ i s t c h r o el f Ġ d in g i t Ġ c Ġ f p ut i d i c Ġ " r e Ġ p l e i g Ġ m Ġ in l o en t od el s e Ġt he i z m p Ġ n ĊĠĠĠĠĠĠĠĠ ĠĠĠĠ a d Ġ o Ġs elf p e Ġt o r a e x Ġ b l a Ġ w m a u r k en c e Ġ r u m on f onf ig ĊĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ ) : Ġ ( c t Ġ a at t t o as s g e a m Ġd e p t u n ut put u l iz e ã ģ on e an d Ġi s c od " , q u i m or ch at ion er s or m Ġi f h t i s Ġ ` ( ) e m Ġ l es t Ġ T ra in s or Ċ ĊĠĠĠĠĠĠĠ N one ent ion Ġ - s elf p o i l o t Ġo f c k ar g Ġ [ i f Ġf or at e as k att ention m odel s ize Ġ # d en Ġm odel o l Ċ ĊĠĠĠ ex t ro m u s Ġ h Ġ * u e id den d e Ġde f l ass ma ge Ġ None u p cod er at ch ur n et urn p ro t h en sor v al it h " : c onfig er t or t to ken ã ĥ a b em b Ġ g Ġ and v e p s y pe in t ĊĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ i st am e Ġ ) o m Ġr eturn ic t Ġ { arg s " ) at es ã Ĥ la y emb ed ig ht _ _ k e in put ion al ul t i p / / Ġn ot m ask al l Ġ A i r Ġt h Ġs t st ates o utput mp ort iz er id s t orch l en f orm r an Ġ lo c lass " " d ing c es p r h a Ġ C Ġo utput h idden Ġin put a mp - - ` ` Ġ re > > t ype la b pt ional Ġ L Ġ S f rom ] , Ġto ken ic e Ġ O Ġ I . . in e el s Ġ el he ad ran s en ce Ġc onfig at as atas et g g o c ( " Ġ or st r Ġt orch c he p y r ue ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ ha pe Ġb e Ġw ith ke y r es s s c on at h as t g th n ame e ight M odel " ] u b en coder en er Ġ v Ġ e at a ) , lay er u se Ġ P Ġ ex mp t ar e Ġn um = " rain ed Ċ Ċ Ġi mport Ġp ro Ġc on d ict o w Ġs e r i f a rans form T ensor Ġm a c o ĠĠĠĠ ĠĠĠĠ i le ab le v ice Ġ F n um Ġ + : // ht t Ġ D t ext ) . len gth [ " ic en Ġ un b atch i x i al Ġf rom Ġel se Ġ y t er o s lo ck d er if f : : gg ing i mage ass ert lo at Ġ on c al Ġ ì e w l y e at Ġ( ` e ct embed ding in it ad d htt ps al se t est o u ) ) u re Ġ attention b o as e e ps ` , p p Ġh idden ar d T rue Ġi mage ar t p ath d im it ion val u et rained k w Ġ" "" Ġ } amp le kw args an ce Ġ as # # e p en d Ġ W od u rain ing v er qu ence a ge icen se ĠO ptional in d Ġ R Ġ _ Ġ M Ġ B ar am Ġ use u gging lab els Ġ us s hape input s Ġr es w eight >> > ent s iff us l es an n Ġw h ip el p re F or valu es i ve odu le a ult Ġdef ault it s if ic val ue an t ch ed pe c f f `` ` or e po int I N Ġ V de vice c om g es Ġth at Ġp r p a Ġ* * Ġ= = ransform ers p er o ptional d ataset ĠT he ĠL icense at or po s Ġt est i se ces sor embedding s fa ce * * c a att n Ġ ' Ġ x lay ers ipel ine a ve ] ( lo ad Ġc o pr etrained ã Ģ T o ct or Ġ an n orm pro mpt ma x Ġb atch head s embed s il l attention s lo g t ensor ck point i v a v Ġth is c c ct ion token s Ġ are ces s e g Ċ ĊĠĠĠĠĠĠĠĠĠĠĠ d ir l i ch ann lo w f t d s ow n Ġl et r or w ard ched ul Ġt ext ugging face b lock .. . ì Ŀ Ġy ou f or Ġst r t rain g et Ġ ë o ut -- -- Ġ en i as C onfig Ċ Ġ ut o f eat i o To ken Ġb y up le a y Ġg ener h uggingface cod e ur es Ġ- > de coder u d O N Ġ U Ġin t Ġinput s p l po ut Ġi t i mport ro pout Ġ E at ed id e j ect f ile b a ma ges chedul er cal e í ķ Ġs h Ġl en d ata n et chann els st ate Ġ k ar y f er Ġc an Ġ all Ġw e Ġg et Ġ al Ġ N Ġ G ĠĠĠĠ ĠĠĠ c l ri b t im an g et h is ion ] ) Ġtoken izer n ing s et Ġ encoder Ġp re Ġd ataset o ol Ġ H ific ation r ad F alse pe ct oc ab Ġ | ` ): output s amp les ion s it y Ġ __ o ur Ġre qu __ ( rib ut L I g ener at ive Ġn n ' , qu al iffus ion a i con v Ġc lass Ġse quence Ġ layer Ġm ask i re Ġma x ] ] ed ict Ġ at ra y u st Ġ- - ĠI n r ror et a Ġs et um ent Ġs up Ġt f er n () . m ent Ġw ill il s d oc Ġ key o k t a E rror Ġd o ` ] . _ Ġ ra Ġ qu od e ë ĭ pro b Ġc he Ġ[ ` Ġt raining Ġw eight f loat Ġ j token izer Ġoutput s Ġun der ro ss Ġ >>> st eps ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ p aram Ġc om ad ding Ġlo ad u al R A st ance er e b ias o b L M Ġ // h is t f Ġ < ic es Token izer a pt p i ãģ ® lab le in al Ġ & de f * , s c "" " Ġs pec pos ition Ġs hape Ġ : Ġ \ a ct on g Ġ @ A R Ġ assert d type T I s er model s Ġd ata ar ray ers ion Ġp ass N et et r Ġdefault s Ġ . bo ol Ġ[ " um m Ġ add b u Ġpro mpt che ckpoint ` . Ġt ransformers ãģ Ļ if i ind ex c ol d ropout Ġn ame e e ist ribut Ġus ed er m ( [ A S ut ils es s pect ed Ġde coder p ar a ch s w st art la x Ġn ew F loat Ġlo ss P U n n Ġd im Float Tensor ai lable log its n p ro p ul d Ġde vice Ġra ise Ġ args pl it ine ar ĠF alse r it E qual ca che s ample iz ation fer ence S T b ert Ġn o ce pt lo ss Ġ / el er se q Ġh as cc eler Ġp ar e c id x Ġrequ ir Ġ value er y g rad ext ra i ent O utput f ix Ġt im re ate Ġp os al ue r un Ġf ile D E istribut ed Ġin d s cale i mages p u Ġ ... ith ub lab el Ġin st i b feat ures ãĢ ģ g ithub P ro Ġ ch I n o uld in stance Ġco mp iz ed ãĥ ¼ a pp } . pro cess C on ãĢ Ĥ ri pt I mage p are Ġin it up y upy ter Ġt ype Ġn e Ġs ize ãĤ Ĵ j upyter ĠT F or g or y Ġl ist r g ma in ( ): w or Ġo b Ġt ensor Ġo ut arg et p ort lo b ang u ransform er Ġs o p en Ġf eat ation s ĠT rue ĠU n id th w w ta in b ase ro up str ing E R [ : Ġn p Ġp aram f o ad apt Ġma y s pec Ġd type = = Ġ head Ġtoken s pro cessor m o f il i es Ġt rain C lass iffus ers at ents Ġ le v ocab ĠV alue Ġwh en ra m E S weight s n o E N Ġ > d own Ġlo g Ġs c l ist etr ic angu age P re ĊĠĠĠĠ Ġ th on pt im i ch S e * ): ) ; ĠA uto L ay e mp P ipeline u mp ur ation Ġis instance eth od ar ning Ġex pected Ġsup er gg er an ge ba ck T est or es eg ative r c Ġel if ĊĠĠĠĠĠĠĠĠ Ġ () , ãģ « s h ĠT his t ro M A ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ arg ument Ġb ool con d b er t able Ġa pp or a um n el p Ġweight s un ction Ġfor ward et ers ) ? pro j e ch ĠI f n ew Y ou ] . Ġd istributed L ist IN G umm y Ġsh ould ĉ ĉ ix el t t ir st ĠC o m odule l ic ar ch n am Ġh ave r ate ĠValue Error O C Ġcon v ---- ---- O R Ġ up as h Ġe ach Ġ labels ' s w ith z er ud io Ġ la Ġpos ition ma p class es assert Equal av ailable ig n ãģ Ĺ à ° ãģ ¾ att r and om re e Ġi mp D I Ġres ult v i ' : u id Ġus ing s o P T Ġp adding e ad it ional p ast Ġb lock n d m l Ġ K t raining he ight ( ( a f Ġ= > Ġ` ( Lay er e val test er tro l Pro cessor D e s on Ġ attn l ice e am Ġche ckpoint amp l ifi er " ], pa che Ġw or Ġy our p atch Class ification e an v ision M odule icen s Ġ ê Ġ https . " ern el ig inal p la } , Ġc all Ġmodel s ĠR es en s adapt er Ġp ast Pre T is s ") , m b ì Ĺ en ces Ġm odule st ep o ad h ub pre fix N orm cod ing key s T he ro w est ion app end ĠC on t ransformers ãĥ ³ Ġon ly Ġp a Ġ í dataset s Ġ one Ġc a i e c ken ar k t r pe ech E T s cheduler ult ip ãĤ ĭ s ave C H val id o ver che ck Ġst ate ãģ ¯ t he Ġcon st fa ult Ġwh ich all y sw er ol ut Ġ u it em E n S E ĠI mage spec ial tim e in es Ġne ed c y em ory uid ance > < f g ĠP y o g py torch un d ãģ § Ġp er s plit re qu ĠA rg a e Ġ( " l l TI ON c at E D ëĭ ¤ a ce ĠS ee ( & re ad r el r eturn Ġ embeddings ãĥ Ī p ad ä ¸ Ġ ! pr edict ĠD e i ate Ġinit ial for ward us al k ip bo ok pt h Ġf loat [ - à © a x h o Ġcom put Ġl anguage Ġpr edict o ff ul l "] ) Ġr un Ġb ut se quence pla ce Ġo s ĠW h Ġc ol Ġf il ï ¼ íķ ĺ u ct re g v ed PreT rained ba cken Ġpar ser i el ar i c all O DE ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ est eps py thon Ġn orm param s ` : a k Ġm ore F I Ġ{ " ĠArg s om e ð Ł Ċ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ lob al cceler ator ëĭ Ī pre pare eth er m on Ġi mages Ġ ): bu il (). __ Ġst art s lice en ame Ġm ethod () ) B ert r ight Ġse q Ġp ipeline un k y p Ġ layers LI P in fo < / ot e it er Ġi d Ġv ocab â ĸ ë Ĭ po ch a g ëĭĪ ëĭ¤ = ' et ad block s Ġd own ve c Ġex cept ump y Ġf n etad ata n g Ġa g a ck Ġh elp l m P R i mp ur l model ing Ġnum ber Ġb u Ġ} , d iffusion T H ultip le g roup ## ## Ġrequir ed ãģ¾ ãģĻ Ġd ict z e i pe co mp Ġlog its ĠL ist in ference Ġ! = backen ds st able Ġf unction Ġ You tt ention les s Ġ ] Ġw idth o p j o feat ure ç Ķ ì Ħ a ut Ġlen gth Ġconfig uration Ġr ange G ener ( ** d o Ġc reate D OC v el nam es ĠH ugging f n ë ¥ de fault sc ores iz ers " { ctor y ëĬ Ķ Ġche ck ab s Ġco py = { Ġc or Ġpro cess Ġr el res s col umn c ross ith er un et ĠT ensor , ) run c j son Ġc ross ãĤ ¹ Ġp ad = ( iel d Ġt arget Ġex ample Ġ q ud a Ġ values Ġ end Ġw rit A M U T ot h à ¤ tensor s ìĿ ´ p on Ġp ipe norm al Ġs rc p an ex amples [: , Ġ+ = Ġspec ific cl s âĸ Ī Ġn egative U n Ġl ast [ [ ãģ ¦ t rans Ġf irst ' t vi ew ãĥ « Ġe ither ul es Ġf ol M ix ur r l ip p ipeline c pu Ġin ter ran ge se ed . __ ptim izer Ġs ample ODE L bo x jo in âĸĪ âĸĪ m od )? ; ìĹ IJ x t ĠF lax ì ļ if y Ġd oc D iffusion ") ; l ora ar ge T F Ġp o Ġrequir es Ġs cheduler Ġso ft im g ä ½ . { Ġf orm ed iate Ġd iff m an Ġt r Ġinst all T orch Ġdataset s ĠT uple fil es init ial Ġl atents li ed T ype olut ion doc string Ġpr etrained res ult I S PreTrained Model Ġqu ery ] : mpt y ind ices nd array s um Ġfeat ure ì Ĭ Q u form at Ġno ise Ġt ask im it Ġlo cal d ate pp ing um ents Ġob tain Ġs ame Ġo ver ww w w e po ol zer o c fg Ġlo gger RA N as ed etric s fa ctor u g el ect P E Ġpro cessor ff ic O UT Ġ Model htt p ãĤ Į ë ¡ ind ow urr ent B lock ãģ ¨ ë¥ ¼ ìĿ Ħ Ġind ex er ror Ġ ðŁ un t w are F OR Ġd iffusers con t Ġor iginal f ore Ġ[ ] wor d Ġ kwargs t al oad er l ast p h Ġo ur Se quence Ġsup port c ript pr ed Ġma ke th er m ean ãģ Į m in ãģ Ħ ĠUn ion us h cceler ate af et â Ķ docstring s qu ery i T v it Ġ height ens ion Ġp ath Ġn ext Ġpro v ampl ing prob s F ace Ġpr int Ġpass ed Ġp erm form er et er ire ctory Ġs ub l atents gener ate t mp ent ence id ual str uct Ġtim e ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ ( - S t t uple Mix in Model For S cheduler res net s qu pe ed ) ] ì ł pr ess Ġan y b s Ġgener ator Ġp y ãģ Ł ffic ient V ec f low Ġì ŀ ject ion ĠA N } ) ing le Ġa ct cod ed c uda ãĥ ĩ () ; l inear Ġ % qu ences Con v t arget Ġa v pa ce pi pe f l g n and le y n ĠWh ether Ġ` " assert True Ġgener ation re ed po se ( ' ment ation doc s ĠC ON Ġt uple ¤ Ĺ Ġto p Ġ arg T ext h f pre c M ODEL MA P Ġdiff er ĠAN Y L inear valid ation == == ãģ ĵ Ġcon t Ġfeat ures . ") Ġr andom Model Output d iffusers ĠPy Torch Ġp ixel re am C T i a n e ĠR eturn ĠG PU } ") o f ĠHugging Face Ġ` `` Ġapp lic s ub swer ing py right extra ctor W ith Ġm od ãĤ ¯ A n A ttention I L e mpty t ransformer m all d rop w idth ĠW e ee ze it le Ġconv ert b y LI C Ġr ight c reate L ong at ic ol der "] . çĶ ¨ Ġ[` ~ I TH class ifier cl ip Ġ ro re place Layer Norm Ġl imit Ġlo gging Ġfol low Ġk ernel orm er ĠO R Ġcon tain a usal d ummy el l Ġin to tim esteps off set e poch Ġb ias ĠO F Ġp ool ãģĻ ãĤĭ RA IN pi ed Ġex press T uple ust om ĠI S Ġ{ } V E it test Ġ" -- ĠV ersion Ġb et ib le gener ation Ġinst ead table Diffusion er as v ersion Ġbu il Ġm atch Ġst ep ä » Ġg rad Ġv ersion Test er DI TION Ġapplic able ç ļ ĠW AR param eters k s ol ve çļ Ħ k v Ġwrit ing Ġsoft ware ST R ãģ ª ke ep c ast ĠA pache down load p ixel Ġal so EN SE v ae Ġc ode ab il Ġ z iss ions b e o us Ġp atch Ġs ave ĠA d a pache w o Ġ ], icens ed IN D å IJ En coder LIC ENSE p ha ãĥ¼ ãĥ Ġla w ult i icens es L icense D ict ĠP re ĠB AS Ġof f TI ES ĠWAR RAN ITH OUT Ġi ds l icenses Ġperm issions ĠCON DITION mask ed ãģ ķ Ġimp lied ĠK IND ĠWARRAN TIES Ġreturn ed Ġcomp li Ġt emp Ġdo es Ġag reed ĠCONDITION S Ġcompli ance __ () ĠBAS IS ed u un c A uto w rit ĠT o po p gener ator Ġg over n umpy p adding ER T Ġsc ript ausal LM C ON mo ve ar n ë¡ ľ Ġgover ning s amples ET RAIN ĠRes ult R es Ġs u Ġb ase g uidance Ġj np ĠW ITHOUT ex p par se Ġs ure Ġgener ated id ed ĠCo pied Ġd is g y Ġa ccelerator } " Ġex amples Ġget attr m ut Ġlimit ations Ġd es b in m emory at io un e S elf i ke ETRAIN ED Con d Ċ ĉĉ Ġ embed ĠCon v l d Ġ X pt ion iv ation od oc aut odoc our ce Ġo ther t e FI G l ate STR ING ° Ģ Ġì Ŀ Ġform at l ed Ġm etadata at ent est ep v ir Ġca che grad ient l arge n egative mod ules o og Ġattention s ## # b ject de code Ġs cale Ġ) , initial izer -------- -------- Ġ embedding ĠA ll âĶ ģ r id Ġstr ide ol d Ġdiffer ent s d squ eeze lo gger C ausalLM sc ore att en Ġs plit ent ity com mon extra ct ĠReturn s a ke at al oog le ifi ed ĠE x f older Ġc l Ġtensor s Ġstr ing a udio le ase Ġt runc position s item s Sequence Classification Long Tensor ì § d is Ġind ices t ask Ġw as requ ire . , âĶģ âĶģ Ġ// / mb ed A T ") ) d ist re ct x iv pr es res hape ve ctor Ġcom m Ġnorm al a ges N AM Ġpre pare T ip et ri g ress b one b it Ġparam eters PR ETRAINED Ġf ield res olve l ong Ġth an zer os n ext Ġm in DOC STRING ar xiv str ide li b vir on ãĤ ¤ class ification trans pose Ġ heads W h b lob : ] es e den se Ġfil es Ġh ow Ġcon trol _ ( c ase a cc s kip c or k ernel T S a in ic k iz es ĠR e N ot Ġun et .. / ¨ ë at ing is h ) `, = - Qu estion a uto ĠðŁ ¤Ĺ Ġob ject atal oader f lax t es Ġw ant = [ ec ut yp es Ġm o conv ert " ): O bject r ary ãĥ ¢ ument ation å ® ob j res pon Ġh f Ġp ub n ot Ġb o . ", dim s un ct Ġl ib Ġd ropout Ġc urrent Ġ argument Ġu size m g AR CH Ġcomput e Ġst eps s cal ĠCo pyright CON FIG ex ample d a Ġb ack po st " ` r andom Ġ label u ra ãĥ ĥ ĠF or m eta b ash d iv co pe o pen Ġf a ist er lo cal Ġp art Ġdown load checkpoint ing ĠD ataset en code on ents ìĿ ĺ ê ³ ca y Ġ http Ġmask ed c ed Ġd el pr int De coder C LIP Ġv ari mbed ding å Ī ess age rain er e ft Ġt itle ä½ ¿ I VE Ġgener ate Gener ation to p ĠO k Ġfollow ing ĠA n g t L E Ġa udio f ter h er Ġpre v Ġclass ification Ġresult s ARCH IVE l ation r and bert a qu ant Ġcol umn g pt in s ar m test ing Ġë ª b ar ut f s rc å ı par ent D ataset token izers Ġst d 使 çĶ¨ ( * L o o id æ ľ et erm if t Ġo ptimizer er ved Ī ĺ in ed st d Ġtr y w ra at ure process ing Ġcon text el y Ġ J ide o F ormer tim m re po Ġl ike `] . p ert o und g en ĠD ata Ġd eta ] ], ff er m t is ual ãĤ Ĭ V ision s afet ĠT ransformers writ e Ġj ust à ± Ġg lobal chann el ann ot g lobal f p Ġa ut t ing ìĬ µ Ġright s ì§ Ģ Ġm ust P O all el f ree al f Ġpro b wor k S I o ice or age Ġres erved Ġper form ĠL icensed so ft co unt um ul ĠUn less er ies um er Se q Ġapp ly ipel ines P r it ect Ġg uidance ent er V iT pe ft M ask we en put e Ġa c Ġspec ial T his umer ate Ġe val An swering Ġa bo " > m ethod ! ( Ġt ra Question Answering h elp P ING : ` ( { Ġg roup con trol ix ed l ang ch unk Ġst ates o se ãĥ¢ ãĥĩ Ġsc ores un ing a p Ġin ference m ark ĠM ask Ġwh ere f ram m ents Ġs ome Ġo pen Ġa x up date b eam int erm ï¼ Į - > Image Processor MAP PING Ġs ingle Ġ error con st interm ediate imp l ction s cl u Ġre g Ġ[ [ Ġimp le Ġ att Ġf alse ") . en se ìĦ ľ Ĥ ¬ C o Ġm emory Ġe valu Ġt wo Ġprov ided A rg t emp E mbedding g oogle Ġt mp Ġa fter ¸ ° com m ì ŀ ext end Ġf inal Ġ ass la ma ĠI t ĠAuto Tokenizer Ġg iv Ġs elect cont ent etri e ĠE n Ġa cc ob ile buil d w ay m ode f irst L A [ ` Ġ keep ãĥ¢ãĥĩ ãĥ« r atio iz ing Ġs kip log prob ' ) ĠC LIP D ummy Ġc ase . """ w h et e m s abil ity re f d ataloader ãģĹ ãģ¦ F eat he ck Ġpa ir For SequenceClassification © ëĭĪëĭ¤ Ġre po or iginal ? ; Ġh ere p ush Ġparam s ĠT raining pres ent Ġrequ ire Ġma in Ġcall back it e int er f unction Ġì ł Ġm ultiple " . ´ ë è ¦ Ġ adapter ĠD iffusion P y m etrics ìļ © B ERT in y AR T se g ĠAuto ModelFor ) [ ck et io us Ġm ean et ect dir name arm up G PT Ġlib rary e ed form ation Ġëª ¨ë ex pected âĸĪâĸĪ âĸĪâĸĪ Ġ arch ch s ĠI D ist ic c lo Ġd on pro jection Ġìŀ Ī Ġbet ween ãģķ ãĤĮ un ed Ġth en i an ob ject A L Ġt rue Ġen coding s peech gener ated t ra Ġen umerate Ġto tal add ed predict ions ĠS elf Ġwor k eterm in A D l r íķ ľ å ¤ ĠH ub ãĥ¼ ãĤ co py f c or k it ial ra w ic al meta class x l im um y s ht ml Dummy Object F ast reg ister A d #### #### wra p ) ), ãĥ Ĺ Ġn am le vel Ġwor d att ern Ġcor respon s cope o pt s entence on ly l ine Ġhas attr ]( ../ m ediate group s Ġ` [ face book or der Ġd rop t ypes w indow el u Ġqu ant æ ĸ Ġi gn Ġax is Ġs cal Ġbe fore le ct w ise Ġen coded ire ct u ff c r e os H ead itect ure Ċ ĉ Ġe ps C E Ġspec ified C L Ġpr ed Ġde pth R e [ ' ver y con text t xt ~ ~ Ġcor rect ìĬµ ëĭĪëĭ¤ C K å Ĭ on es Ġres idual Ġthe m or ed Ġin formation fl atten Ġre fer Ġì Ĥ¬ Ġc od í Ĭ imp le in et s mall Ġt yp ar ed ĠD ict Ġgiv en ĠS e lo c ĠL o ' ] Ġtim esteps s v Ġarg uments Ġit s em ent or d ãĥ ª ensor s ul ary Ġì Īĺ l ator ĠG PT unct ional low er Ġ: , le an ] `, rel ative Ġ url ĠĠĠĠ Ġ ut il n er Mask ed t ransform m etric r on f ill Ġg o ä º Ġm ut w av Ġth ere r w pa ir m at en v le x P ath Ġf ine Ġf ram box es no ise clo se A N g pu Ġs av xt ra Cond itional ent ial g ram ĠS cript ura cy E X ĠP IL Ġadd ed ert a Ġtrain er call back a ccelerate f ul ãģĹ ãģ¾ãģĻ Ġcl s ĠT ransformer viron ment ĠS tableDiffusion l p r t ãĥ Ĩ ĠC lass Ġre ad e fficient us ed pro pert ab led íĬ ¸ Ġd en ml p Ġw arning ={ " Ġw indow ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ pe at ĠS ome s p com pute å ħ im il ch ar se quences Ġ ent į ¸ Ġex ist ìĿ Ģ imil ar Ġt rans ë ł // // ran k ĠB ert exp and L O ver age Ġ íķ ma pping Ġ# [ s izes de pth Ġp l ãĥ © clu de u ous Gener ator Ġ $ by tes un ittest ate gy n x Ġ et as es ĠT eam ë IJ res h s pan ) ): ê °Ģ al pha il on Ġrel ative F lax act ivation en ch U N ur ing Ġblock s G E ern al è ¿ ĭ ¤ ob erta Ġn on length s eps peed Ġbuil d Ġ channels m atch s ig T O s ampling Tokenizer Fast it ies p ng ĠB atch ĠS t ĠC heck d iff ase ModelOutput Feat ure umul ation ( __ . / c ing d f æ ķ re m train er Ġ array U p s ide Ġm ode Ġsupport ed qu estion Conditional Generation st em s q pos it ãĥ³ ãĤ Ġt ransform ** ** fil ename ĠIn itial In put p ipelines } } = _ Ġpre fix T rainer Ġtim estep ĠIn d P I ast er Ġse quences E x t iny Ġt gt res ize ew ork o ptim ìĬ ¤ ! [ Ġs in tr ing ] ; en able m ul Ġre move L U Ġ모ë į¸ ) ` t rue b le d ump propert y Ġ( ) æ Ī # [ h s ĠL ay ha p Ġn umpy Ġ .. Ġde prec Ġtest s M E to tal n ect For ConditionalGeneration en coding Ġg u Ġpredict ions st age Ġb oth ath er f ull Ġs d q a ãĤ Ī Tester Mixin ãģ į d ed ãģ Ĥ X L Ġal low E xtra Ġdef ined Ġse cond Ġinter mediate edu ce s umm D own sw ith mb da Ġpool ed Ġch ar Ġcod ing P ar p ub test s ] [ "] [ in to CH E M Scheduler un squeeze config uration est s e v " } Ġse e Ġe os Ġ> = etermin istic ig uous Ġqu estion NAM E Ġf l Ġê ² ĠTo ken U L ĠC an Ġres net man ual For QuestionAnswering sc ript D P Ġpredict ion ĠN ote Ġload ing ĠIn c b os R E y nam s ampl buil der i k ul ar int ernal ig h ĠT h Ġv ae w arning Ġtyp ing s low en den Ġl ong read y Ġm an Ġparam eter Ġdim ension Ġu int Ġt ransformer ĠC h > , um e ì ľ e arch u il all close predict ion ab les ĠìĤ¬ ìļ© Un ion p ip ca use D ata Ġv er ra ph Ġj son ê ¸° Ġc ustom off load ĠP lease Ġl ora result s A v ë ĵ Ġtrunc ation u le _ { ĠF ace LI ST epoch s s up Ġc re IN PU Ġp t ro und lo gging Ġx s Ġp res // / (" -- um ber comp onents D ep Ġ à w armup in k Ġthe y R L C ase Ġm at pt r Ġl atent control net Ġv ision Ġt eam m er M S h as Ġs er Ġconst ant column s yn c Ġin stance Ġra w f unctional A I ight man Ġinitial izer I s __ ) Ġnot e Masked LM Token Classification Ġpa per For CausalLM Ġse g Ġ: ] Ġlo w il ation ==== ==== m etadata u x c re ìĿ ¸ u ally Ġor der o is o ptimizer Ġz ip ign ature un ch ìĹIJ ìĦľ T emp Ġun ittest r en Ġal ready ition ing Ġlo ok f inal S peech Ġ Q Ġi mg Ġtemp file è¦ ģ le ft L e t ree Ġwh ile tt n F low Ġav oid struct ure Ġselect ed b eta v ari Ġj ax li ke Ġr ename bu ffer res olution m ultiple Ġe very as on y le Ġcont in ) `. C ross I d ãĤ ī Test Case Ġd uring e q Ġkey s Ġc andle ri or Ġload ed Ġl ine è ¡ an k Ġcon d Ġmethod s safet ensors ãģ ĭ pre v Ġ row bs z ant ic Ġre present m d íķ ł Ġgrad ient íķ ´ Ġ( ( Ġbo x Ġav ailable ãģĵ ãģ¨ :: < pi e normal ize back bone Ġp eft fil ter bu cket S tableDiffusion n b est ed ĠM ark I f Ġa ction assert List i or t y Ġl inear Ġë ° INPU TS ãģ ı om atic cat en ë ¦ l anguage Ġd set ĠS ize Lo ss "] [" un wrap Ġabo ut Ġk eras i er b ot Ġse ed Ġ} ; ) `): iss ing åIJ Ī è ª A B ar row assertList Equal h or Ġf ound ri x Ġdoc umentation ĠU Net check er Ġm etrics Ġsu ch Dep enden d m Ġs ampling ir d ĠW av Ġat ol Ġo ld im e P A Ġd s d irectory Ġun cond Ġbe cause ST ART : , T ransformer ool ing With P Av ailable en cy his per Dependen cy c ut posit ory Ġvocab ulary cript ion N e P aram d eta íķĺ ëĬĶ âĶģâĶģ âĶģâĶģ ĠA l Ġind ic Arg uments m id ì ĸ Ġtask s Extra ctor T E ic ro for ce Ġfil ename Ġle arning Ġi ter Ġ ad Ġb eta Not Available Dependency NotAvailable py test D ropout ĠOptional DependencyNotAvailable ion ary prec ision b f ut ure L og re peat L P Ġt able rw ightman g elu ynam ic D O pa int m essage D ense ãĤ ģ Ġspec ify R oberta ex port Ġprev ious = [" ma k Ġ es h ash Ġm etric F T m em Ġ ठĠoff set Ġ extra f ast buil t â Ģ sh ard CHE CK ĠA ttention orm al ) ). Auto ModelFor Ġf ind la p str ategy assert Is ä¸ Ģ j ax Ġl r Ġt rained str ip etrained Config ĠInd ices layer norm j np è ¨ ched ule Ġf ree cl one Ġnam es k eras m m le arning Ġal pha Ġd iffusion Ġê ° s ource íķ ©ëĭĪëĭ¤ cont iguous ĠAn y de cay ĠG ener pro gress ãĥ ¬ Ġqu e Ġpo ss u es Ġs um il y or ary Ġm ath ĠLo RA r ng Ġ ext Ġd i ur se Ġre mo ä» ¥ Ġs p C h Ġd irect Ġcon s Ġ vec Ġen vironment Ġmodel ing Ġth ese For TokenClassification vector s k y Ġadd itional w n ãĥ Ń Ġthe ir PT ext ({ " S eg tmp dirname Ġd ummy c enter Wh at ] [" bu g ug h R O LI PText C om Ġbe en Ġo ptim il ed a it Ġd st patch es Ġa ccelerate ** . ch o Ġwith out m e chedul ers ĠT O ĠP r pp ed ) } ĠC onfig ` ) Ġch unk é Ģ token ization ds et ok i Ġs ig Ġatt ribut For MaskedLM on nx ag er ĠP aram Ġmax imum ĠImage Net de v ãĥ³ãĤ ° C ho G B per mut ecut ter oki ecutter f ct Ġs pan pie ce Ġd irectory ãĤ¹ ãĥĪ ĠClass ification Ġo ptional trol Net offset s rem ote en c Ġ( [` acc umulation Ġe poch a ccelerator ì ĭ ment ed ate g u ch co s mask s de ep pa ck ĠS et ĠìŀĪ ìĬµëĭĪëĭ¤ A l Ġh uggingface Ġv b Ġid x cept ion A ttn app ly ar ange Ġbe am ãģ§ ãģĻ Ġì Ħ Ġup date S tring å Ń ë © Ġf p pro ject ĠW eight af e Ġdeta ils an s ro ad Ġmask s C A t s m ulti For Image l atent lay out ä ¾ Ġcheckpoint s res earch ench mark Ċ ĠĠ at ial Ġ) . i qu Ġs peech qu eries a w Ġe v ìľ ¼ ĠP ath d istributed ro ugh qu ential ca usal s z ìĦ ± doc umentation I T L oader eg at ï¼ ļ soft max Ġa p eps ilon ãģ§ ãģį s ingle Ġ à° Ġs afet IN T ìļ Ķ Ġevalu ation . < Ġp la ĠU se at er Ġ adapt . * ĠID s pon se Ġb est r y ' re t runc ign ore and ard Ġst age Ġpro per der ed Ġen able ! (" ĠP ro Log its e qual g s w in Ġde coding pr ior Ġmod ules ı Ħ re ak Ġbe ha road cast W e Ġca ched Ġarch itecture ĠMark down way s n ly Ġ Z eg r i ated dis able l et å ¿ ëĵ ľ c rop n ow Ġus es as ic ĠC PU M ultiple ist il ìĦ ¸ Ġm er Ġh alf pp er g it re move Cho ice ( | .. = Ġ< = Ġ` -- ĠN ormal se e Ġs mall ĠN umber sq rt Ġgu ide q dm Ġch oice B uil c ul n ode ãģ Ĩ ãĤĴ 使çĶ¨ f unc st atic ol s requ ir j it po lation de epspeed p g Ġc lip Ġre place Ġcontin ue " `): Ġc rop scal ing Ġf ull m y Ġstr uct ê³ ł æķ ° ê ² Ġclass ifier Ġs entence I MA str u c ur er r co okiecutter Ġ channel ãĥ ķ Ġd eterministic ĠL e ĠU s V alue p attern ] `: a c à ¥ Ġ[ - wor ds permut e B atch Ġst ream ter able en gth Ġ" . Ġsc ore Ġpo int Feature Extractor Ġ val Ġê °Ģ resh old è¡ Į ĠT ext Ġcall ed Ġm ulti Ġre vision im ent ois ing Ġ< / Ġle arn om ent å ¾ Ġ extract att er Ġrun ning Ġpo st å ° Ġ ar pl its Ġ Ċ is m `] ): Ġnote book h alf ãģ® ãĤ start swith å ł an swer Ġn ow Ġcomm and } ; å ľ Ġpres ent rel e ¯ ¸ Ġë ĭ¤ NAM ES å ĩ è ¯ etect ion Ġc fg Ġcontain ing p df Ġma p ĠTensor Flow spec ific Ġp ush Ġpy thon st ream ĠT ask Ġan swer rit er unc ased D irectory v ar n ote ress ion D im g l x s c ision ĠC om Ġsh ift Ġcorrespon ding f inet Ġcontrol net l in orary Directory Multiple Choice > ", ĠT est p il ĠA cc Ġtoken izers t en st orage B art ho ok c ustom Lay out ĠIn st o v ĠP ipeline Ġa uto () ?; Ġsh ard Ġprompt s ìĸ ´ í ģ oth er O P model name s pace PE CT ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ C ol ! -- ( _ ` ). s ignature ---------------- ---------------- ]] ] ' ll vi or l v Ġp rior ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ pa ces à ¼ í Ļ Ġt ake tim estep be fore Ġex p ") ] Ġb reak Ġw ay Ġproper ly r al à ¡ Ġs amples [: - y stem p ers path s load ed å® ļ W av Ġ" " point s Buil der m aster Ġb os Ġr etrie ĠV ec Ġdirect ly e y beam s Ġsin ce icro soft Ġs lice Ġb ased Ġdict ionary z ip Cond ition ĠParam eters token ize Ġwarning s us er Ġ... , Ġaut omatic d es Temp oraryDirectory ë¦ ¬ EX T Ġbatch ed Ġb el Ġmo st in f se arch ãģ ¤ ist ant PECT ED Ġd iv loss ary j pg ic s ě [ åı ¯ re vision pool ed ĠS ie < !-- ] ` g rid Ġth rough )) ) a pi l n Ġpredict ed Ġ Y Ķ Ħ æ ¨ Ġ validation Ġe as c urrent ĠGPU s ĠAd d m ix re q Ġit em ins ky Ġprocess ing ro berta Ġ} ) Ġ_ , T h le m Ġcomput ing g lossary Class ifier ` ]( c am and insky i ed l lama cr ete Ġs ource co urse ie ce Ġì ķ Ġposs ible æĪ IJ Ġr ate Ġus er Ġfa ctor t uning æ ³ Ġc apt ro t ] `): Ġid ent uff le åł ´ ¡ å Ġh y al lable ãģ ¿ egat ron Ġ valid Ġs ort ĠĠĠĠĠĠĠĠ ĠĠĠĠ { { / > Ġposition s b art Ŀ ¼ I G Ċĉĉ ĉĉ cho ices ff ect å ī Ġh ub apt er ing s Ġo pt our ces qual ity col lator e ar Ġ zero Ġd ist Ġcomp onents PreTrained Tokenizer Ġv isual ens ions Ġimple mentation ìľ¼ ë¡ľ ãĥ¼ãĥ ī odu ct mak er n on Ġwh at Ġprov ide N N s ure ĠìĿ ´ë b ig ĠA r tensor flow DI R åľ ¨ Ġsav ed ãĥ ģ age maker ff n ] ), Ġre pository Ġca usal v a u f à ¨ Ġlo c Ġsh ow Ġ est m ath is es Param eter Ġn ull id es ĊĠĠĠĠĠĠĠĠĠĠĠĠ Ġ Ġsoft max Ġset up Ġinst ant I D Ġpre process Ġign ored Ġm id åł´ åIJĪ sh ift ĠTO DO la mbda ĠAcc uracy ãĤ Ĥ à± į l er Ġs imple res cale ĠS table F A Ġcontain s -- > ĠO ption âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ note book and b ãģĤ ãĤĬ D Condition ĠT op st ore w arn Ġpar ent b n el lo Ġwor ks v ideo Ġë ³ Ġtim m ~~ ~~ al cul Ġo wn l at ed ge ĠL oad Ġ" \ SI Z ForImage Classification } / Ġn et v isual at s Ġvari ant A nd å Į Ġor ig Ġneed s Ġdeta il Ġs yn h y s elect ĠT PU ĠR un ĠSe quence ĠConv olution cond itioning il ing ãĥ ¡ Ġst em ĠìĿ ´ ĥ ½ Ġd one ìł ķ Ġarg parse v ing Ġin v Ġì ľ const ant Ġst andard ĠDe ep ca ption ê³ ¼ col lect Ġle ft s pect sh ort Ġì ¶ ë § ĠDe fault summ ary Un less / ', T raining Ġs umm requir es p art () ` ist s Ġpro jection ĠG et Ġcomp at af ter Ġass oc I mg ãģĹ ãģŁ à ³ safet y ĠM obile Ġbeha vior load ing Ġen v aseModelOutput WithP c ard PO INT oment um D A é ĩ Ġs peed em antic ow er Ġact ivation Ġal ways IMA GE N ext ĠB lock ĠPy thon Ġv ery Ġpr et gg reg ë© ´ r l ãģ ł mat mul Ġdeprec ated Ġremo ved c sv Ġ) ; Ġma pping Ġl arge Ġy ield al ty Ġh igh ) < G PU Ġh andle Ġì ĥ Į Ģ ĠS o o st Ġë į í Ħ Ġch ange A r S peed ë ² Ġc lean Ġo per Pr etrainedConfig ro gram ol ic -- - ĠE r Ċ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġs imilar Ġgroup s M ER l ines æ ĺ Ġm ight ãĥ ī ãĤ ¢ ran ch Ġì ĺ FI L ãģŁ ãĤģ la mp :: { Ġac cept Ġap pro ì ķ iv ely us s Ġre f ĠWh en f w m as av ing ĠEx ample ] ]) Ġs low un g il d par allel b est d p m ixed ad am Ġst yle PreT raining ad a Ġ( { Ġcont ent fram ework s ent to list Ġa verage () } Ġpar a Ġinstall ed åĪ Ĩ m icrosoft st ack ra pper )) ; or ding Ġs ent ìĹ ¬ M P ãĥ ij ĠD Type form ers ines e Ġback bone Ġden oising rate gy Ġ eta iter able ' ), s ha Ġobject s Ġabo ve ĠInitial ize Ġel ement Classifier Output Ġassoc iated Ġc rate data class LIPText Model > ( d el il ity ãĥķ ãĤ p lay Ġc annot () ? Ġre ference pl ic fram es ak er Ġdis able Ġpro ject For MultipleChoice extend ed c le Ġis s class method //// //// H ub V I g in Ġë § n s em o Ġdoes n Ġautomatic ally DCondition Model ar ization " /> ? ](../ Ġin her æľ Ģ i ew q kv Ġt qdm Ġw ere Ġrefer ences s m Ġw ould CL AS UL T r educe Ġpre cision ðŁ ¤Ĺ ph as St rategy ŀ ĭ ë Ł ra ct Ġfil ter resnet s D is ĠAutoModelFor CausalLM ax is comp ile a z Ġc ur un n Ġset ting Ġign ore cam el ann el abil ities ] )) on s Ġpro gress Ġal ong ãĤĴ æ Ġw ell Norm al ë ıĦ Ġclass es edict ion efficient net config s Ġv ideo ì ¹ finet uned un cond è ® ãĤ¤ ãĥ³ deta ch è ĥ½ Ġ entity le ctions ma ke Ġí Į Bert Model ìł ģ z en pro c p end r ed Ġm essage In stance temp late å º Ġp added M T å ĥ od es Ġc ast ĠL ab Ġres ponse Ġle vel " ; ` ], Ġst ore ver al P iece Ġfol der åĥ ı em ents ĠC ode A udio er ature Ġup d Le vel Ġf ast Ġì Ĺ Ġinitial ized open ai P IL i ents ĠH ow sampl er Ġm lp th read work ers rele ases th ing param eter v ers í ĸ Ġ` ` Ġneed ed in ish U S dist il bo ard mp le Ġde v / ) Ġ util Ġe qual Ġbuil der Ġin fo ĠT rainer ãĤ » Co pyright Ġacc ording bucket s Ġbel ow less M T rained er ial Ġd ie ** , Ġ" < Ġreturn s ãĥ¼ãĤ ¿ Par ser camel case Ġ( : Ġ[ ], pr et con cat ver se Ġ& [ Ġp lease and id c ent ĠB y in v egr ation æ¨ ¡å Ġconvert ed edu ction Ġevalu ate s in ĠE fficient Ġin clude pr od ĊĊ Ġ G EN T able ĠS peech "] : ĠEx amples Co mp or ds Ġd er ĠPre Trained p ct Ġun k ĠR o Ġpass ing CHECK POINT e ach r u íĦ ° F F Ġ quality L icensed De vice ######## ######## Ġtoken ized StableDiffusion Pipeline S ize che ma IN E }. " it ch Ġre ward ë Ŀ¼ Ġt ak For PreTraining ĠN ot ãĥĨ ãĤ B y ĠM ODEL A E { " Ġ" __ FOR MER Ġal t Ġag ent od y Ġmat rix Ġbox es la g Ġ[ ' ĠB ERT ãĥ¼ ãĤ¯ tr y caten ate ãĥ¬ ãĥ¼ãĥ t rained or ig Ġde code ãĤ ¿ istribut ion ĠRes Net Ġcomput ed en et ĠF eat e ck ¤ ij us ic hap ed ì¹ ĺ id er Ġa b all back ĠS h co gn ãģĵ ãĤĮ ãĥ ĸ fl ash æĸ ¹ U DA Ġwh ether oc al Ġbe gin Ġuse ful Ġus age Ġth ose åĮ ĸ Ġsafet y å¿ ħ Ġse veral Ġet c s imilar ĠC ross Ġbet ter c ore t len CT C p us ar r ãĥ § Ġv iew Ġco uld AS K tlen eck s l ¸ ı å Ĩ ï ¸ı ed it il i back ward ãĥĥ ãĥĪ Ĥ ĺ Ġs ys ĠB e Ġob j Ġtra ck Ġb sz Ġbe ing ĭ ãĥ³ãĤ° ig her der ive cess ing task s " }, est amp d st ad e is y c li ro ot Ġr ank sor t Model Tester Res ult s g ĠA PI co lab ĠIn ter wh ere se m é ģ ur al onf ormer ign ed En coding ï¼ ī íģ ¬ s chedule la va Ġex pect ä¸ ª æĸ ĩ seq len Ġpy torch 模å ŀĭ } ", Ġf lax Ġm ixed Ġi l ĠP R ĠM ake ca ched Ġsh ort sh ared Ġpy test Ġchar act sup er d b ch oice Ġcom mon ë ³ el se à° ¿ a a é ĸ ì Ļ Ġse p Ġlow er Ġe in Ġman y b et Ġl m Wh ether Ġdes c ĠC reate e valu u int Ġl es ĠLay er Ġwor ds Ġperform ance Embedding s ĠDiffusion Pipeline ç » or ial ìĿ ¼ Dim ension åī į ig B ma gen add itional per iment m ult ï¼ Ī Ġprob ability igB ird in clude Ġc lo Ġsig ma FA ULT unn el up lic ãģ§ãģį ãģ¾ãģĻ å ¸ an swering Ġ" , pre process Ġfram ework g ather Ġc ell un it ave form ãĥĪ ãĥ¬ãĥ¼ãĥ v es Ġc alcul Ġmean s R o ĠN ame SIZ E ib ility port ed Ġcorrect ly **** **** D Type Ġh and pr une Ġp our lo om ìĿ Į C ODE Ġrequ ests Module List D AT down sample æ ¬ ut orial Ġf eed Ġapp lied ho to ãģ¦ ãģĦ L oad { ' å Ľ u ke ĠV iT St ate Ġê² ĥ i ence in ner ãĤ ³ LM Head comm and Ġ emb en g b roadcast ãģ Ĭ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ Ġch o process es Ġobtain ed pred s et al Ġla unch sig ma B e t itle Ġd ilation Ġen code nect ion FIL ES annel Dimension row s g ray Logits Processor ãĥĪãĥ¬ãĥ¼ãĥ ĭãĥ³ãĤ° le ction ĠB lip ument Parser tro py ëIJ ĺ i ver w ait ĠF ile av g OC AB CL S æ ŀ un ity ĠO r olut e Ġover r ro zen Ġ( ' un ding ern els ([ [ ä¸ ĭ I terable En tropy O M f ield Ġth read Ġtoken ize ãĤ¹ ãĤ¯ olic y ìĻ Ģ f s Ġt a Ġa bs ĠE X Ġpa ck B lip ãĤ · Ġres olution Ġcomm it Ġac cess K EN e f x formers sd p ] ): In t rel u ol ver Ġv ers ... , ) ], n own Ġlo op Ġindic ates ëŁ ¬ e ma Ġin fer Ġg it ĠRes idual we ak å¿ħ è¦ģ n one en viron Ġ" ` Ġdo ing DI MScheduler g raph æ ı its and å® Ł assertIs Instance Com mon : < F N c ell it ive ĠP ooling ĠLay out B ase X LM m el ex cept Ġë ĮĢ in st ĠP eft Ċĉĉ ĉ ìĦ¸ ìļĶ p ix Ġs pace se p Ġ{ ' am ma Ġsav ing ut er Ġr en trans lation Ġcolumn s ĠInitial izing itsand bytes N o con cept f req c d å ¯ mo oth ħ ëĭĪëĭ¤ ãĤĴ ç Ġcomput ation l ing un ter )? . Ġà ł åĬ ł ra g seg mentation ded Token Ġì¶ Ķ d etr Ġint eg Ġ\ \ sw ers ìĭ ľ æľ ī ĠToken izer Ġsort ed ĠíĮ Į K L h i å ¼ __ .__ Ġse par f eed re ason ¯¸ ì§Ģ ħ į istil Bert N D reg ression Ġtransform s Y PE ] ", a ug m issing th is Ġfloat s hap es b box h ot p lo Ġr ng res idual res ume ê ° Ġc os ãģ Ŀ Ġpos itional u ge v is ser ial l ight Ġpar allel p ass lic ing O S p ull aut h ĠĠĠĠ ĠĠ Ġnormal ize sentence piece ir ed U M Ġmodel o Ġv ar os ition Ġen abled free ze æ¬ ¡ i os m k ist ent ä¾ ĭ Ġp attern batch es Ġen sure ìļ ° flow ing Ġro und len der ĠO nly Ġq kv Vision Model Ġg ot Ġaut hor ãģĤãĤĬ ãģ¾ãģĻ Ġ vectors ub le li er Ġfl atten Ġnet work c i s afe Ġl arg Ġsub folder Ġother wise t une IJ Ĩ it es ug in Encoder Decoder Ġnormal ized ì ² Ġla mbda Ġev en Dis crete f ig lect ra åĩ º ech n De ep Ġcol lections ãĥ§ ãĥ³ Ġde coded __( ** Ġne cess cogn ition or ies ĠL ora bo se object s Discrete Scheduler h ere Ġm ult qu et Ġinitial ization Ġpl us st ages Ġk now an y Pr ediction lender bot " `, pool er ãģĵ ãģ® ãĥĥ ãĤ¯ r etrie Ġde pen qu i () [ Seq LM Ġr atio Ġex ecut Ġquant ization ļ ł Ġsupport s Ġtrunc ated Ġpair s Ġscal ing c ased Ġn b Ġh ref ta x Arg umentParser ggreg ation Ġm e PA TH ĠB ase nam ed Ġí Ļ am l ist ral ĠA ccelerator ĠP adding cl ud Vision Config ç ī Ġ& & PU T Ġinitial ize Ġcre ated distil bert el em ĠC ol pen alty íķĺ ìŬ Ġdoc string Ġ he ãĥ ŀ Ġseg mentation æ³ ķ è ĩ as type Ġfor ce nn x Ġtarget s ìĬ¤ íĬ¸ Ġd ynamic th reshold Ġtr ust b float f e í ĶĦ Ĥ ¨ s chedulers ar ies ou g Ġìľ Ħ I mple c it m ed is ible eg in DE FAULT ĠEX PECTED f ine g ed cor rect ] } Ġ Qu am es Ġattribut e ( \ j ust ert s Ġpro j F orm a cken v els ãĤ µ Ġqu ick Ġpool er ãĤĪ ãģĨ Ġp hoto us ing íķĺ 기 Ġag ain rand n riter ia Ġexp and e ver et s ĠM odule Ġcom bin ho st Ġsp atial t le v t Ġr and encoder KL par ser Ġcorrespon ds Ġcapt ion ] ). Ġle ad íĸ ī H ello U Net i ally Ġf uture Ġv ia bos ity Py Torch ëIJ ľ IG H µ ¬ checkpoint s ìŀ ħëĭĪëĭ¤ A ct M obile R I end ed prob lem LM v b ugging st ract Ġn ode ĠìĿ´ë ¯¸ì§Ģ s paces t pu ç IJĨ Ġh o ]] : Ġt ypes Ġst orage Ġ``` """ Ġin spect Ġì § conv next ĠCon trolNet c lamp al s () ); Ġdef ine ãģ« ãģ¯ âĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģ Ġs ign Ġd u Ġk v fer ences ampl er ä¸ Ń ĠLe arning Ġs a pect rogram >< / ãģ§ ãģ¯ ĠDe cay Ġbu ffer ¦ Ĥ Ġf inet Ġp ip ãĥ Ŀ Ġhas h ãģĻãĤĭ ãģĵãģ¨ as us Ġin side token ized ãĥ ¥ u size Ġm issing ce iver Ġel ements ĠD DP Ġco unt ä¸ Ĭ Ad apter Diffusion Pipeline lat est Ġ" _ Ġre ason Ġe ffect tra ck Ġsyn tax p ed ex ists tr ast D etr x lm ë Ĥĺ Ġc ard ( [" Ġse arch Con trolNet ch ine opt ions _ , Ġb ert Cross Attention Ġin cre Ġpla in å ½ Ġ vector way ml ó n H ugging m n t gt Ġ , il t Ġh igher Ġgener al A X ck pt ĠM ax Pro jection Ġtensor flow ¬ ¸ ä ¿ Ġo p Ġe fficient Ġê ¸° Ġeas ily ) },  ł ĠV ision ìł Ħ Ġ ? Ġs to if orm æ Ĥ¨ Ġf mt re ference ex ecut Ġwrit e extra ction ãĥĪ ãĥ¼ãĤ¯ çĶ » ĠDataset s iqu e ut e Ġr est pa pers h r â ļł Ġr hs Ġp ers over write çĶ Ł Ġgo ing ĠStableDiffusion Pipeline Ne o W rapper f lex l ash æ Ģ am W ãĤ § ĠI mport batch ed iss ues olut ional Ġoff load R un ì ĺ transform s Down Block ` " a pan Ġt emb to k ĠL anguage SE P Ġver ify Ġtak es ãĤ ª ĠL inear ĠR eg s plits et ch Self Attention Ġtra cing âļł ï¸ı D X c andle (" .") 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ĠI l .. ]; Ġcon structor Tensor Shape ĠD LA ĠD inat ĠB elow For Generation Ġco efficients ĠE le ĠE SM Ġpre computed LM PredictionHead In f ĠTF LayoutLM Ġsc atter ãģ« ãĤĪãģ£ãģ¦ arch itectures Ġla m ãģĹ ãģ¾ãģĹãģŁ ĠPy torch é l Ġpredict ing Ġ] ; ãĤ¯ ãĤ·ãĥ§ãĥ³ Ġcontext lib ĠìŀĪ ëıĦë¡Ŀ ĠSe cond ĠQ DQ Ġext ras me asure ĠSet s åł ± ten ir Ġclo ser Ġclo sure Act Dense ãģĻãĤĭãģĵãģ¨ ãĤĴ hr net Ġsqu irrel Ġë³Ģ ê²½ çķ ¥ Ġactiv ate ãģ¡ ãĤī STAN T Ġatom ic Ġdro pping Ġagg i Ġcach ing ãĥĿãĤ¸ ãĥĪãĥª I Adapter N eed R educe Y i c umulative d et g erman l attice § ãĤģ ì ¸¡ Ġ âĶľâĶĢâĶĢ al so ed i Ġc ir Ġc riteria Ġm argin Ġw arn ĠT hose rom ise Ġh im ĠA T ĠA lex Ġst oring py list Ġex ceed Ġun ic Ġì Ī ĠW uerstchen Ġ' ' ĠG u gener ations ĠIn to Ġí Ķ group vit Ġfol ders ĠFlax T à± Ĩ fram eworks Par a UL ES hy brid Ġaccept able ãĥij ãĤ¹ ãģĬ åĭ cd n Ġport ion Ġê³ ł Ġëį°ìĿ´íĦ° ìħĭ ĠNet work dat adir ĠíĮĮìĿ¼ ìĿĦ ipp et OW L contin uing æĮĩ å®ļ BU G Ġface book di ag Ġëħ¸ ìĿ´ì¦Ī è¾ĵ åħ¥ Ġíĺ¸ ì¶ľ ĠðŁĶ ¥ ======================== ====== INTER PO æ£ Ģ ãĥİãĥ¼ãĥĪ ãĥĸãĥĥãĤ¯ åIJĪãĤı ãģĽ ãģĬåĭ §ãĤģ > \ C ausal E L G N M D M M R wLock W ITH d eter m ust p ho s elu { } | >" } ). 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ĠTFAlbert ForSequenceClassification Ġsynchron izing ?\ â Doc umentation IR ED Ġìłģìļ© íķ©ëĭĪëĭ¤ Ġì²ĺ리 íķ©ëĭĪëĭ¤ HA VE ĠìĦ¸íĬ¸ ìĹIJìĦľ enh anced ãĤĴæĮģ ãģ£ãģ¦ãģĦãĤĭ ĠSqu ared ãģ«ä½¿çĶ¨ ãģķãĤĮãģ¾ãģĻ ĠTr im Clap Model Ġtensorboard X ĠImageGPT ForImageClassification ĠExtract ed ĠCOR D ONNX Tensor Ġste er ĠMat ena Ġpreced ence Ġfocal net ĠMvp Config Ġ리 ìĥĺíĶĮë§ģ ãģ®ãģ¿ ãģĮ ĠTFConv Bert æľĢãĤĤ ç°¡åįĺãģª BlenderbotSmall PreTrainedModel Ġray on è¤ĩæķ°ãģ® GPU ĠSplinter Model ĠTFDeberta ForSequenceClassification ĠJs SplitDelimiterBehavior éģ¸æĬŀ èĤ¢ dr um ĠCpu Offload click bait 容 éĩı Url Dependency ĠìĺĪìĭľ 를 è¿IJè¡Į 以ä¸ĭåij½ä»¤ CLIPText ModelOutput CLIPText Transformer ĠìĤ´íİ ´ë Ġpod r ãģ®ä¾ĭ ãģ§ãģĻ ĠAST ForAudioClassification ãĥį ãĤ¤ãĥĨãĤ£ãĥĸ efficientformer v ĠìĿ´ë¦Ħ ìľ¼ë¡ľ Byte Tensor ĠíĽĦ ìĹIJ Gu ide OLO v ãĤ¹ãĤ± ãĤ¸ãĥ¥ãĥ¼ãĥ« Summary Writer ĠPreTokenizedInput Sequence Ġê°ĢìĿ´ëĵľ ìĹIJìĦľ introduction certification ĠðŁĵ ĸ hua wei 주 ëĬĶ ĠCore ference Ġrespons ibilities ãĤ¹ãĥĨãĥĥãĥĹ ãģ§ ĠDinat ForImageClassification ĠTFLayoutLM ForSequenceClassification ĠTFLayoutLM ForMaskedLM ĠWuerstchen Prior ĠWuerstchen DiffNeXt Ġcutoff s Ġ"* / ĠCamembert Model ĠFlaxWhisper Model Ġtrick ling Ġüber prüfen ĠFlaxBart ForSequenceClassification ĠOpenAIGPT LMHeadModel ĠOpenAIGPT DoubleHeadsModel ãĥĩãĤ³ãĥ¼ãĥĢ ãĥ¼ãģ¯ ĠLaunch ing Ġsatisf ies ĠCpmAnt Model ĠCpmAnt Tokenizer Ġdifficult y TFBert MainLayer ãĥĺ ãĥ« ĠXmod ForMaskedLM Deta ForObjectDetection Ġcool down REG EX Ġê°Ĵ ìĿ´ ĠìŀijìĦ± íķł OUR CES DeformableDetr Model DeformableDetr ForObjectDetection Realm Reader Ġ무 ìĭľ Ġutiliz an Ġë°©ìĭĿ ìĿĢ ĠViTHybrid ForImageClassification Da S zv ous ä½İ ä¸ĭ Ġhood ie Ġproces seur ãģĮåIJ«ãģ¾ãĤĮ ãĤĭ ĠSummarization Module Ġsequ enze åij¼ãģ³åĩº ãģĹ Ġ', ' ĠTFXLM WithLMHeadModel 导 èĩ´ Ġparagraph s ĠTFRobertaPreLayerNorm Model ĠTFEsm Model Ġscreen shot ĠðŁĴ ¾ Ġrestrict ed æ·± åħ¥ Ġ커뮤ëĭĪíĭ° ìĹIJ Node Ref ĠìķĦëŀĺ ìĿĺ íĹ Ī Ġshr inking ĠTFEfficientFormer ForImageClassificationWithTeacher Ġrobot ic ĠíĮ¨ ìĬ¤ }$ , Ġmilit 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ellig ent ãģĻãĤĭãģĵãģ¨ãģ¯ ãģ§ãģįãģ¾ãģĽãĤĵ å¢Ĺ ãĤĦ Ko haku Ġbb x å¤į æĿĤ ĠDepthAnything ForDepthEstimation LANGU AGES Ġmal formed ([^ `] çĶ»åĥıãģ¨ ãĥĨãĤŃãĤ¹ãĥĪãģ® Contribut or Ġparametr ized ĠBarkSemantic GenerationConfig ĠFUN C Ġsubstant ive ĠìĹ´ ìĿĦ Ġì§ij ì¤ij ĠDummyGeneratorBasedBuilder With DDPM Pipeline ĠTFCamembert Model èªŃãģ¿è¾¼ ãģ¾ãĤĮ Light ning Ġbus y definit ions Ġconsult ez åĩĨ å¤ĩ ĠìķĦëĭ ĻëĭĪëĭ¤ ĠAdaLora Model Den oiser reh ension Ġbloc s ãĥªãĥªãĥ¼ãĤ¹ ãģķãĤĮãģŁ ámet ros åĪĨå¸ĥ å¼ı ĠOpenLlama Config ĠSeed ableRandomSampler ĠKon vertierung ĠExpl oration Ġì¦ī ìĭľ Iss ues stell ung Ġful filled ĠAU C å®ĮäºĨ ãģĹãģŁãĤī PATCH TSMIXER Ġsé quence itut ional Ġ"| ". æĬĢ æľ¯ ĠëĤĺíĥĢ ëĥħëĭĪëĭ¤ Bn Act Ġ----- - Ġ목ë¡Ŀ ìĿĦ Reward Trainer íı ī Ġmor bid Ġetiqu eta Ġà¤ħ पन MIX ED eh calabres Ġling ue THRES H Ġì§Ī문 ìĹIJ Serial izedProto Ġsched a Ġrad io ĠDies e =` [ tan reinama Ġmist akes abel ief Ġä hn аР· Ġpersonalizz ata ĠCE O ĠAppend ix TextToVideo SDPipeline ãģ®ãĤ³ãĥ³ ãĥĨãĤŃãĤ¹ãĥĪ 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Custom Tokenizer Stage B ĠXLNet TokenizerFast è¿Ľè¡Į è®Ńç»ĥ day s ĠPost process ĠEsm Tokenizer IF PipelineOutput IF Inpainting ĠTFMobile BertForSequenceClassification ĠTFMobile BertForNextSentencePrediction ĠMod ulation ĠPrediction Output DeiT ForImageClassification ĠëĮĢíķ´ ìĦľëĬĶ Ġlot to Ġë°° ìļ¸ ĠDPT FeatureExtractor GroupViT VisionConfig GroupViT TextConfig Ġty po ãĥijãĥ©ãĥ¡ãĥ¼ãĤ¿ ãĥ¼ ãĥijãĥ©ãĥ¡ãĥ¼ãĤ¿ ãģ¯ æİ¥ åıĹ ĠRequest Handler ãĥģãĤ§ãĥĥãĤ¯ãĥĿãĤ¤ãĥ³ãĥĪ ãģ® Ġfon ctions ONE FORMER MPNet TokenizerFast ĠReformer Layer ĠReformer ForQuestionAnswering Ġarr ested SP MD Weight Norm ĠEnd points Ġ구ìĦ± ëIJ©ëĭĪëĭ¤ ç±» åĪ« Ġì¶ľëł¥ íķ©ëĭĪëĭ¤ Ġì¶ľëł¥ ëIJ©ëĭĪëĭ¤ Visual BertModel о Ñģ ĠBloom ForSequenceClassification ĠLMSDiscreteScheduler State 미 ëĦIJ orth and åĬłè½½ åĴĮ ãĤĴ使çĶ¨ãģĻãĤĭ åīįãģ« Diff erent ĠRMS prop modal ities äºĭåīįãĥĪãĥ¬ãĥ¼ãĥĭãĥ³ãĤ° ãģķãĤĮãģ¦ãģĦãģ¾ãģĻ Ġtuning Params Ġnone mpty pow ers ĠMetric Module ĠFree Init ĠVer wenden à°ļ à°µà°ļ å½ĵ æĤ¨ å½ĵ ä½ł å½ĵ çĦ¶ Ġnu ances Ġnu cleus æ§ĭ æĸĩ ãģªãģı ãģ¦ãĤĤ ĠLayoutLM V ĠLayoutLM ForTokenClassification ij bn does n Tra cking Ġuniform e ãĥĦãĥ¼ãĥ« ãĤĴ ĠMarkupLM ForTokenClassification ĠMusicgen Decoder ĠRegression TrainingArguments He LLo ãĤĴä½ľæĪIJ ãģ§ãģįãģ¾ãģĻ ĠFlaxBigBird ForMaskedLM ĠPatchTST ForClassification ĠSimple Model ĠRel evant е ÑĢ Ġalle z ĠëĤ´ ìĿĺ Command Encoder è°ĥ è¯ķ ĠãģĮ å¿ħè¦ģãģ§ãģĻ Ġpiece wise Ġhack er Sam ImageProcessor fla gged Ġret ro BASE PATH ]+ ), ]+ (.| ĠDep recationWarning åĪ¶ ç´Ħ "[ ^\ ãĥľ ãĥĥãĥĪ ĠJukebox Conv ĠJukebox Prior ĠNezha ForQuestionAnswering ĠNezha ForTokenClassification ĠUniSpeechSat ForAudioFrameClassification ĠLocal EntryNotFoundError Copy ToClipBoardBtn Tim estamp Ġì¡°ìłķ íķ©ëĭĪëĭ¤ Ġexit ing Perceiver Model Perceiver Tokenizer Perceiver ImageProcessor ĠTyp edExamplesIterable ĠFNet ForPreTraining åı¯èĥ½æĢ§ ãģĮé«ĺãģĦ åĪ©çĶ¨ ãģ§ãģįãģ¾ãģĻ ãĥijãĤ¤ãĥĹãĥ©ãĤ¤ãĥ³ ãĤĴ ĠMy TrialShortNamer Ġcrash es Ġíģ¬ê¸° ìĿĺ Ġhop ing ãĥ¡ãĤ½ãĥĥãĥī ãĤĴ ãĤµãĥĿãĥ¼ãĥĪ ãģĹãģ¦ãģĦãģ¾ãģĻ ĠWavLM ForSequenceClassification ĠWavLM ForCTC ĠEfficientFormer V ĠMPNet ForSequenceClassification Ġfun zioni CTCTokenizer Output OpenAIGPT Config SU PPORTS ĠPool FormerModel Ġì²´íģ¬íı¬ìĿ¸íĬ¸ ìĿĺ ĠInfer StreamResponse Local EntryNotFoundError Padding Mode Ġ***** ". CTRL Config ĠìĤ¬ìļ©ìŀIJ ëĬĶ ä»İ èĢĮ Ġamong st Softmax Backward ĠNorm Type ãģĻãĤĭæĸ¹æ³ķ ãĤĴ示ãģĹãģ¦ãģĦãģ¾ãģĻ ãĥ©ãĥĻãĥ« ãģ¯ èªŃ çĤ¹ ãĤ·ãĥ¼ãĤ±ãĥ³ãĤ¹ åĪĨé¡ŀãĥĺãĥĥãĥī ĠLogits Warper ĠOnnx Seq AUTO FORMER ĠìĦ¤ëªħ ìĿ´ introdu ced Ġusar lo éĩįãģ¿ ãģĮ qh bits Ġë¬ ´ë£Į Client Error macro language ĠTFBaseModelOutput WithNoAttention ĠAgent Text ĠLED Model ĠRealm KnowledgeAugEncoder Mod i Ġcy tosolic Ġdr ums ĠAnn a Unexpected DType Ġë¡ľëĵľ íķĺ기 åģ ı ĠTFLongformer ForQuestionAnswering ĠTFLongformer ForTokenClassification ViTMAE ForPreTraining åħ³ èģĶ Ġentren ado ĠìĺĪìłľ ìĹIJìĦľëĬĶ Single Block Smem CopyAtom review ed ĠTFFunnel ForQuestionAnswering ĠTFFunnel ForTokenClassification ĠTFFunnel ForMaskedLM ĠTFFunnel ForPreTraining å¤ĸ ãĤĮ ãĤ³ãĥ³ ãĥĨ bi encoder ĠFlaubert ForSequenceClassification itg lied ĠMarian ForCausalLM ĠMarian MT ĠëĤ ľ éĿŀ常ãģ« å½¹ç«ĭãģ¡ãģ¾ãģĻ SER PER SER IALI ĠProcess es ĠTFRoberta MainLayer Ġconc ise Ġìłģìļ© íķĺìŬ Ġìłģìļ© ëIJ©ëĭĪëĭ¤ Ġë¶Ī룬 ìĻĢ н ой EXTRA CTED æ¸Īãģ¿ ãģ¾ãģĻ ĠTFRoFormer ForQuestionAnswering ĠTFRoFormer ForCausalLM ĠTFRoFormer ForTokenClassification GPTNeoX Model Ġ구íĺĦ ìĿ´ Ġ구íĺĦ ê³¼ Ġ## ", ĊĊĉĉ ĉĉĉĉ ĠTFElectra ForQuestionAnswering ĠTFElectra ForTokenClassification ĠTFElectra ForMaskedLM Decay LengthPenalty Ġë¹ł ë¥´ê³ł Siglip Tokenizer Siglip TextModel ĠTr OC ĠFeed forward ĠNystromformer ForSequenceClassification land ing rer oll ĠGPTNeo ForSequenceClassification CLIPSeg VisionModel Buf Reader ĠCvt SelfAttention ãĤĴçĶŁæĪIJ ãģĹ ĠìŀijëıĻ íķĺëĬĶ