Résumé
Adam mooy optimisatëru fasu liggéey bi ci ginaaw reso neuronal yu bees yi, di ajuste ci saasi tolluwaayu jàng bu wuute ngir parametre bu nekk. Dafa am solo ndax dafay tax tàggat model yu xóot yi gëna gaaw te gëna néew finicky ni wàcci gradient bu leer.
Adam ak Adaptive Optimizers ab bloku tabax la bu am njeexital ci kalite model bi, njëgu infrastructure bi, yeexal bi, ak wóor ci eskaal bi.
Plongeur bu xóot
Adam (Xayma waxtu buy méngoo), bi Kingma ak Ba dugal ci 2014, dafa boole ñaari xalaat. Bi njëkk mooy momentum: dafay tëye ab moyenne buy jeex ci gradient yi weesu (waxtu bu njëkk bi) suko defee yeesali tabax gaawaay ci yoon yu méngoo. Ñaareel ba mooy, eskalaasioŋ bu nekk ci parametre yi: dafay topp gradient kaare yi (ñaareelu saa bi) ba noppi xaaj jéego bu nekk ak rasine kare bu valeur boobu, suko defee parametre yi am gradient yu yaatu te bari bruit dañuy jël jéego yu gëna ndaw te yu bariwul luñuy yeesal dañuy jël jéego yu gëna mag. Adaptabilite bii dafay tekki ni mën nga jëfandikoo benn tolluwaayu jàng ci reso bi yépp. Benn xeetu, AdamW, dafay dindi diisaay bi ci yeesali gradient bi ba noppi nekk na default ngir tàggat transformateur yu mag ak modeli làkk.
Gis-gis xarala
Adam dafay tëye ñaari moyenne ci parametre bu nekk: m (degrade) ak v (degrade kaare), ñu yeesal ko ak tolluwaayu yàqu-yàqu beta1 (dafay faral di nekk 0.9) ak beta2 (dafay faral di am 0.999). Ndax ñoom ñaar ñu ngi tàmbalee ci zero, dañu leen di xaaj ak (1 - beta^t) ngir saafara jafe-jafe yi. Coppite bi mooy theta = theta - lr * m_hat / (sqrt (v_hat) + epsilon), fu epsilon (ci diggu 1e-8) tere xaaj ak nul. Lii moo tax Adam soxla tuuti jàng-taux tuning buñu ko méngale ak SGD bu yomb.
Xam Adam ak optimisatër yiy méngoo
Adam mooy optimisatëru fasu liggéey bi ci ginaaw reso neuronal yu bees yi, di ajuste ci saasi tolluwaayu jàng bu wuute ngir parametre bu nekk. Dafa am solo ndax dafay tax tàggat model yu xóot yi gëna gaaw te gëna néew finicky ni wàcci gradient bu leer. Adam ak Adaptive Optimizers ab bloku tabax la bu am njeexital ci kalite model bi, njëgu infrastructure bi, yeexal bi, ak wóor ci eskaal bi. Ngir tabax xam-xam bu xóot, jàppal Adam ak Adaptive Optimizers ni xeetu liggéey, du benn man-man: leeral njariñ yi nga bëgg, leeral xalaat yi, ak tàqale li sistem bi mëna def ci anam wu wóor ak li ba leegi soxla àtteb kàngam.
Ci jëf, ekip yu am doole yiy jëfandikoo Adam ak Adaptive Optimizers dañuy gëna xéewale architecture, done, ak tànneefi infrastructure ci wàllu wóor ak njëg. Dañuy bind kritër yu leer ngir am ndam, natt leen ci done yu dëggu ak def liggéey, ba noppi ñu baamtu ci anamu ñàkka mëna seetlu, du ci benn yoon benchmark wins. Mooy barab bi xam-xam theorie bi di soppiku nekk kàttan buy yàgg ci produit yi, ci politik yi ak ci liggéey yi.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jamano jooju, Optimisation benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi. Xeetu jëf bi gëna dëgër mooy boole gaawaayu jàngat ak disipline nguur: doxal pilote, jàpp firnde, siiwal dogal yi, ak wéy di yeesal kaaraange gi ci anam wi ñuy doxalee, li jëfandikukat bi di xaar, ak sàrti sàrt yi di jëm kanam.
njeextalu pexe
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Doxal ci àdduna dëgg
Taggat xeetu làkk yu mag yu melni GPT ak Llama, yuy jëfandikoo AdamW ni optimisatër buñ miin.
Fine-tuning benn classifier nataal buñu tàggat bu njëkk (lu melni, ResNet) ci kaw benn done buñ personaalise bu am taxawaayu njàngum Adam buñ jagleel.
Taggat xeetu diffusion yi ci ginaaw defarkati nataal yu melni Diffusion bu dëgër.
Doxal Adam 8-bit ci bibliotek yu melni bitsandbytes ngir méngale staadu optimisatër bi ci mémoire GPU bu néew.
Modèlu jëfandikoo
Adam ak Optimiser yuy méngoo ci jëf
Taggat xeetu làkk yu mag yu melni GPT ak Llama, yuy jëfandikoo AdamW ni optimisatër buñ miin.
Taggat xeetu làkk yu mag yu melni GPT ak Llama, yuy jëfandikoo AdamW ni optimizer standard Teams yi dañuy faral di am njariñ yu gëna baax suñu joxee threshold yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit ak njëgu njuumte ci diir bi.
Adam ak Optimiser yuy méngoo ci jëf
Fine-tuning benn classifier nataal buñu tàggat bu njëkk (lu melni, ResNet) ci kaw benn done buñ personaalise bu am taxawaayu njàngum Adam buñ jagleel.
Fine-tuning ab classifier nataal buñu tàggat bu njëkk (lu melni, ResNet) ci benn dataset buñ jagleel ak benn Adam learning rate Teams yi dañuy faral di am njariñ yu gëna baax suñu joxee thresholds yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njuréefi produit ak njëgu njuumte ci diir bi.
Adam ak Optimiser yuy méngoo ci jëf
Taggat xeetu diffusion yi ci ginaaw defarkati nataal yu melni Diffusion bu dëgër.
Taggat xeetu diffusion yi ci ginaaw generatëri nataal yu melni Stable Diffusion Teams dañuy faral di am njariñ yu gëna baax suñu leeralee threshold yu kalite ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit ak njëgu njuumte ci diir bi.
Adam ak Optimiser yuy méngoo ci jëf
Doxal Adam 8-bit ci bibliotek yu melni bitsandbytes ngir méngale staadu optimisatër bi ci mémoire GPU bu néew.
Dawal 8-bit Adam ci bibliotek yu melni bitsandbytes ngir méngale réew yu optimiser yi ci memory GPU bu gàtt Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee thresholds yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit ak njëgu njuumte ci diir bi.
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Benchmark ci biir sargal ak done yu dëggu.
Benchmark ci biir sargal ak done yu dëggu. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.