Tirade Model
Qiyaasida moodeelku waxa ay yaraynaysaa shabakada neerfayaasha iyada oo ku kaydisa nambaradeeda qaybo yar, markaa isla moodelku si dhakhso ah ayuu u shaqeeyaa oo ku shaqeeya qalab yar.
Dulmar
It is the main reason large models can fit on a single GPU, a laptop, or even a phone.
quusid qoto dheer
Moodooyinka la tababaray waxay caadi ahaan u kaydiyaan miisaan kasta sidii 32-bit ama 16-bit nambar sabaynaya. Tiraabiddu waxay beddeshaa kuwa leh qaabab sax ah oo hooseeya sida 8-bit integers (INT8) ama 4-bit values (INT4), gooynta xusuusta qiyaastii 4x ilaa 8x. Moodeelka cabbirka 70-bilyan ee u baahan 140GB gudaha 16-bit wuxuu hoos ugu dhici karaa 35GB 4-bit, isagoo ku habboon hal GPU-ga macaamiisha ah. Qabashadu waa sax: ku tuujin qiyamyo kala duwan oo kala duwan 256 ama 16 baaldi waxay luminaysaa faahfaahinta. Hababka casriga ah sida GPTQ, AWQ, iyo qaabka NF4 ee lagu isticmaalo QLoRA waxay soo xushaan arrimo ismiidaamineed oo caqli badan waxayna ilaaliyaan miisaanka ugu xasaasisan, markaa tayada lumintu badanaa way yar tahay. Tira koobnidu waa sababta qalabka sida llama.cpp iyo Ollama ay u socodsiin karaan moodooyinka karti u leh gudaha iyaga oo aan lahayn xarun xogeed.
Aragtida Farsamada
Khariidadaha qiyaasidu waxay qiimeeyaan qiyamka dhabta ah ee shabaqyada isugaynta yar iyadoo la isticmaalayo miisaan iyo eber-dhibceed: kaydsan_int = wareeg(qiimaha / cabirka) + eber_point. Doorashada miisaanka si fiican ayaa ah ciyaarta oo dhan. Isku-dheellitirka kanaalka ama koox kasta waxay haysaa miisaan gooni ah oo loogu talagalay jeexjeexyada shaxanka miisaanka, iyada oo ilaalinaysa saxnaanta halka ay muhiim tahay. Tirakoobka tababarka ka dib waxa uu beddelaa qaab la dhammeeyey, halka tabobarka xog-warranka ahi uu ka dhigayo isku-dubarid inta lagu jiro tababarka si ay shabakadu u barato in ay u dul-qaadato, iyada oo badiyaa siinaya saxsanaan hoose oo wanaagsan.
Saamaynta Istiraatijiyadeed
Qiimaha iyo miisaaniyada
Go'aamada qaab-dhismeedku waxay horseedaan waxqabadka iyo kharashka hawlgalka sannadaha.
Go'aamo cad
Waxbarashada farsamada waxay ka caawisaa kooxaha inay doortaan xidhmo sax ah, ma aha oo kaliya kan ugu cusub.
Xakamaynta tayada
Doorashooyinka injineernimada ee wanaagsan waxay yareeyaan shilalka la isku halleyn karo ee wax soo saarka.
Mustaqbalka Tirooyinka Model
Filo in saxnaanta weligeed-hoose ay noqoto mid caadi ah. Cilmi-baaristu waxay riixaysaa 4-bit, 2-bit, iyo xitaa miisaannada binary, iyo qorshayaal sax ah oo isku-dhafan oo ilaalinaya lakabyada xasaasiga ah sare. Hardware waa soo socota: GPU-yada iyo chips-yada taleefanka hadda waxaa ku jira unugyo xisaabeed INT8, INT4, iyo FP8. Qaababka sida FP8 iyo MXFP4 waxay ujeedadoodu tahay inay isku daraan baaxadda badmaaxiinta iyo cabbirka isugeynta. Marka lagu daro farsamooyinka sida QLoRA, qiyaasiddu waxay sii wadi doontaa samaynta moodooyinka xuduudka ah ee ka jaban si ay u shaqeeyaan oo si fiican u habeeyaan qalabka maalinlaha ah.
Dhaqangelinta Adduunka-dhabta ah
Ku shaqaynta moodelka 7B ama 13B Llama ee laptop-ka leh llama.cpp ama Ollama adoo isticmaalaya 4-bit GGUF files.
QLoRA waxay hagaajinaysaa moodal weyn oo hal GPU ah iyadoo lagu ilaalinayo miisaanka salka ku jira 4-bit NF4.
Ku darida moodooyinka INT8 ee taleefoonada leh wakhtiyada ay ku shaqeeyaan qalabka si ay kaaliyayaashu u shaqeeyaan offline iyo si gaar ah.
U adeegida barta dhamaadka API raqiis ah halkaas oo tirooyinka INT8/FP8 ay qiyaas ahaan labanlaabmaan wax soo saarka oo ay dhimaan qiimaha xusuusta.
Khatarta & Dariiqyada Ilaalada
Hagaajinta hal bartilmaameed waxay qarin kartaa daciifnimada nidaamka ballaaran.
Kaabayaasha dhaqaalaha iyo dayactirka inta badan waa la dhayalsadaa.
Nabadgelyada iyo daldaloolada u fiirsashada ayaa kori kara marka nidaamyadu noqdaan kuwo aad u adag.
Qorshe Hawleedka Dhaqangelinta
Qeex daahida, tayada, iyo bartilmaameedyada qiimaha ka hor inta aan la hirgelin.
Benchmark marka la eego culeyska dhabta ah iyo xaaladaha xogta.
La socodka qalabka khaladaadka, leexashada, iyo saamaynta isticmaalaha.
U diyaari dib-u-noqoshada iyo dariiqyada jawaab-celinta dhacdada ka hor inta aanad miisaan.
Sii wad Sahaminta
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Hagaha xiga
Diiwaanada Model
Su'aalaha soo noqnoqda
What is Model Quantization?
Qiyaasida moodeelku waxa ay yaraynaysaa shabakada neerfayaasha iyada oo ku kaydisa nambaradeeda qaybo yar, markaa isla moodelku si dhakhso ah ayuu u shaqeeyaa oo ku shaqeeya qalab yar. Waa sababta ugu weyn ee moodooyinka waaweyni ay ugu habboonaan karaan hal GPU, laptop, ama xitaa taleefan.
Waa maxay qiyaasta tusaalaha ugu horrayn ka beddela shabakada neural?
Tiro-ururintu waxay ku kaydisaa isla halbeegyada qaybo yar (tusaale INT8 ama INT4 halkii ay ka ahaan lahayd 16- ama 32-bit floats), yaraynta xusuusta waxayna dedejisaa xisaabta.
Qiyaastii intee in le'eg ayay xusuusta ka beddeli kartaa moodelka 16-bit ilaa 4-bit kaydinta?
Ka noqoshada 16-bits miisaankiiba ilaa 4-bits waxay dhimaysaa kaydinta qiyaastii afar jeer, waana sababta moodooyinka waaweyni ay ugu habboonaan karaan hal GPU.
Waa maxay hoos-u-dhaca ugu weyn ee tirada-yar-yar ee gardarrada leh?
Khariidaynta tiro balaadhan oo qiyam ah oo loo galiyay heerar kala duwan ayaa luminaya tafaasiisha, taas oo yarayn karta tayada ilaa miisaan-qaadista caqliga leh ay ilaalinayso miisaanka xasaasiga ah.
Sidee buu tabobarka xog-warranka uga duwan yahay tirooyinka tababarka ka dib?
Tababarka xog-ururinta-ka-og-ogaanshaha ahi waxa ay dhistaa khaladka soo-wareejinta wareegga tababarka, markaa shabakadu way la qabsataa oo badiyaa waxay ilaalisaa saxsanaan badan oo ballac yar.
Farsamokee adeegsata 4-bit quantization si ay u hagaajiso moodooyinka waaweyn ee la awoodi karo hal GPU?
QLoRA waxay ku haysaa moodelka saldhigga u ah mid la qaboojiyey oo ah 4-bit NF4 waxayna tababartaa miisaannada adabtarada yaryar, taasoo u oggolaanaysa moodooyinka waaweyn inay si fiican ugu habboon yihiin qalabka fudud.