Quantization
I-Quantization incipha imodeli ye-AI ngokugcina izinombolo zayo ngokunemba okuphansi, ngakho imodeli ebidinga i-GPU yesikhungo sedatha kwesinye isikhathi ingasebenza kukhompuyutha ephathekayo noma ifoni.
Uhlolojikelele
It is the main trick that makes large language models cheap and fast enough to deploy widely.
I-Deep Dive
Inethiwekhi ye-neural ngokuvamile iyinqwaba yezinombolo ezibizwa ngokuthi izisindo, ezivamise ukugcinwa njengamavelu angu-16- noma angu-32-bit floating-point. I-Quantization igcina lezo zisindo kusetshenziswa amabhithi ambalwa, ngokuvamile angu-8-bit (INT8) noma ama-4-bit integers. Ukusuka ku-16-bit kuye ku-4-bit kusike inkumbulo cishe ngokuphindwe kane, ngakho imodeli yepharamitha engu-70-billion edinga cishe u-140GB ku-16-bit ingangena cishe ku-35GB ku-4-bit. Izinombolo ezincane nazo zihamba ngenkumbulo ngokushesha, okuvame ukusheshisa ukukhiqiza. Ukubamba ukunemba: ukuminya ububanzi bamanani emazingeni ambalwa kwethula iphutha lokusondeza. Izindlela ezinhle zinciphisa lokho kulahlekelwa ngokukhetha ngokucophelela izici zokukala nokuvikela izisindo ezibucayi kakhulu, ngakho imodeli iziphatha cishe ngokufana ngenkathi isebenzisa ingxenye encane yezinsiza.
I-Technical Insight
Iqembu ngalinye lezisindo lithola isici sesikali esibeka amanani angempela kusethi encane yama-integer; ukuphindaphinda emuva ngesilinganiso cishe kwakha kabusha inombolo yoqobo. Izindlela zokulinganisa ngemva kokuqeqeshwa njenge-GPTQ ne-AWQ zihlaziya idathasethi encane yokulinganisa ukuze kunqunywe ukuthi yiziphi izisindo ezibaluleke kakhulu futhi zisethe izikali ukuze kuncishiswe iphutha lokuphumayo, kunokusondeza yonke into ngokungaboni. Ukwenza kusebenze kuvame ukugcinwa ngokunemba okuphezulu ngoba kuyahluka kakhulu ngesikhathi sokusebenza. Umphumela uyimodeli egcina izinombolo ezingu-4-bit kodwa ehlanganisa imiphumela eduze kakhulu nenguqulo enembe ngokugcwele.
I-Strategic Impact
Isivinini nesikali
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana.
Finyelela futhi ufinyelele
Yandisa ukufinyelela kuzo zonke izilimi nezitayela zokuxhumana.
Izinqumo ezicacile
Amaqembu angachitha isikhathi esiningi ekwahluleleni kuyilapho i-automation isingatha impinda.
Ikusasa Le-Quantization
Lindela ukulinganisa ukuze kube okuzenzakalelayo kunokuba ukulungiselelwa. Abathengisi bezingxenyekazi zekhompuyutha bengeza ukwesekwa komdabu kwe-4-bit ngisho ne-low-bit, kanye namasu afana nokuqeqeshwa kokwazi ukulinganisa inani lokubhaka ukubekezelela ukunemba okuphansi kumodeli kusukela ekuqaleni, kunciphisa ukulahlekelwa ukunemba ngokuqhubekayo. Ucwaningo lwezethulo ezingu-2-bit kanye no-1-bit (kanambambili) luyasebenza, kuhloswe ngalo ukusebenzisa amamodeli anekhono kumafoni nama-chip ashumekiwe. Njengoba i-AI ekudivayisi neyimfihlo ikhula, amamodeli anenani asebenza kahle azoba maphakathi nokusebenzisa abasizi endaweni ngaphandle kokuthumela idatha emafini.
Ukuqaliswa Komhlaba Wangempela
Ukusebenzisa imodeli yengxoxo efana ne-Llama endaweni ku-GPU yomthengi usebenzisa amafayela angu-4-bit GGUF noma e-GPTQ esikhundleni sokudinga amakhadi amaningi esikhungo sedatha.
Izisizi ezikudivayisi kumafoni, lapho amamodeli angu-8-bit noma angu-4-bit avumela izici zenkulumo nezombhalo zisebenze ngaphandle koxhumano lwenethiwekhi.
Ukunciphisa izindleko ze-cloud ze-bot yokwesekwa kwamakhasimende ngokunikeza imodeli ye-INT8, ukufaka izicelo eziningi ku-GPU ngayinye.
Amadivayisi e-Edge anjengamakhamera ahlakaniphile noma izinzwa ze-IoT asebenzisa amamodeli olimi okubona alinganiselwe ngaphakathi kwemikhawulo eqinile yenkumbulo.
Izingozi & Guardrails
Amaqiniso akhonjiwe angafaka ngokuthula imibiko, ukugeleza kosekelo, noma imiphumela yocwaningo.
Ukuzwela okusheshayo kungadala imiphumela engahambisani kuzo zonke izicelo ezifanayo.
Idatha yombhalo ebucayi ingase idalulwe uma izilawuli zokufinyelela zibuthakathaka.
Ukuqalisa Umhlahlandlela
Chaza ifomethi yokuphumayo, ithoni, namazinga wekhwalithi ngaphambi kokukhishwa.
Izimpendulo eziyisisekelo ngemithombo ethembekile noma nini lapho ukunemba kubalulekile.
Gcina indawo yokuhlola isibuyekezo somuntu ukuze uthole imiphumela ephezulu.
Landela amaphethini okuhluleka futhi uqeqeshe kabusha imiyalo noma ukuhamba komsebenzi njalo.
Qhubeka Uhlole
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Umhlahlandlela olandelayo
I-Residual Vector Quantization
Imibuzo evame ukubuzwa
What is Quantization?
I-Quantization incipha imodeli ye-AI ngokugcina izinombolo zayo ngokunemba okuphansi, ngakho imodeli ebidinga i-GPU yesikhungo sedatha kwesinye isikhathi ingasebenza kukhompuyutha ephathekayo noma ifoni. Iqhinga eliyinhloko elenza amamodeli ezilimi amakhulu ashibhile futhi asheshe ngokwanele ukuze asetshenziswe kabanzi.
I-quantization ishintsha ini ngokuyinhloko ngenethiwekhi ye-neural?
Ukulinganisa kugcina izisindo zemodeli ngokunemba kwezinombolo eziphansi, njengama-integer angu-8-bit noma angu-4-bit esikhundleni sama-16- noma angu-32-bit antantayo.
Cishe ingakanani inkumbulo elondolozwa ngokuhambisa izisindo ukusuka ku-16-bit ukuya ku-4-bit?
Amabhithi angu-4 ingxenye eyodwa yesine yamabhithi angu-16, ngakho ukugcinwa kwesisindo kwehla cishe kukota, cishe ukuncishiswa okungu-4x.
Yini embi kakhulu ye-aggressive quantization?
Ukumela amanani anamazinga ambalwa kwethula iphutha lokusondeza, elingehlisa izinga lokuphumayo uma lingalawulwa ngokucophelela.
Izindlela ezifana ne-GPTQ ne-AWQ zisebenzisela ini idathasethi encane yokulinganisa?
Lezi zindlela zangemuva kokuqeqeshwa zihlaziya okokufaka kwesampula okumbalwa ukuze kusethwe izici zokukala futhi kuvikelwe izisindo ezibucayi, kugcinwe okukhiphayo kuseduze nokwangempela.
Kungani i-quantization ngokuvamile yenza imodeli isheshe, hhayi nje ibe mncane?
Amanani anemba eliphansi athatha umkhawulokudonsa wenkumbulo omncane ukuze ulayishe futhi uhanjiswe, okuvamise ukuba yibhodlela ngesikhathi sokukhiqiza, ngakho ukucabangela kuyashesha.