Uhlolojikelele
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 iyingxenye yesitaki solimi-AI esisetshenziselwa ukufunda, ukukhiqiza, ukuhlukanisa, nokuguqula umbhalo nenkulumo ngezikali.
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-Quantization ye-Mastering
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 iyingxenye yesitaki solimi-AI esisetshenziselwa ukufunda, ukukhiqiza, ukuhlukanisa, nokuguqula umbhalo nenkulumo ngezikali. Ukuze wakhe ukuqonda okujulile, phatha i-Quantization njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho isistimu engakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa ukwaziswa kwe-Quantization design, ukubuyisa, nokubuyekeza ama-loops njengohlelo olulodwa lokuxhumana oludidiyelwe. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana. Ngesikhathi esifanayo, amaqiniso Akhohliwe angafaka imibiko buthule, ukugeleza kosekelo, noma imiphumela yocwaningo. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana.
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Yandisa ukufinyelela kuzo zonke izilimi nezitayela zokuxhumana.
Yandisa ukufinyelela kuzo zonke izilimi nezitayela zokuxhumana. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Amaqembu angachitha isikhathi esiningi ekwahluleleni kuyilapho i-automation isingatha impinda.
Amaqembu angachitha isikhathi esiningi ekwahluleleni kuyilapho i-automation isingatha impinda. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
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.
Amaphethini Okusebenzisa
Quantization ngokusebenza
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.
Ukusebenzisa imodeli yengxoxo efana ne-Llama endaweni ku-GPU yomthengi usebenzisa ama-4-bit GGUF noma amafayela e-GPTQ esikhundleni sokudinga amakhadi amaningi anesikhungo sedatha Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka yomuntu yamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Quantization ngokusebenza
Izisizi ezikudivayisi kumafoni, lapho amamodeli angu-8-bit noma angu-4-bit avumela izici zenkulumo nezombhalo zisebenze ngaphandle koxhumano lwenethiwekhi.
Izisizi ezikudivayisi kumafoni, lapho amamodeli angu-8-bit noma angu-4-bit avumela izici zenkulumo nezombhalo zisebenze ngaphandle koxhumano lwenethiwekhi Amaqembu ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, agcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Quantization ngokusebenza
Ukunciphisa izindleko ze-cloud ze-bot yokwesekwa kwamakhasimende ngokunikeza imodeli ye-INT8, ukufaka izicelo eziningi ku-GPU ngayinye.
Ukunciphisa izindleko ze-cloud ze-bot yokwesekwa kwamakhasimende ngokunikeza imodeli ye-INT8, ukufaka izicelo ezengeziwe kuQembu ngalinye le-GPU ngokuvamile kuthola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, gcina indlela yokukhuphuka yomuntu yamacala asemaphethelweni, futhi ulandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Quantization ngokusebenza
Amadivayisi e-Edge anjengamakhamera ahlakaniphile noma izinzwa ze-IoT asebenzisa amamodeli olimi okubona alinganiselwe ngaphakathi kwemikhawulo eqinile yenkumbulo.
Amadivayisi e-Edge anjengamakhamera ahlakaniphile noma izinzwa ze-IoT asebenzisa amamodeli olimi ombono alinganiselwe ngaphakathi kwemikhawulo eqinile yenkumbulo Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
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.
Chaza ifomethi yokuphumayo, ithoni, namazinga wekhwalithi ngaphambi kokukhishwa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Izimpendulo eziyisisekelo ngemithombo ethembekile noma nini lapho ukunemba kubalulekile.
Izimpendulo eziyisisekelo ngemithombo ethembekile noma nini lapho ukunemba kubalulekile. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gcina indawo yokuhlola isibuyekezo somuntu ukuze uthole imiphumela ephezulu.
Gcina indawo yokuhlola isibuyekezo somuntu ukuze uthole imiphumela ephezulu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Landela amaphethini okuhluleka futhi uqeqeshe kabusha imiyalo noma ukuhamba komsebenzi njalo.
Landela amaphethini okuhluleka futhi uqeqeshe kabusha imiyalo noma ukuhamba komsebenzi njalo. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.