- Ukulungiswa kwendawo, ikakhulukazi nge-LoRA/QLoRA, kwenza kube lula ukuqokwa okusebenzayo nokuyimfihlo kwama-LLM avulekile kuhadiwe encane.
- I-RAG kanye nokulungisa kahle kuxazulula izinkinga ezahlukene: I-RAG ifaka ulwazi olusha, kuyilapho ukulungisa kahle kuhlanganisa ukuziphatha okuzinzile kanye nesitayela.
- Ama-schema asezingeni eliphezulu, iziqondiso zezichasiselo kanye nezilinganiso zokuhlola zibalulekile ekuqeqesheni amamodeli endawo athembekile aqondene nemisebenzi ethile.
- Izakhiwo ezihlanganisiwe ezihlanganisa i-RAG nokulungiswa okulula zivame ukuletha ibhalansi engcono kakhulu yokunemba, ukulawula, izindleko kanye nokugcinwa kahle.
Ukulungiswa kahle kwemodeli yolimi lwendawo kuzwakala kuyesabisa uma uvela ku-OpenAI UI elula kakhulu, lapho ulayisha khona ifayela, uchofoze inkinobho bese ulinda ukuthi kwenzeke umlingo. Kodwa uhlelo lwe-ecosystem oluzungeze ama-LLM omthombo ovulekile luthuthuke kakhulu kangangokuthi manje usungakwazi ukuphinda lolo lwazi endaweni yangakini ngenkathi ugcina ukulawula okugcwele kwedatha yakho, izindleko zakho kanye nokuziphatha kwemodeli yakho.
Uma okufunayo kuyimodeli yendawo ebhala ngendlela yomkhiqizo wakho, eqonda ulimi lwakho lwangaphakathi noma esebenza njenge-chatbot evalwe kahle phezu kwamadokhumenti akho, Ungafika lapho ngokusebenzisa inhlanganisela yamasu: ukukhuthaza okungcono, i-Retrieval-Augmented Generation (RAG) kanye, uma udinga ubuchwepheshe bangempela, ukulungisa izindlela ezifana ne-LoRA noma i-QLoRA. Isihluthulelo ukuqonda ukuthi indlela ngayinye isebenza kanjani ngempela nokuthi ihlangana kanjani emsebenzini osebenzayo.
Okushiwo ngempela ukulungisa imodeli yolimi lwendawo
Uma sikhuluma “ngokulungisa i-LLM yendawo”, asiqeqeshi imodeli kusukela ekuqaleni; Sithatha i-transformer esivele iqeqeshwe kakade, elayishwe emshinini wakho noma engqalasizinda yangasese, futhi sicindezela izisindo zayo ukuze ivumelane nesizinda sakho, isitayela kanye nemisebenzi. Ngesikhathi sokuqeqeshwa kwangaphambi kokuqeqeshwa, imodeli isivele idle inani elikhulu lombhalo ojwayelekile futhi yafunda amaphethini abanzi olimi, kodwa lolo lwazi lusakazekile futhi aluhambisani neze nezidingo zakho ezithile.
Ukulungisa kahle kusebenzisa kabusha lolu lwazi olujwayelekile futhi kwenze kube lula ngenani elincane kakhulu ledatha ekhethiwe, njengamathikithi akho okusekela, amadokhumenti angaphakathi, amalogi engxoxo noma izakhiwo ze-JSON ezichaziwe. Esikhundleni sokukhokhela amaqoqo amakhulu e-GPU kanye namasonto okuqeqeshwa kwangaphambi kokuqeqeshwa, wakha ungqimba oluncane lokwenza ngokwezifiso phezu kwemodeli eqinile yesisekelo. Leyo ngqimba eyengeziwe yanele ukuguqula uhlelo "olwaziyo konke" lube yinto eziphatha njengochwepheshe wangaphakathi.
Ngokombono webhizinisi, ukukhanga kusobala: Ugcina idatha yakho isendaweni ngezizathu zobumfihlo, unciphisa ukuncika kuma-API angaphandle, futhi ungaphoqelela ithoni noma ifomethi ehambisanayo kuzo zonke izizukulwane. Ezinhlanganweni eziningi, ukulungiswa kwendawo kuyindlela yokuthobela imithetho eqinile (cabanga ngezempilo, ezezimali noma uMthetho we-AI e-EU) ngaphandle kokuyeka amandla amamodeli amakhulu.
Kubalulekile futhi ukuhlukanisa ukuthi “kanjani” nokuthi “yini” ekwenzeni ngokwezifiso imodeli, ngoba akuwona wonke amasu ashintsha imodeli ngendlela efanayo. Ukukhuthaza kanye nokulungisa kahle kutshela imodeli ukuthi iziphathe kanjani; esikhundleni salokho i-RAG inikeza imodeli ulwazi olwengeziwe ukuze yazi ukuthi izokhuluma ngani. Empeleni, izinhlelo eziklanywe kahle zivame ukuhlanganisa zonke ezintathu.
Ukwenza ama-LLM abe ngokwakho: umongo, amapharamitha kanye nesitayela
Ukwenza imodeli yolimi ibe ngeyakho kusho ukuguqula ukuziphatha kwayo, isilulumagama kanye nolwazi lwayo lube ngokoqobo lwenhlangano yakho, kunokwamukela okuzenzakalelayo okuvamile. Lokho kungabandakanya ukuyifundisa amagama angaphakathi, ukuphoqelela ithoni ethile yezwi, noma ukubhala imithetho yebhizinisi njengokuthi “izimpendulo kumele zibe mfushane futhi kumele zicaphune umbhalo womthombo ngokwezwi nezwi”.
Izinkampani zifuna lolu hlobo lokuzivumelanisa nezimo ikakhulukazi ukuze kwandiswe ukufaneleka nokunemba, ngoba amamodeli ayisisekelo afana ne-GPT noma i-LLaMA awakaze ayibone i-CRM yakho, izinqubomgomo zakho, izincwadi zakho zomkhiqizo noma izigaba zakho zomthetho. Ngaphandle kokufinyelela kulowo mongo, ngisho ne-LLM enekhono kakhulu izobona izinto ngendlela engaqondakali noma inikeze izimpendulo ezingacacile zezinga eliphezulu ezingasizi ngalutho emisebenzini yangempela njengokusekelwa kwamakhasimende, ukuhlolwa kokuthobela imithetho noma ukusesha kwangaphakathi.
Ukwenza kube ngokwakho kudlala indima ebalulekile kumasu obumfihlo nokuphepha, njengoba unganquma ngqo ukuthi iyiphi idatha ethinta imodeli, ukuthi igcinwa kuphi, nokuthi ihlolwa kanjani. Emikhakheni enedatha ebucayi (amarekhodi emitholampilo, imisebenzi yezezimali, amadokhumenti amasu), ukugcina iziphetho kanye nokulungisa kahle ihadiwe yendawo kwenza kube lula ukuthobela izinqubomgomo zangaphakathi kanye nemithethonqubo yangaphandle.
Empeleni, kunezindlela ezintathu eziyinhloko zokwenza i-LLM ibe ngeyakho: ukufaka umongo wesikhashana (i-RAG), ukuguqula izisindo ngokulungiswa kahle nokuhlanganisa kokubili kuzilungiselelo ezihlanganisiwe. Imigomo yakho - izimpendulo ezimfushane, ukucabanga okuqondene nesizinda, isitayela somkhiqizo - inquma ukuthi iyiphi inhlanganisela enengqondo nokuthi udinga ukuya kude kangakanani ngale kokugqugquzela.
I-RAG: ukukhulisa isizukulwane ngolwazi lwangaphandle
I-Retrieval-Augmented Generation (RAG) iyindlela engcono kakhulu lapho ufuna imodeli yakho icabange ngamadokhumenti ayimfihlo noma ashintsha njalo ngaphandle kokuwaqeqesha kabusha, njenge-chatbot phezu kwamadokhumenti omkhiqizo wakho noma umsizi wangaphakathi phezu kwezinqubomgomo ze-HR. Esikhundleni sokufundisa imodeli amaqiniso amasha, uyiphakela ngokuzenzakalelayo izindima ezifanele ngesikhathi sokubuza.
Ukwakhiwa kohlelo olujwayelekile lwe-RAG kunezigaba ezintathu eziyinhloko: Okokuqala ufaka okuqukethwe kwakho ku-vector embeddings, bese uthola izingxenye ezifanele kakhulu zombuzo womsebenzisi othile, bese ekugcineni ucela i-LLM ukuthi ikhiqize impendulo ngokusekelwe kuphela kulezo zingxenye. Imodeli eyisisekelo ayithintwanga; kuphela ipayipi lokubuyisa kanye nesitolo semibhalo eshintshayo njengoba isisekelo sakho solwazi sishintsha.
Lokhu kuletha izinzuzo eziningana kuzilungiselelo zebhizinisi: Ulwazi lungabuyekezwa ngokushesha ngokuphinda ubhale amadokhumenti, izindleko zokusebenza ziphansi kunokulungisa okuqhubekayo, futhi kulula ukuhlola ukuthi yimuphi umbhalo osekela impendulo enikeziwe. Ngenxa yokuthi imodeli ayilokothi imunce idatha yangasese unomphela, imodeli yokuphepha ilula futhi isobala kakhudlwana.
Uhlangothi oluphambene luwukuthi i-RAG iyaphila futhi iyafa ngenxa yekhwalithi yesendlalelo sakho sokubuyisa, kufaka phakathi isu lokuqoqa, imodeli yokushumeka, izihlungi kanye nokubeka ezingeni. Uma uhlelo luhluleka ukuveza izindima ezifanele, i-LLM izobona izinto ngendlela engaqondakali noma iphendule ngokwethembeka ukuthi ayikwazi ukuthola impendulo kumongo onikeziwe, noma ngabe ulwazi lukhona endaweni ethile ku-corpus yakho.
Ukulungisa kahle: ukulungisa amapharamitha emodeli
Ukulungisa kahle kumayelana nokushintsha izisindo zangaphakathi zemodeli ngokwayo zibe ukuziphatha kwekhodi eqinile, esikhundleni sokuthembela kuphela ezikhuthazweni ezihlakaniphile noma kumongo wangaphandle. Ngokulungisa kahle ungafundisa imodeli ukulandela amafomethi okukhipha aqinile, wamukele isitayela esithile sombhalo, noma uthuthukise ukucabanga kwayo ezizindeni ezichazwe kahle.
Kunezinhlobo eziningana zokulungisa kahle kuye ngokuthi ufuna ukuhlasela kangakanani nokuthi unezibalo ezingakanani: ukuhleleka okuphelele, lapho zonke izendlalelo zibuyekezwa khona; ukuhleleka okuncane, lapho kuqeqeshwa khona izendlalelo eziphakeme kuphela; kanye nezindlela zesitayela se-adaptha noma se-LoRA, lapho ufaka khona amamojula amancane aqeqeshwayo phezu komgogodla oqandisiwe. Kumasethingi amaningi endawo, iqembu lokugcina yilona elisebenziseka kakhulu.
Ukulungiswa okuphelele kwendabuko kunikeza ukuguquguquka okuphezulu kodwa ngokuvamile kudlula kakhulu ukusetshenziswa kwendawo, njengoba idinga ama-GPU amaningi aphezulu, amasethi edatha amakhulu anelebula kanye nokuhlelwa kabusha ngokucophelela ukuze kugwenywe overfitting vs underfittingUphinde ugcine unemodeli enzima, eqondene nomsebenzi onzima ukwabelana ngayo, inguqulo kanye nokubuyisela emuva.
Izindlela ezisekelwe ku-adaptha njenge-LoRA ne-QLoRA ziguqula lokhu kuhwebelana ngokuqandisa izisindo zokuqala futhi kufundwe kuphela "i-delta" encane efaka ikhodi yezinguquko ezithile zomsebenzi. Leli sethi elincane lamapharamitha engeziwe lingalayishwa futhi lilayishwe uma kudingeka, okukuvumela ukuthi uguqule imodeli eyodwa eyisisekelo ibe izinhlobo eziningi ezikhethekile ngaphandle kokuphinda yonke indawo yokuhlola imodeli.
I-LoRA, i-QLoRA kanye nokulungiswa kwendawo okuphumelelayo
I-Low-Rank Adaptation (LoRA) ingenye yezindlela ezibalulekile ezenza ukulungiswa kwendawo kube nokwenzeka kwihadiwe yezimpahla, ngoba kunciphisa kakhulu inani lamapharamitha angaqeqeshwa ngenkathi kulondolozwa ukusebenza. Esikhundleni sokushintsha ngqo i-matrix enkulu yesisindo, i-LoRA ilinganisela isibuyekezo njengomkhiqizo wama-matrices amabili amancane kakhulu, okumelela ngempumelelo ukuguqulwa kwezinga eliphansi.
Izisindo zokuqala eziqeqeshwe kusengaphambili zihlala ziqinile, futhi lokho okulungisayo empeleni yizisindo ezibizwa ngokuthi i-delta, umehluko phakathi kwemodeli eyisisekelo kanye nokuziphatha okuguquliwe okufunayo. Ngesikhathi sokuphetha, lawa ma-delta afakwa ezingqimbeni ezifanele, ngakho-ke izisindo ezisebenzayo ziba “ukulungiswa kwesisekelo + umsebenzi othile”, kodwa ungasusa kalula noma ushintshe lawo ma-tweak noma nini lapho kudingeka.
Lokhu kunemiphumela emibili esebenzayo emisebenzini yendawo: Okokuqala, ukulungisa kahle kuba ngokushesha futhi kube lula kakhulu kwimemori, kuze kufike lapho ungakwazi khona ukuzivumelanisa namamodeli epharamitha ayizigidigidi ku-GPU eyodwa yesimanje noma ngisho nakwihadiwe yabathengi ephezulu; okwesibili, ungagcina umtapo wolwazi wama-adaptha e-LoRA wemisebenzi ehlukene (ukubhala kwezomthetho, ukwesekwa kwamakhasimende, imibhalo yobuchwepheshe) bese ushintsha phakathi kwawo ngezindleko ezincane.
I-QLoRA iqhubekisela phambili lo mbono ngokulinganisa imodeli eyisisekelo iye ekuqondeni okuphansi ngaphambi kokuqeqeshwa, kunciphisa izidingo ze-VRAM nakakhulu. Usaqeqesha ama-adaptha e-LoRA phezulu, kodwa umgogodla ongaphansi uyacindezelwa. Kumaqembu azama amamodeli afana ne-Mixtral‑8x22B, i-Mistral‑7B noma i-BLOOM‑7B ngokuphelele endaweni, i-QLoRA ingaba umehluko phakathi "kokulingana emshinini" kanye "nokungenzeki nhlobo".
I-RAG vs ukulungiswa kahle: lapho ngayinye ikhanya
Kokubili i-RAG kanye nokulungisa kahle kuyizindlela zokwenza imodeli ibe ngeyakho, kodwa isebenza ngezendlalelo ezahlukene zesitaki, ngakho-ke ukukhetha phakathi kwazo (noma ukunquma ukuthi ungazihlanganisa kanjani) kuncike kulokho okulungiselelayo: ulwazi oluguquguqukayo, ukulawula isitayela, ukuchaza, izindleko noma izindleko zokulungisa.
I-RAG ingcono kakhulu uma ulwazi lwakho lushintsha njalo noma kufanele lulandeleke ngokugcwele, njengemithethonqubo yezomthetho, amakhathalogi omkhiqizo noma amadokhumenti obuchwepheshe avuselelwa njalo. Ugcina imodeli ijwayelekile futhi ufaka umongo omusha, ohloliwe othathwe esitolo se-vector. Ukubuyekeza okuqukethwe kwakho kulula njengokuphinda ubhale amadokhumenti amasha, akudingeki ukuqeqeshwa kabusha.
Ukulungisa kahle kuyakhanya uma udinga ulwazi olujulile, oluzinzile kanye nokuziphatha okuvumelanayo, isibonelo ukuphoqelela i-schema eqinile ye-JSON, ukuphinda isitayela esithile sokubhala, noma ukuqonda isizinda esikhethekile kakhulu lapho imininingwane emincane ibaluleke khona ngempela. Uma imodeli isiyifakile ngaphakathi le ndlela yokuziphatha, awunciki ezicelweni ezinde noma emiyalweni ebuthakathaka ukuze uthole umphumela ofanele.
Ngokombono wokusebenza, i-RAG ivame ukuba ishibhile futhi kube lula ukuyinakekela, njengoba uphatha kakhulu ipayipi ledokhumenti kanye nenkomba yokushumeka. Ngakolunye uhlangothi, ukulungisa kahle kudinga idatha yokuqeqesha eqinile, ukubala izinsiza, ukuqapha ukuzulazula kanye nokuqeqeshwa kabusha okungenzeka ngezikhathi ezithile njengoba isizinda sakho sithuthuka.
Amaphrofayili okuphepha kanye nobandlululo nawo ayahluka: I-RAG igcina imodeli eyisisekelo ingashintshi, ukuze ungashintshi ukucwasa kwayo okungokwemvelo kodwa futhi awuhlanganisi unomphela idatha eyimfihlo. Ukulungisa kahle kuveza imodeli ngqo kumasethi akho edatha, okunamandla kodwa kudinga ukuphathwa kwedatha okuqinile ukuze kugwenywe ukufaka ukucwasa, amaphutha noma ulwazi olubucayi ezisindweni.
Amasu e-hybrid: ukuxuba i-RAG kanye nokulungisa kahle
Kumaphrojekthi amaningi angempela, iresiphi ephumelelayo iwukusethwa okuhlanganisiwe okuhlanganisa i-RAG yolwazi oluphilayo nokulungiswa okulula kwesitayela kanye nenqubo, okukuvumela ukuthi ugcine umongo usesikhathini ngenkathi imodeli ifunda ukuphendula ngezwi eliqondile kanye nefomethi oyidingayo.
Cabanga ngomsizi wamadokhumenti wangaphakathi njengesibonelo esiqondile: I-RAG iphatha ukutholwa kwezincwadi, izinqubomgomo kanye nama-wiki, iqinisekisa ukuthi okuqukethwe kusesikhathini futhi kuyalandeleka; i-LoRA encane ifundisa imodeli ukugwema inkulumo encane enenhlonipho, iphendule ngokufushane, futhi njalo icaphune umusho oqondile kumongo osekela isimangalo. Umphumela uba ithuluzi eligxile, elithembekile esikhundleni se-bot evamile exoxayo.
Izindlela ezihlanganisiwe nazo zivamile lapho kwakhiwe izixhumi zemvelo zolimi kuzinhlelo zokusebenza, njengezinhlelo zokusebenza zeselula eziqhutshwa yizwi eziguqula imiyalo ekhulunywayo ibe yizenzo ezihlelekile. Ungasebenzisa ukuqalisa kodwa ukuhlukanisa imiyalelo eyinkimbinkimbi ibe yizinyathelo ze-athomu, kuyilapho uthembele ekuhleleni kahle ukuze uhlele kahle umyalo ngamunye ube yi-schema ye-JSON engasebenza ngemuva kwakho.
Ukuze lokhu kusebenze, ukwakhiwa kwezakhiwo kubalulekile: ukugcina ukubuyisa, ukuqonda imodeli kanye nokucubungula ngemuva kwe-modular kukuvumela ukuthi uphinde uhlaziye ingxenye ngayinye ngokuzimela. Ungalungisa i-index, ubuyekeze ama-adaptha e-LoRA, noma ushintshe imithetho yokuqinisekisa ngaphandle kokudiliza lonke uhlelo, okubaluleke kakhulu njengoba ukusetshenziswa kwangempela kwembula amacala onqenqema obungawalindelanga.
Ukuhlola ukulungiswa kwendawo ngokusebenzisa i-RAG chatbot
Indlela enhle yokubona umthelela wokulungisa kahle ekusebenzeni ukubheka i-RAG chatbot eyakhelwe phezu kwesethi yamadokhumenti aqinile, lapho umgomo kungekona nje ukuphendula ngendlela efanele kodwa ukwenza kanjalo ngefomethi emfushane nejwayelekile abasebenzisi abayithola kulula ukuyisebenzisa.
Cabanga nje unezingxoxo eziningi ezingamakhulu ambalwa, ngayinye inezibhangqwana eziningana zemibuzo nezimpendulo, Kuhlelwe futhi kwahlolwa ochwepheshe bezilimi zokubala noma ochwepheshe besizinda. Uhlukanisa le sethi yedatha ibe yingxenye yokuqeqesha yokulungisa kahle kanye nengxenye yokuhlola ukuhlola ukuthi uhlelo luhlanganisa kahle kangakanani. Izimpendulo zinikezwa amaphuzu kusukela ku-1 kuya ku-5 ngokwezilinganiso ezifana nokufaneleka, isisekelo somongo kanye nokungabikho kwemibono engaqondakali.
Uma uxhuma lokhu kusetha kumodeli ye-API engekho eshelufini njenge-GPT 3.5 ngaphandle kokulungiswa kahle, Ungase uthole isilinganiso esihle samaphuzu – ake sithi cishe u-3.6 kwabayi-5 – kodwa ngokuziphatha okucasulayo: izitatimende zokuzikhulumela ezinjengokuthi “Ngokomongo onikeziwe...” kuzo zonke izimpendulo, ukuxolisa ngokweqile, noma izimangalo zokuthi ulwazi oluceliwe alukho kumongo ngisho noma lukhona ngempela.
Manje thatha imodeli yomthombo ovulekile njenge-StableLM 12B, uyilungise endaweni yakho kusigaba sokuqeqesha bese uyihlola kusethi efanayo yokuhlola, ukuyivumelanisa ngqo nomsebenzi wokukhipha izimpendulo ezimfushane nezinembile kumongo otholiwe. Ezivivinyweni zalolu hlobo, imodeli yendawo elungisiwe kahle ingadlula i-API ejwayelekile ngamaphuzu aphelele, ifinyelele amaphuzu angaphezu kuka-4.5 kwangu-5.
Umehluko wekhwalithi ubaluleke njengezilinganiso: Imodeli ehlelwe kahle ihlanganisa imisho embalwa engadingeki, ixolisa kancane uma ulwazi lungekho futhi iyakwazi ukuthola ingxenye efanele kumongo. Ngamanye amazwi, ayigcini nje “ngokwazi” okwengeziwe ngomsebenzi wakho, kodwa futhi ifunde isitayela sakho sempendulo osithandayo.
Idatha, izichasiselo kanye nesistimu yokulungisa kahle
Ngemuva kwakho konke ukulungiswa okuphumelelayo kukhona uhlelo lwedatha oluklanywe ngokucophelela, ngoba imodeli ingafunda kuphela amaphethini abonakala njalo ezibonelweni oziphakelayo. Emisebenzini ehlelekile, lokho kusho ukuthi imisho ihambisana nezichasiselo eziqondile ezihambisana nalokho okulindelwe yi-backend yakho.
Ibhloko lokuqala lokwakha liwuhlelo olucacile lokumelela, ukuchaza izinhloso, amapharamitha kanye nendlela ezihambisana ngayo nezinhlaka ezihlelekile. Kumsizi wekhalenda, ungase ucacise izimfanelo ezifana nomhleli, ababekhona, isikhathi sokuqala, ubude besikhathi, indawo noma isihloko, ngasinye sine-sub-schema yaso (isibonelo, ukuthi yini eyakha into yomsebenzisi evumelekile: igama, i-imeyili, inhlangano, njalo njalo).
Okulandelayo udinga iziqondiso zezichasiselo ezigcina amalebula abantu ehambisana, ukupela, isibonelo, ukuthi kufanele uqambe nini isikhulumi njengomhleli womcimbi, ukuthi ungaphatha kanjani izindima ezingacacile, noma ukuthi ungaphatha kanjani imisho engacacile. Lezi ziqondiso zingaxuba izindlela zolimi nolwazi lwesizinda futhi zibalulekile ukugwema amalebula anomsindo, aphikisanayo angadida imodeli.
Ithuluzi lesichasiselo elenzelwe i-schema yakho livala iluphu, ngokufanele ukuhlinzeka ngokuhlola okuzenzakalelayo kokufaneleka kwesakhiwo kanye nokuvumelana kwencazelo. Amanye amathuluzi angaphakathi afaka ngisho nemithetho yokuqinisekisa efana nokuthi “yonke inhloso yomcimbi kumele ibe nomhleli oyedwa wohlobo oluthile”, ibambe amaphutha kusenesikhathi esikhundleni sokuthola ukungahambisani kuphela ngemva kokuqeqeshwa.
Uma lokhu kuhlanganiswa, ukulungisa kahle kuba yinto ebalulekile kunokuba kube iskripthi esisodwa: ukubambisana nababambiqhaza besizinda ukuchaza i-schema, abachazi bochwepheshe ukukhiqiza nokubuyekeza izibonelo, kanye nengqalasizinda yokuqinisekisa, ukuhumusha nokuqapha amasethi edatha ngokuhamba kwesikhathi. Kudinga kakhulu kunokusikisela okulula, kodwa yilo kanye lolu qina oluvumela amamodeli endawo aqinile, asezingeni lokukhiqiza.
Ukuqala ngokulungiswa kwendawo okunobungane nabaqalayo
Uma okuwukuphela kwento oyenze ngaphambili kuyi-UI yokulungisa kahle i-OpenAI, indawo yangakini ingazwakala ingcolile ekuqaleni, kodwa izindaba ezinhle ukuthi amathuluzi esimanje anciphise kakhulu isithiyo. Akusadingeki ukuthi ubhale izihibe zokuqeqesha ezingavuthiwe ku-PyTorch ukuze uvumelanise imodeli nesitayela sakho.
Amamodeli adumile avulekile njengeMistral‑7B, Mixtral‑8x22B, StableLM noma i-BLOOM‑7B manje aseza nezindlela zokupheka ezilungisiwe, kufaka phakathi amathempulethi okucushwa kwe-LoRA noma i-QLoRA kanye nokuhlanganiswa namalayibrari afana ne-Hugging Face Transformers kanye ne-PEFT. Amaphrojekthi amaningi omphakathi ahlanganisa lokhu kube amathuluzi alula omugqa wemiyalo noma izixhumi ezibonakalayo lapho ukhomba khona isethi yedatha yakho, ukhethe ukucushwa kwe-adaptha bese uqala ukuqeqeshwa.
Ukuhamba komsebenzi okusezingeni eliphezulu kubonisa lokho okwenzile nge-OpenAI: lungisa ifayela lakho lokuqeqesha (ngokuvamile i-JSONL enama-pair okufaka-okukhiphayo), cacisa ukuthi ufuna ukuhleleka kahle kwemiyalelo noma ukulingisa isitayela, khetha imodeli eyisisekelo efanela ihadiwe yakho, bese usebenzisa iskripthi eqalisa ukuqeqeshwa kwe-adaptha. Uma usuqedile, ulayisha imodeli eyisisekelo kanye ne-adaptha eqeqeshiwe futhi unemodeli yakho yendawo "elungisiwe kahle" elungele ukuqagela.
I-Python isalokhu iwulimi olunamathelayo lwamathuluzi amaningi, ukuhlela ukucubungula idatha kusengaphambili, ukuqala ukuqeqeshwa, ukuhlanganisa izitolo ze-vector ze-RAG, nokwakha ama-API alula azungeze imodeli yakho eguquliwe. Ngolwazi olujwayelekile lwesayensi yedatha ungalandela izifundo zesinyathelo ngesinyathelo bese uguqukela ohlelweni oluziphatha ngendlela ecishe ifane nalokho okujwayele kubahlinzeki ababanjwe - manje selusebenza ngaphansi kolawulo lwakho.
Njengoba lawa masu ethuthuka, sibona ukusethwa okuyinkimbinkimbi lapho ama-ejenti ephatha khona izihibe zawo zokuthuthukisa, ukuthola umongo omusha nge-RAG, ukuhlela ukuhleleka okulula lapho kuvela amaphethini azinzile, kanye nokuqala ukuqopha kabusha noma ukubuyekezwa kwabantu lapho kutholwa izinto ezingavamile. Indlela yokuhamba icacile: ama-LLM enziwe ngokwezifiso, alawulwa endaweni aqhubeka nokuzivumelanisa nezimo ngenkathi ehlala ehlolwa futhi ehambisana nemigomo yenhlangano yakho.
Konke lokhu kusho ukuthi ukwakha imodeli yolimi yendawo, ehlelwe kahle efanelana nesitayela sakho kanye nesizinda osithandayo akuseyona into yokunethezeka yocwaningo kuphela; ngama-LLM avulekile, amasu asebenzayo njenge-LoRA ne-QLoRA, imikhuba yedatha eqinile kanye nokwakhiwa kwe-RAG okuhlanganisiwe, amaqembu osayizi abahlukene kakhulu angathumela abasizi abazimele, abakhethekile abadlula ama-API ajwayelekile emisebenzini yabo yangempela ngenkathi begcina idatha, ukuhambisana nomthetho kanye nokuvela kwesikhathi eside ezandleni zabo.
