Amamodeli amakhulu olimi: Umgwaqo wokuzihlola

Amamodeli amakhulu olimi ayinyathelo elikhulu phambili kubuhlakani bokufakelwa. Bangabikezela futhi bakhiqize umbhalo ozwakala sengathi wabhalwa ngumuntu. I-LLMS Funda imithetho yolimi, efana nohlelo lolimi nencazelo, ebavumela ukuba benze imisebenzi eminingi. Bangakwazi ukuphendula imibuzo, bafingqe imibhalo emide, bakha izindaba. Isidingo esikhulayo sokuqukethwe okwenziwe ngokuzenzakalelayo futhi okuhleliwe ukushayela ukunwetshwa kwemakethe enkulu yezilimi. Ngokombiko owodwa, Imodeli enkulu yolimi imodeli (LLM) usayizi wemakethe & isibikezelo:

“Imakethe ye-Global LLM njengamanje ibona ukukhula okunamandla, okulinganiselwa kukhombisa ukwanda okukhulu kosayizi wemakethe. Ukuqagelwa kwezigidigidi ezingama-2024 kuya ku-203.2% esikhathini sokubikezela”

Lokhu kusho ukuthi i-2025 ingaba unyaka omuhle kakhulu wokuqala ukufunda i-LLMS. Ukufunda imiqondo esezingeni eliphezulu ye-LLMS kufaka phakathi indlela ehlelekile, ezayo efaka imiqondo, amamodeli, ukuqeqeshwa kanye nokwenza kahle kanye nokuthunyelwa kanye nezindlela ezithuthukisiwe zokubuyisa. Le mampasi yomgwaqo iveza indlela yesinyathelo ngesinyathelo sokuthola ubungcweti kuma-llms. Ngakho-ke, ake siqale.

Isinyathelo 1: Mboza izisekelo

Ungeqa lesi sinyathelo uma usuvele uyazi izisekelo zohlelo, ukufunda ngomshini, kanye nokucutshungulwa kolimi lwemvelo. Kodwa-ke, uma umusha kule miqondo cabanga ukuthi uwafunde kulezi zinsizakusebenza ezilandelayo:

• Uhlelo: Udinga ukufunda izisekelo zezinhlelo kuPython, ulimi oluhle kakhulu lwezinhlelo zokufunda ngomshini. Lezi zinsiza zingakusiza ufunde i-Python:
• Ukufundwa Komshini: Ngemuva kokufunda uhlelo, kufanele uhlanganise imiqondo eyisisekelo yokufunda komshini ngaphambi kokuqhubekela phambili ne-LLMS. Isihluthulelo lapha ukugxila emibonweni efana ne-Divid VS. Ukufundwa okungasekelwe, ukubuyiselwa emuva, ukuhlukaniswa, ukuhlanganisa, kanye nokuhlolwa kwemodeli. Inkambo enhle kakhulu ebengithola ngifunde izisekelo ze-ML yile:
• Ukucutshungulwa kolimi lwemvelo: Kubaluleke kakhulu ukuba ufunde izihloko eziyisisekelo ze-NLP uma ufuna ukufunda i-LLMS. Gxila emikhondweni esemqoka: UTokenization, ukushumeka kwamagama, izindlela zokunakwa, njll. Nginikeze izinsiza ezimbalwa ezingakusiza ufunde i-NLP:

Isinyathelo 2: Qonda izakhiwo ezibalulekile zezakhiwo ngemuva kwamamodeli wezilimi ezinkulu

Amamodeli amakhulu wezilimi ancike ekwakheni okuhlukahlukene, nabaguquli baba yisisekelo esivelele kunazo zonke. Ukuqonda lezi zindlela ezahlukahlukene zokwakha kubalulekile ukuze usebenze ngempumelelo ne-LLMS yesimanje. Nazi izihloko ezibalulekile nezinsizakusebenza zokuthuthukisa ukuqonda kwakho:

• Qonda ukwakhiwa kwezakhiwo ze-Transformer futhi ugcizelele ekuziboneni kokuziqonda, ukunakwa kwekhanda okuningi, kanye nokufakwa encebweni ngemuva.
• Qala nge Ukunakwa konke okudingayobese uhlola ukwahlukahluka kwezakhiwo ezahlukahlukene: amamodeli we-decoder-kuphela (uchungechunge lwe-GPT kuphela), amamodeli we-encoder-kuphela), namamodeli we-encoder-decoder (T5, BART).
• Sebenzisa imitapo yolwazi efana nokuqabula i-Transformers yobuso yobuso ukufinyelela nokusebenzisa ukwakheka okuhlukahlukene kwemodeli.
• Prakthiza ukwakhiwa okuhlukile okuhlukile kwemisebenzi ethile efana nokuhlukaniswa, ukukhiqizwa, kanye nokufingqa.

Izinsizakusebenza Zokufunda Ezinconyiwe

Isinyathelo 3: Ukugxila kumamodeli amakhulu olimi

Ngezisekelo ezisekelweni, sekuyisikhathi sokugxila ngqo kuma-LLMS. Lezi zifundo zenzelwe ukujulisa ukuqonda kwakho ngokwakhiwa kwazo, imiphumela yokuziphatha, kanye nezicelo zomhlaba wangempela:

• I-LLM University – Core (Kunconyiwe): Inikeza zombili ithrekhi elandelanayo yabasanda kufika kanye nendlela engenalo ukulandelana, okuqhutshwa ngohlelo lokusebenza kwabachwepheshe abanolwazi. Inika ukuhlola okuhlelekile kokubili izici zethiyori nezisebenzayo ze-LLMS.
• UStanford CS324: Amamodeli amakhulu olimi (anconyiwe): Inkambo ephelele ehlola umbono, izimiso zokuziphatha kanye nokusebenza kwe-LLMS. Uzofunda ukuthi ungakha futhi uhlole i-LLMS.
• Umhlahlandlela weLaxime Labonne (Kunconyiwe): Lo mhlahlandlela uhlinzeka ngomgwaqo ocacile wezindlela ezimbili zomsebenzi: I-LLM Scientist kanye no-LLM Unjiniyela. Indlela yososayensi ye-LLM yenzelwe labo abafuna ukwakha amamodeli wezilimi athuthukile asebenzisa amasu wakamuva. Indlela ye-LLM Engineer igxile ekudaleni nasekusetshenzisweni kwezicelo ezisebenzisa i-LLMS. Kubandakanya ne-Handbook ye-LLM gener's, okuthatha wena ngesinyathelo ngesinyathelo kusuka ekuklameni ukwethula izinhlelo zokusebenza ezenzelwe i-LLM.
• IPrinceton Cos597G: Ukuqonda amamodeli amakhulu olimi: inkambo yeziqu ezithwebule iziqu ezihlanganisa amamodeli afana ne-bert, GPT, T5, nokuningi. Kuhle kulabo abahlose ukuzibandakanya ocwaningweni olujulile lwezobuchwepheshe, le khosi ihlola womabili amandla nemikhawulo ye-LLMS.
• Amamodeli amahle e-LLM – Amamodeli we-LLM amnandi – Inkambo ye-AI ekhiqizayo lapho usebenza nama-LLMS, ngokuvamile uzodinga amasu we-LLMS amahle, ngakho-ke cabanga ngokufunda amasu afanele ahleni amahle anjengeLora ne-Qlora, kanye namasu wokulinganisa amamodeli. Lezi zindlela zingasiza ekunciphiseni usayizi wemodeli kanye nezidingo ze-computational ngenkathi kugcinwa ukusebenza. Le khosi izokufundisa ukuhlelela okuhle usebenzisa i-Qlora neLora, kanye nenani elisebenzisa i-LLAMA2, i-gradient, kanye nemodeli ye-google Gemma.
• I-Fifetune LLMS Ukubafundisa noma yini nge-huggapp ne-Pytorch | Isifundo se-Step-by-step: Inikeza umhlahlandlela ophelele we-LLMS elungiselelwe kahle usebenzisa ukwanga kobuso kanye ne-Pytorch. Ihlanganisa yonke inqubo, kusuka ekulungiseleleni kwedatha kumodeli ukuqeqeshwa kanye nokuhlola, okwenza ababukeli basebenzise ama-LLMS ngemisebenzi ethile noma izizinda.

Isinyathelo 4: Yakha, Suka futhi usebenzise izinhlelo zokusebenza ze-LLM

Ukufunda umqondo ngokomqondo kuyinto eyodwa; Kuyisebenzisa cishe kungenye. Okwedlule kuqinisa ukuqonda kwakho ngemibono eyisisekelo, kuyilapho okugcina kukuvumela ukuthi uhumushe leyo mibono ibe izixazululo zangempela zomhlaba. Lesi sigaba sigxile ekuhlanganiseni amamodeli amakhulu olimi ngamaphrojekthi asebenzisa izinhlaka ezidumile, ama-API, kanye nemikhuba emihle yokuthumela kanye nokuphatha i-LLMS ekukhiqizeni nasezindaweni zasekhaya. Ngokusebenzisa kahle la mathuluzi, uzokwakha kahle izinhlelo zokusebenza, ukuthunyelwa kwamanani, kanye nokusebenzisa amasu we-LLMOPS wokuqapha, ukusebenza kahle, kanye nokunakekelwa.

• Ukuthuthukiswa kwesicelo: Funda ukuthi ungahlanganisa kanjani i-LLMS kwizicelo noma izinsizakalo ezibhekene nomsebenzisi.
• ILangchain: I-Langchain uhlaka olusheshayo nolusebenzayo lwamaphrojekthi we-LLM. Funda ukuthi ungazakha kanjani izinhlelo usebenzisa iLangchain.
• Ukuhlanganiswa kwe-API: Hlola ukuthi ungaxhuma kanjani ama-API ahlukahlukene, njengama-Opelai's, ukwengeza izici ezithuthukile kumaphrojekthi akho.
• Ukuhanjiswa kwe-LLM yendawo: Funda ukusetha nokusebenzisa i-LLMS emshinini wangakini.
• Imikhuba ye-LLMOPS: Funda izindlela zokuthumela, ukuqapha, kanye nokugcina ama-llms ezindaweni zokukhiqiza.

Kunconywe izinsizakusebenza zokufunda namaphrojekthi

Ukwakha Izicelo ze-LLM:

Ukuhanjiswa kwe-LLM yendawo:

Ukuhambisa & Ukulawula izinhlelo zokusebenza ze-LLM ezindaweni zokukhiqiza:

I-GitHub Repositories:

• Awesome-LLM: Iqoqo elikhethiwe lamaphepha, izinhlaka, amathuluzi, izifundo, tutorials, kanye nezinsizakusebenza ezigxile kumamodeli amakhulu wezilimi (LLMS), ngokugcizelelwa okukhethekile ku-Chatgpt.
• I-Awesome-Langchain: Le repository yi-Hub ukulandelela amasu namaphrojekthi ahlobene nemvelo kaLangchain.

Isinyathelo 5: Rag & Vector databases

I-Retrieval-Augmented Generation (Rag) iyindlela eyi-hybrid ehlanganisa ukubuyiselwa kwemininingwane ngesizukulwane sombhalo. Esikhundleni sokuncika kuphela olwazini oluqeqeshwe ngaphambili, i-rag ibuyisa amadokhumenti afanele kusuka emithonjeni yangaphandle ngaphambi kokukhiqiza izimpendulo. Lokhu kuthuthukisa ukunemba, kunciphisa ama-hallucinations, futhi kwenze amamodeli awusizo kakhulu emisebenzini ebanzi.

• Qonda i-rag nezakhiwo zayo: I-Rag ejwayelekile, i-rag ye-hierarchical, i-hybrid rag njll.
• Imininingwane ye-Vector: Qondisisa ukuthi ungayisebenzisa kanjani imininingwane ye-vector nge-rag. Isitolo se-Vector Databases futhi sibuyise imininingwane ngokususelwa ku-semantic ven in devint than that igama elingukhiye elingukhiye. Lokhu kubenza balungele izinhlelo zokusebenza ezisuselwa ku-rag njengoba lokhu kuvumela ukubuyisa okusheshayo nokusebenzayo kwemibhalo efanele.
• Amasu wokubuyisa: Sebenzisa ukubuyisa okuminyene, ukubuyisa okubuyisanayo, kanye nokufuna kwe-hybrid ukufana okungcono kwedokhumenti.
• ILamaindex neLangchain: Funda ngokuthi lezi zinhlaka zisiza kanjani i-rag.
• Ukukala i-rag yezinhlelo zokusebenza zebhizinisi: Qondisisa ukubuyisa okusatshalaliswa, ukulondolozwa kwesikhashana, kanye nokusebenza kahle kwe-latency kokuphatha ukubuyisa amadokhumenti amakhulu.

Kunconywe izinsizakusebenza zokufunda namaphrojekthi

Izifundo eziyisisekelo eziyisisekelo:

I-Advanced Rag yokwakha nokusetshenziswa:

I-Enterprise-grade rag nokulinganisa:

Isinyathelo 6: Lungiselela i-LLM Indence

Ukwenza kahle ukutholwa kubalulekile ukwenza izinhlelo zokusebenza ezinamandla ze-LLM zisebenze kahle, zibiza kakhulu, futhi zikhulu. Lesi sinyathelo sigxile kumasu wokunciphisa i-latency, ngcono izikhathi zokuphendula, futhi unciphise ngaphezulu kwe-ophead yekhompyutha.

Izihloko ezibalulekile

• I-Model Night: Yehlisa usayizi wemodeli futhi uthuthukise i-Speed ​​usebenzisa amasu anjenge-8-bit kanye ne-4-bit quant (isib., I-GPTQ, AWQ).
• Ukusebenza kahle: Thumela amamodeli kahle ngezinhlaka ezinjenge-VLLM, i-TGI (I-Text Generation Indepered), kanye ne-DeepSpeed.
• Lora & Qlora: Sebenzisa izindlela ze-parameter-tuning ezinhle zokuthuthukisa ukusebenza kwemodeli ngaphandle kwezindleko zomthombo eziphezulu.
• Ukubhekisisa & Cachiching: Lungiselela Izingcingo ze-API nokusetshenziswa kwememori nge-Batch Processing and Caching Strategies.
• Ukutholwa Kwedivayisi: Gijimani ama-LLMS kumadivayisi we-Edge Usebenzisa amathuluzi afana ne-GGUF (ye-LLama.cpp) kanye nezikhathi ezenziwe kahle ezinjenge-ONNX neTensort.

Izinsizakusebenza Zokufunda Ezinconyiwe

• Ngokusebenza kahle kwe-LLMS – Coursera – iphrojekthi eqondiswa ekwenzeni kahle futhi ikwazisa amamodeli amakhulu olimi kahle ukuthola izinhlelo zokusebenza zangempela zomhlaba.
• Ukusebenza kahle kwe-LLM Ukulinganisa Ukusebenza kahle: kusuka emcabangweni kuya ekuthumelweni okusebenzayo – i-YouTube – Isifundo sixoxa ngezinselelo nezixazululo ku-LLM ukutholwa. Igxile ekusetshenzisweni, ekusebenzeni, kanye nokuphathwa kwezindleko. (Kunconyiwe)
• I-MIT 6.5940 iwela i-2024 TINYML ne-Computer ejulile yokufunda – Ihlanganisa i-Model Compression, amandla, kanye namasu wokusebenza kahle wokuthumela amamodeli wokufunda ajulile kahle kumadivayisi acindezelwe izinsiza. (Kunconyiwe)
• Isifundo se-Inforiction sokulinganisa (KDD) – ukwenza amamodeli agijime ngokushesha – i-YouTube – Isifundo esivela eqenjini le-Amazon AWS ngezindlela zokusheshisa ukusebenza kwe-LLM Ruretime.
• Ukutholwa kwemodeli enkulu yolimi nge-ONNX RUNTIME (Kunal Vaishnavi) – umhlahlandlela wokulungisa amandla we-LLM usebenzisa i-Onnx Trailtime ngokubulawa ngokushesha nangokufanele.
• Run LLama 2 endaweni yakini ku-CPU ngaphandle kwe-GPU GGUF Ama-Models Modeb Modeb Patebook Demo – Isifundo se-Step-By-step kuma-Cunning Llama 2 Amamodeli Wendawo e-CPU usebenzisa ubungako be-GGUF.
• Isifundo ku-LLM qualization w / qlora, i-GPTQ ne-LLAMCPP, LLamama 2 – Ihlanganisa amasu ahlukahlukene wokunciphisa ubungako njenge-Qlora ne-GPTQ.
• Ukuthobeka, ukukhonza, i-petatatttantion kanye ne-vllm – kuchaza amasu wokusebenzisa amandla okulandela, kufaka phakathi i-petatatuattention ne-vllm, ukusheshisa i-LLM ekhonza.

Ukugoqa phezulu

Lo mhlahlandlela uhlanganisa umgwaqo ophelele we-RoadMap wokufunda kanye nokufunda kabusha i-LLMS ngo-2025. Ngiyazi ukuthi kungabonakala kungaphezu kokuqala, kepha ungangethemba – uma ulandela le ndlela ye-step, uzomboza yonke into nganoma yisiphi isikhathi. Uma unemibuzo noma udinga usizo olwengeziwe, khuluma.

Kanwal Mehreen IKanwal ngunjiniyela wokufunda umshini kanye nomlobi wezobuchwepheshe onesifiso esikhulu sesayensi yedatha kanye nokuhlangana kwe-AI nomuthi. Ugunyaze i-eBook “eyandisa umkhiqizo nge-Chatgpt”. Njengesikikhi se-Google Senzakalo 2022 nge-APAC, yena uPhawuni wehlukahlukana kanye nobuhle bezemfundo. Ubuye waqashelwa njengokwehluka kwe-teradata kwisazi se-tech, i-mitacs Globalk ucwaningo lwesazi, kanye ne-Harvard WeCode Scholar. UKanwal ungummeli oshisekayo ngoshintsho, osusele ama-femcode ukuhlomisa abesifazane amasimu.