Uhlolojikelele
I-YaRN (Nokho enye isandiso se-RoPE) iwubuchule obunweba iwindi lomongo elisebenzisekayo le-transformer kude kakhulu kunalokho ebiqeqeshelwa kukho, ngokulungiswa okuncane okuncane. Ibalulekile ngoba ivumela amamodeli akhona ukuthi aphathe amadokhumenti amade ngaphandle kokuphinda aqeqeshwe kusukela ekuqaleni.
I-YaRN Context Window Scalling iyingxenye yesitaki solimi-AI esisetshenziselwa ukufunda, ukukhiqiza, ukuhlukanisa, nokuguqula umbhalo nenkulumo ngezinga.
I-Deep Dive
Ama-LLM amaningi esimanje ahlanganisa izindawo zamagama kusetshenziswa i-Rotary Position Embeddings (RoPE), esebenza kahle kuphela kuze kufike kubude obubonwe yimodeli ngesikhathi sokuqeqeshwa. Okuphakelayo ngokulandelana okude futhi imodeli yonakala kabi. I-YaRN ixazulula lokhu ngokukala kabusha amafrikhwensi e-RoPE ngendlela eqaphela imvamisa: izilinganiso zefrikhwensi ephezulu (ezithwebula ubudlelwano bendawo, obuseduze) zishiywa zingakathintwa kakhulu, kuyilapho izilinganiso zefrikhwensi ephansi (ezithwebula indawo yebanga elide) ziyahlanganiswa. Futhi yengeza ukulungiswa kwezinga lokushisa ekunakeni ukuze kugcinwe amalogi eziphatha kahle kumabanga amade. Umphumela, oboniswe kumamodeli we-LLaMA, unweba umongo ukusuka ku-4K ukuya kumathokheni angu-64K-128K kusetshenziswa kuphela cishe u-0.1% wedatha yokuqeqeshwa yasekuqaleni kanye nezinyathelo ezingamakhulu ambalwa zokulungisa kahle.
I-Technical Insight
I-RoPE izungezisa umbuzo namavekhtha angukhiye nge-engeli elinganayo nendawo kanye nefrikhwensi yobukhulu bobukhulu. I-Naive linear interpolation (Position Interpolation) icindezela wonke amafrikhwensi ngokulinganayo, ilimaza imininingwane yendawo. I-YaRN esikhundleni salokho isebenzisa i-'NTK-by-parts': ihlanganisa kuphela ubukhulu befrikhwensi ephansi (ubude begagasi elide), ishiya amaza aphezulu wodwa, kanye namarampu phakathi kwawo. Ukukalwa kwezinga lokushisa lokunaka kunxephezela ukushintsha kwe-entropy, okugcina ukunemba ngobude obunwetshiwe.
I-Mastering YaRN Context Window Scaling
I-YaRN (Nokho enye isandiso se-RoPE) iwubuchule obunweba iwindi lomongo elisebenzisekayo le-transformer kude kakhulu kunalokho ebiqeqeshelwa kukho, ngokulungiswa okuncane okuncane. Ibalulekile ngoba ivumela amamodeli akhona ukuthi aphathe amadokhumenti amade ngaphandle kokuphinda aqeqeshwe kusukela ekuqaleni. I-YaRN Context Window Scalling iyingxenye yesitaki solimi-AI esisetshenziselwa ukufunda, ukukhiqiza, ukuhlukanisa, nokuguqula umbhalo nenkulumo ngezinga. Ukuze wakhe ukuqonda okujulile, phatha i-YaRN Context Window Scaling njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela oyifunayo, ucacise ukucabanga, futhi uhlukanise lokho isistimu engakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa idizayini ye-YaRN Context Window Scaling, ukubuyisa, nokubuyekeza amalophu 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
Ukunweba imodeli evulekile ye-LLaMA ukusuka ku-4K kuye kumathokheni angu-128K ukuze ingenise yonke i-codebase noma inkontileka ende ngephasi eyodwa.
Ukuvumela i-chatbot ukuthi igcine imilando emide kakhulu yezingxoxo ngaphandle kokunciphisa izikhathi zangaphambili
Ifinyeza amadokhumenti obude bencwadi noma imibhalo yamahora amaningi eyeqa iwindi lomdabu lemodeli eyisisekelo
Ukushintsha kalula imodeli eqeqeshwe kusengaphambili yemisebenzi yokubuyisa okuqukethwe okude kusetshenziswa ukuhlelwa okuncane kuphela
Amaphethini Okusebenzisa
I-YaRN Context Window Scaling in practice
Ukunweba imodeli evulekile ye-LLaMA ukusuka ku-4K kuye kumathokheni angu-128K ukuze ingenise yonke i-codebase noma inkontileka ende ngephasi eyodwa.
Ukunweba imodeli ye-LLaMA evulekile isuka ku-4K iye kumathokheni angu-128K ukuze ingenise yonke i-codebase noma inkontileka ende ephasini elilodwa Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka komuntu ngamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-YaRN Context Window Scaling in practice
Ukuvumela i-chatbot ukuthi igcine imilando emide kakhulu yezingxoxo ngaphandle kokunciphisa izikhathi zangaphambili.
Ukuvumela i-chatbot igcine imilando emide kakhulu yezingxoxo ngaphandle kokunqamula amajika angaphambili Amaqembu ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-YaRN Context Window Scaling in practice
Ifinyeza amadokhumenti obude bencwadi noma imibhalo yamahora amaningi eyeqa iwindi lomdabu lemodeli eyisisekelo.
Ukufingqa amadokhumenti obude bencwadi noma okulotshiweyo okuthatha amahora amaningi okweqa iwindi lomdabu lemodeli yesisekelo Amaqembu ngokuvamile athola imiphumela engcono uma echaza ikhwalithi ephezulu ngaphambili, egcina indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-YaRN Context Window Scaling in practice
Ukulungisa ngokushibhile imodeli eqeqeshwe kusengaphambili yemisebenzi yokubuyiswa yomongo omude kusetshenziswa uhlaka oluncane lokushuna kahle.
Ukushintsha kalula imodeli eqeqeshwe kusengaphambili yemisebenzi yokubuyiswa kokuqukethwe okude kusetshenziswa uhlelo oluncane lokuhlela kahle Amaqembu ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu 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.