{"id":114737,"date":"2024-04-03T00:25:44","date_gmt":"2024-04-02T22:25:47","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/databricks-otkryl-bolshuyu-yazykovuyu-model-dbrx-operezhayushhuyu-v-testah-gpt-3-5"},"modified":"2024-04-03T00:25:44","modified_gmt":"2024-04-02T22:25:47","slug":"databricks-otkryl-bolshuyu-yazykovuyu-model-dbrx-operezhayushhuyu-v-testah-gpt-3-5","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/databricks-otkryl-bolshuyu-yazykovuyu-model-dbrx-operezhayushhuyu-v-testah-gpt-3-5","title":{"rendered":"Databricks hapi nj\u00eb model t\u00eb madh gjuh\u00ebsor DBRX, i cili e kalon n\u00eb testet GPT-3.5","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Kompania Databricks shpalli hapjen e modelit t\u00eb madh gjuh\u00ebsor DBRX, i cili mund t\u00eb p\u00ebrdoret p\u00ebr krijimin e chatbot-eve q\u00eb p\u00ebrgjigjen n\u00eb pyetje n\u00eb gjuh\u00ebn natyrale, zgjidhin probleme matematikore dhe jan\u00eb n\u00eb gjendje t\u00eb gjenerojn\u00eb p\u00ebrmbajtje mbi nj\u00eb tem\u00eb t\u00eb caktuar dhe t\u00eb krijojn\u00eb kod n\u00eb disa gjuh\u00eb programimi. Modeli \u00ebsht\u00eb zhvilluar nga kompania Mosaic ML, e cila u ble nga Databricks p\u00ebr 1.3 miliard dollar\u00eb. P\u00ebr trajnimin u p\u00ebrdor nj\u00eb klaster me 3072 GPU NVIDIA H100 Tensor Core. P\u00ebrnisjen e modelit t\u00eb gatsh\u00ebm rekomandohet 320GB memorie. <\/p>\n

Gjat\u00eb trajtimit t\u00eb modelit u p\u00ebrdor arkitektura MoE (Mixture of experts), q\u00eb mund\u00ebson nj\u00eb vler\u00ebsim m\u00eb t\u00eb sakt\u00eb nga ekspert\u00ebt, dhe nj\u00eb koleksion tekstesh dhe kode me madh\u00ebsi 12 Tb. Madh\u00ebsia e kontekstit t\u00eb marr\u00eb parasysh nga modeli DBRX \u00ebsht\u00eb 32 mij\u00eb tokene (numri i tokeneve q\u00eb modeli mund t\u00eb procesoj\u00eb dhe mbaj\u00eb mend gjat\u00eb gjenerimit t\u00eb tekstit). P\u00ebr krahasim, madh\u00ebsia e kontekstit tek modelet Google Gemini dhe OpenAI GPT-4 \u00ebsht\u00eb 32 mij\u00eb tokene, Google Gemma \u2014 8 mij\u00eb, nd\u00ebrsa modelin GPT-4 Turbo \u2014 128 mij\u00eb. <\/p>\n

Modeli mbulon 132 miliard\u00eb parametra dhe \u00ebsht\u00eb i ndar\u00eb n\u00eb 16 rrjete ekspert\u00ebsh, nga t\u00eb cilat gjat\u00eb p\u00ebrpunimit t\u00eb nj\u00eb k\u00ebrkese mund t\u00eb p\u00ebrdoren jo m\u00eb shum\u00eb se 4 (mbulimi jo m\u00eb shum\u00eb se 36 miliard\u00eb parametra p\u00ebr \u00e7do token). P\u00ebr krahasim, modeli GPT-4 supozohet t\u00eb p\u00ebrmbaj\u00eb 1.76 trilion parametra, modeli Grok (X\/Twitter) i zbuluar s\u00eb fundmi \u2014 314 miliard\u00eb, GPT-3.5 \u2014 175 miliard\u00eb, YaLM (Yandex) \u2014 100 miliard\u00eb, LLaMA (Meta) \u2014 65 miliard\u00eb, GigaChat (Sber) \u2014 29 miliard\u00eb, Gemma (Google) \u2014 7 miliard\u00eb. <\/p>\n

Modeli dhe komponent\u00ebt e lidhur shp\u00ebrndahen n\u00ebn licenc\u00ebn Databricks Open Model License, q\u00eb lejon p\u00ebrdorimin, riprodhimin, kopjimin, ndryshimin dhe krijimin e produkteve t\u00eb dedikuara, por me disa kufizime. P\u00ebr shembull, licenca ndalon p\u00ebrdorimin e DBRX, modeleve t\u00eb dedikuara dhe \u00e7do prodhimi n\u00eb baz\u00eb t\u00eb tyre p\u00ebr p\u00ebrmir\u00ebsimin e modeleve t\u00eb tjera gjuh\u00ebsore, p\u00ebrve\u00e7 DBRX. Licenca gjithashtu ndalon p\u00ebrdorimin e modelit n\u00eb fusha q\u00eb shkelin ligjet dhe rregulloret. Modelet e dedikuara duhet t\u00eb shp\u00ebrndahen n\u00ebn t\u00eb nj\u00ebjt\u00ebn licenc\u00eb. Kur p\u00ebrdoren n\u00eb produkte dhe sh\u00ebrbime q\u00eb p\u00ebrdoren nga m\u00eb shum\u00eb se 700 milion p\u00ebrdorues n\u00eb muaj, k\u00ebrkohet marrja e nj\u00eb lejeje t\u00eb ve\u00e7ant\u00eb. <\/p>\n

Sipas krijuesve t\u00eb modelit, karakteristikat dhe mund\u00ebsit\u00eb e DBRX tejkalojn\u00eb modelet GPT-3.5 nga kompania OpenAI dhe Grok-1 nga Twitter, dhe mund t\u00eb konkurroj\u00eb me modelin Gemini 1.0 Pro gjat\u00eb testeve t\u00eb nivelit t\u00eb kuptimit t\u00eb gjuh\u00ebs, mund\u00ebsive t\u00eb shkruarjes s\u00eb kodit n\u00eb gjuh\u00ebt e programimit dhe zgjidhjen e problemeve matematikore. N\u00eb disa aplikime, p\u00ebr shembull, gjat\u00eb gjenerimit t\u00eb pyetjeve SQL, DBRX arrin nj\u00eb efikasitet t\u00eb ngjash\u00ebm me modelin GPT-4 Turbo, i cili dominon n\u00eb treg. P\u00ebr m\u00eb tep\u00ebr, modeli dallon nga sh\u00ebrbimet konkurruese me nj\u00eb shpejt\u00ebsi shum\u00eb t\u00eb lart\u00eb dhe ndihmon n\u00eb formimin e p\u00ebrgjigjeve thuajse menj\u00ebher\u00eb. N\u00eb ve\u00e7anti, DBRX mund t\u00eb gjeneroj\u00eb tekst me shpejt\u00ebsi deri n\u00eb 150 token n\u00eb sekond\u00eb p\u00ebr \u00e7do p\u00ebrdorues, q\u00eb \u00ebsht\u00eb rreth dy her\u00eb m\u00eb shpejt se modeli LLaMA2-70B.

\"Databricks<\/center>
\"Databricks<\/center> <\/p>\n

Gjithashtu, vlen t\u00eb p\u00ebrmendet publikimi i p\u00ebrshkrimit teknik t\u00eb modelit t\u00eb madh gjuh\u00ebsor t\u00eb hapur InternLM2, i cili shp\u00ebrndahet n\u00ebn licenc\u00ebn Apache 2.0, i disponuesh\u00ebm n\u00eb variante me 20, 7 dhe 1.8 miliard parametra. Modeli po zhvillohet nga laboratori i inteligjenc\u00ebs artificiale n\u00eb Shangai me pjes\u00ebmarrjen e disa universiteteve kineze dhe \u00ebsht\u00eb i njohur p\u00ebr mbajtjen e deri n\u00eb 200K token konteksti dhe mb\u00ebshtetjes jo vet\u00ebm t\u00eb anglishtes, por edhe t\u00eb gjuh\u00ebs kineze. N\u00eb shum\u00eb teste, modeli \u00ebsht\u00eb af\u00ebr GPT-4.

\"Databricks<\/center>
\"Databricks<\/center>
\"Databricks<\/center> <\/p>\n

P\u00ebr m\u00eb tep\u00ebr, raportohet p\u00ebr zhvillimin e 84 b\u00ebrthamave t\u00eb reja p\u00ebr shumimin e matricave p\u00ebr mjetin llamafile, i zhvilluar nga Mozilla dhe q\u00eb lejon krijimin e skedar\u00ebve t\u00eb ekzekutuesh\u00ebm universale p\u00ebr ekzekutimin e modeleve t\u00eb m\u00ebdha gjuh\u00ebsore t\u00eb m\u00ebsimit t\u00eb makineris\u00eb (LLM). Ndryshimet lejuan nj\u00eb shpejt\u00ebsi shum\u00eb m\u00eb t\u00eb madhe n\u00eb pun\u00ebn e modeleve n\u00eb llamafile gjat\u00eb ekzekutimit n\u00eb CPU. P\u00ebr shembull, ekzekutimi i modeleve duke p\u00ebrdorur llamafile tani \u00ebsht\u00eb m\u00eb i shpejt\u00eb, se sa duke p\u00ebrdorur llama.cpp nga 30% deri n\u00eb 500% var\u00ebsisht nga ambienti, nd\u00ebrsa p\u00ebr sa i p\u00ebrket bibliotek\u00ebs MKL, operacionet matricore q\u00eb hyjn\u00eb n\u00eb cache L2, n\u00eb implementimin e ri ekzekutohen dy her\u00eb m\u00eb shpejt.
\n
Burimi: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Databricks \u043e\u0431\u044a\u044f\u0432\u0438\u043b\u0430 \u043e\u0431 \u043e\u0442\u043a\u0440\u044b\u0442\u0438\u0438 \u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u044f\u0437\u044b\u043a\u043e\u0432\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 DBRX, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0447\u0430\u0442\u0431\u043e\u0442\u043e\u0432, \u043e\u0442\u0432\u0435\u0447\u0430\u044e\u0449\u0438\u0445 \u043d\u0430 \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u043d\u0430 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u044f\u0437\u044b\u043a\u0435, \u0440\u0435\u0448\u0430\u044e\u0449\u0438\u0445 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043d\u044b\u0435 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0437\u0430\u0434\u0430\u0447\u0438, \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u044b\u0445 \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043a\u043e\u043d\u0442\u0435\u043d\u0442 \u043d\u0430 \u0437\u0430\u0434\u0430\u043d\u043d\u0443\u044e \u0442\u0435\u043c\u0443 \u0438 \u0441\u043e\u0437\u0434\u0430\u0432\u0430\u0442\u044c \u043a\u043e\u0434 \u043d\u0430 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u0430\u0445 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f. \u041c\u043e\u0434\u0435\u043b\u044c \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 Mosaic ML, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0431\u044b\u043b\u0430 \u043a\u0443\u043f\u043b\u0435\u043d\u0430 Databricks \u0437\u0430 1.3 \u043c\u043b\u0440\u0434 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432. \u0414\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043b\u0441\u044f \u043a\u043b\u0430\u0441\u0442\u0435\u0440 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":114738,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-114737","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-novosti-interneta"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Databricks hap modelin e madh gjuh\u00ebsor DBRX, q\u00eb tejkalon n\u00eb testet GPT-3.5 | ProHoster","description":"Kompania Databricks shpalli hapjen e modelit t\u00eb madh gjuh\u00ebsor DBRX, i cili mund t\u00eb p\u00ebrdoret p\u00ebr krijimin e chatbot-eve q\u00eb p\u00ebrgjigjen n\u00eb pyetje n\u00eb gjuh\u00ebn natyrale, zgjidhin probleme matematikore dhe jan\u00eb n\u00eb gjendje t\u00eb gjenerojn\u00eb p\u00ebrmbajtje mbi nj\u00eb tem\u00eb t\u00eb caktuar dhe t\u00eb krijojn\u00eb kod n\u00eb disa gjuh\u00eb programimi. Modeli \u00ebsht\u00eb zhvilluar nga kompania Mosaic ML, e cila u ble nga Databricks p\u00ebr 1.3 miliard dollar\u00eb. P\u00ebr trajnimin u p\u00ebrdor nj\u00eb klaster","canonical_url":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/databricks-otkryl-bolshuyu-yazykovuyu-model-dbrx-operezhayushhuyu-v-testah-gpt-3-5","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"sq_AL","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47Databricks \u043e\u0442\u043a\u0440\u044b\u043b \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u044f\u0437\u044b\u043a\u043e\u0432\u0443\u044e \u043c\u043e\u0434\u0435\u043b\u044c DBRX, \u043e\u043f\u0435\u0440\u0435\u0436\u0430\u044e\u0449\u0443\u044e \u0432 \u0442\u0435\u0441\u0442\u0430\u0445 GPT-3.5 | ProHoster","og:description":"\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Databricks \u043e\u0431\u044a\u044f\u0432\u0438\u043b\u0430 \u043e\u0431 \u043e\u0442\u043a\u0440\u044b\u0442\u0438\u0438 \u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u044f\u0437\u044b\u043a\u043e\u0432\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 DBRX, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0447\u0430\u0442\u0431\u043e\u0442\u043e\u0432, \u043e\u0442\u0432\u0435\u0447\u0430\u044e\u0449\u0438\u0445 \u043d\u0430 \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u043d\u0430 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u044f\u0437\u044b\u043a\u0435, \u0440\u0435\u0448\u0430\u044e\u0449\u0438\u0445 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043d\u044b\u0435 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0437\u0430\u0434\u0430\u0447\u0438, \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u044b\u0445 \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043a\u043e\u043d\u0442\u0435\u043d\u0442 \u043d\u0430 \u0437\u0430\u0434\u0430\u043d\u043d\u0443\u044e \u0442\u0435\u043c\u0443 \u0438 \u0441\u043e\u0437\u0434\u0430\u0432\u0430\u0442\u044c \u043a\u043e\u0434 \u043d\u0430 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u0430\u0445 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f. \u041c\u043e\u0434\u0435\u043b\u044c \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 Mosaic ML, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0431\u044b\u043b\u0430 \u043a\u0443\u043f\u043b\u0435\u043d\u0430 Databricks \u0437\u0430 1.3 \u043c\u043b\u0440\u0434 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