{"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\/et\/blog\/novosti-interneta\/databricks-otkryl-bolshuyu-yazykovuyu-model-dbrx-operezhayushhuyu-v-testah-gpt-3-5","title":{"rendered":"Databricks avas suure keeletehnoloogia DBRX, mis \u00fcletab GPT-3.5 teste","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Databricks kuulutas v\u00e4lja suure keelemudeli DBRX, mida saab kasutada vestlusrobotite loomisel, mis vastavad loomuliku keele k\u00fcsimustele, lahendavad esitatud matemaatilisi \u00fclesandeid, genereerivad sisu m\u00e4\u00e4ratud teemal ja kirjutavad koodi erinevates programmeerimiskeeltes. Mudeli arendas Mosaic ML, mille Databricks ostis 1,3 miljardi dollari eest. Koolitamiseks kasutati 3072 NVIDIA H100 Tensor Core GPU klastrit. Valmis mudeli k\u00e4itamiseks on soovitatav 320 GB m\u00e4lu. <\/p>\n

\u041f\u0440\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0438 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u043b\u0430\u0441\u044c \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430 MoE (Mixture of experts), \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0430\u044f \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0431\u043e\u043b\u0435\u0435 \u0442\u043e\u0447\u043d\u0443\u044e \u044d\u043a\u0441\u043f\u0435\u0440\u0442\u043d\u0443\u044e \u043e\u0446\u0435\u043d\u043a\u0443, \u0438 \u043a\u043e\u043b\u043b\u0435\u043a\u0446\u0438\u044f \u0442\u0435\u043a\u0441\u0442\u043e\u0432 \u0438 \u043a\u043e\u0434\u0430, \u0440\u0430\u0437\u043c\u0435\u0440\u043e\u043c 12 Tb. \u0420\u0430\u0437\u043c\u0435\u0440 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u043c\u043e\u0433\u043e \u043c\u043e\u0434\u0435\u043b\u044c\u044e DBRX \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u0430 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 32 \u0442\u044b\u0441\u044f\u0447 \u0442\u043e\u043a\u0435\u043d\u043e\u0432 (\u0447\u0438\u0441\u043b\u043e \u0442\u043e\u043a\u0435\u043d\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u043c\u043e\u0436\u0435\u0442 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0438 \u0437\u0430\u043f\u043e\u043c\u043d\u0438\u0442\u044c \u043f\u0440\u0438 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u0442\u0435\u043a\u0441\u0442\u0430). \u0414\u043b\u044f \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \u0440\u0430\u0437\u043c\u0435\u0440 \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u0430 \u0443 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 Google Gemini \u0438 OpenAI GPT-4 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 32 \u0442\u044b\u0441\u044f\u0447\u0438 \u0442\u043e\u043a\u0435\u043d\u043e\u0432, Google Gemma — 8 \u0442\u044b\u0441\u044f\u0447, \u0430 \u0443 \u043c\u043e\u0434\u0435\u043b\u0438 GPT-4 Turbo — 128 \u0442\u044b\u0441\u044f\u0447. <\/p>\n

\u041c\u043e\u0434\u0435\u043b\u044c \u043e\u0445\u0432\u0430\u0442\u044b\u0432\u0430\u0435\u0442 132 \u043c\u0438\u043b\u043b\u0438\u0430\u0440\u0434\u0430 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u043e\u0432 \u0438 \u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0430 \u043d\u0430 16 \u044d\u043a\u0441\u043f\u0435\u0440\u0442\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439, \u0438\u0437 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u043f\u0440\u0438 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0435 \u0437\u0430\u043f\u0440\u043e\u0441\u0430 \u043c\u043e\u0433\u0443\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043d\u0435 \u0431\u043e\u043b\u0435\u0435 4 (\u043e\u0445\u0432\u0430\u0442 \u043d\u0435 \u0431\u043e\u043b\u0435\u0435 36 \u043c\u043b\u0440\u0434 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u043e\u0432 \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0442\u043e\u043a\u0435\u043d\u0430). \u0414\u043b\u044f \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u044c GPT-4 \u043f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 1.76 \u0442\u0440\u0438\u043b\u043b\u0438\u043e\u043d\u0430 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u043e\u0432, \u043d\u0435\u0434\u0430\u0432\u043d\u043e \u043e\u0442\u043a\u0440\u044b\u0442\u0430\u044f X\/Twitter \u043c\u043e\u0434\u0435\u043b\u044c Grok (X\/Twitter) — 314 \u043c\u043b\u0440\u0434, GPT-3.5 — 175 \u043c\u043b\u0440\u0434, YaLM (Yandex) — 100 \u043c\u043b\u0440\u0434, LLaMA (Meta) — 65 \u043c\u043b\u0440\u0434, GigaChat (Sber) — 29 \u043c\u043b\u0440\u0434, Gemma (Google) — 7 \u043c\u043b\u0440\u0434. <\/p>\n

Mudelit ja sellega seotud komponente levitatakse Databricks Open Model License'i alusel, mis v\u00f5imaldab neid kasutada, kopeerida, muuta ja luua derivaatooteid, kuid teatud piirangutega. N\u00e4iteks keelab litsents DBRX, derivaadi mudelite ja nende p\u00f5hjal tehtud v\u00e4ljundite kasutamise teiste keelemudelite, mis erinevad DBRX-ist, t\u00e4iustamiseks. Litsents keelab ka mudeli kasutamise valdkondades, mis rikuvad seadusi ja m\u00e4\u00e4rusi. Derivaadi mudelid peavad olema levitatud sama litsentsi alusel. Tooteid ja teenuseid, mida kasutavad \u00fcle 700 miljoni kasutaja kuus, kasutades, on n\u00f5utav eraldi luba. <\/p>\n

Mudeli koostajate s\u00f5nul \u00fcletab DBRX oma omaduste ja v\u00f5imetega OpenAI GPT-3.5 ja Twitteri Grok-1 mudeleid ning suudab konkureerida Gemini 1.0 Pro mudeliga keele m\u00f5istmise, programmeerimiskeelte kirjutamise v\u00f5imaluste ja matemaatiliste probleemide lahendamise testimisel. Teatud rakendustes, n\u00e4iteks SQL-p\u00e4ringute genereerimisel, on DBRX t\u00f5husus l\u00e4hedane mudelile GPT-4 Turbo, mis on turuliider. Lisaks eristab mudelit konkurentidest \u00e4\u00e4rmiselt kiire t\u00f6\u00f6 ja see v\u00f5imaldab vastuseid peaaegu koheselt genereerida. Eriti DBRX suudab genereerida teksti kiirusel kuni 150 tokenit sekundis \u00fche kasutaja kohta, mis on ligikaudu kaks korda kiirem kui LLaMA2-70B mudel.

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

Samuti tasub \u00e4ra m\u00e4rkida tehnilise kirjelduse avaldamine avatud suure keelemudeli InternLM2 kohta, mis levib Apache 2.0 litsentsi alusel ja on saadaval variantides, millel on 20, 7 ja 1,8 miljardit parameetrit. Mudelit arendab Shanghai tehisintellekti labor koost\u00f6\u00f6s mitmete Hiina \u00fclikoolidega ning sellel on kuni 200K tokeni konteksi arvestamine ning see toetab mitte ainult inglise, vaid ka hiina keelt. Paljusid teste on mudel GPT-4-le l\u00e4hedane.

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

Lisaks on teatatud 84 uue maatriksi korrutamise s\u00f5lme arendamisest llamafile t\u00f6\u00f6riistakomplektile, mille arendab Mozilla ja mis v\u00f5imaldab luua universaalseid t\u00e4idetavaid faile suurte keelemudelite masin\u00f5ppe (LLM) k\u00e4itamiseks. Muudatused on oluliselt kiirendanud mudelite t\u00f6\u00f6tlust llamafile'is, kui neid k\u00e4itatakse CPU-l. N\u00e4iteks aitab llamafile'i kasutamine n\u00fc\u00fcd mudelite k\u00e4itamisel saavutada kiiruset\u00f5usu 30% kuni 500%, v\u00f5rreldes llama.cpp'ga keskkonnast s\u00f5ltuvalt, ning uues teostuses t\u00e4idetakse maatriksite toimingud, mis mahuvad L2 vahem\u00e4lu, kaks korda kiiremini kui MKL teek.
\n
Allikas: 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 tutvustas suurt keelemudelit DBRX, mis \u00fcletab testides GPT-3.5 | ProHoster","description":"Databricks kuulutas v\u00e4lja suure keelemudeli DBRX, mida saab kasutada vestlusrobotite loomisel, mis vastavad loomuliku keele k\u00fcsimustele, lahendavad esitatud matemaatilisi \u00fclesandeid, genereerivad sisu m\u00e4\u00e4ratud teemal ja kirjutavad koodi erinevates programmeerimiskeeltes. Mudeli arendas Mosaic ML, mille Databricks ostis 1,3 miljardi dollari eest. Koolitamiseks kasutati klastrit","canonical_url":"https:\/\/prohoster.info\/et\/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":"et_EE","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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