{"id":2039,"date":"2023-06-24T12:10:58","date_gmt":"2023-06-24T12:10:58","guid":{"rendered":"https:\/\/dailyai.com\/?p=2039"},"modified":"2023-06-24T13:04:21","modified_gmt":"2023-06-24T13:04:21","slug":"mosaicml-unveils-the-mpt-30b-family-of-open-source-ai-models","status":"publish","type":"post","link":"https:\/\/dailyai.com\/de\/2023\/06\/mosaicml-unveils-the-mpt-30b-family-of-open-source-ai-models\/","title":{"rendered":"MosaicML stellt die MPT-30B-Familie von Open-Source-KI-Modellen vor"},"content":{"rendered":"

MosaicML hat seine neuen Open-Source-KI-Modelle MPT-30B Base, Instruct und Chat vorgestellt.\u00a0<\/strong><\/p>\n

Als Teil der MPT-Serie (MosaicML Pre Trained Transformer) gelten diese Open-Source-Modelle als die anspruchsvollsten in ihrer Kategorie und \u00fcbertreffen GPT-3 in den meisten Schl\u00fcsselmetriken.\u00a0<\/span><\/p>\n

Mosaik<\/span><\/a> haben ihre neuen Modelle mit den neuesten H100-Chips von NVIDIA geschult, die Anfang dieses Jahres auf den Markt kamen.\u00a0<\/span><\/p>\n

MPT-30B ist das erste \u00f6ffentlich bekannte LLM, das auf High-End NVIDIA H100 GPUs trainiert wurde.\u00a0<\/span><\/p>\n

Seit ihrer Einf\u00fchrung am 5. Mai 2023 wurden die vorherigen MPT-7B-Modelle von Mosaic (Base, Instruct, Chat, StoryWriter) \u00fcber 3,3 Millionen Mal heruntergeladen. MPT-30B hat 30 Milliarden Parameter - weit weniger als die 175 Milliarden Parameter von GPT-3 oder die angebliche 1 Billion Parameter von GPT-4.\u00a0<\/span><\/p>\n

Aber die Anzahl der Parameter ist nicht alles - ganz im Gegenteil. MPT-30B wurde auf l\u00e4ngeren Sequenzen mit bis zu 8.000 Token trainiert, viermal mehr als GPT-3, die LLaMA-Modellfamilie und das Falcon-Modell.<\/span><\/p>\n

Dadurch kann der MPT-30B datenintensive Unternehmens-Workflows besser verwalten und andere Modelle f\u00fcr codeintensive Workflows ausstechen.<\/span><\/p>\n

Mehrere Unternehmen wie Replit, eine f\u00fchrende webbasierte IDE, und Scatter Lab, ein KI-Startup, haben bereits die Open-Source-Modelle von MPT genutzt, die sich besser anpassen lassen als propriet\u00e4re Modelle wie GPT-3.\u00a0<\/span><\/p>\n

Ilan Twig, Mitbegr\u00fcnder und CTO bei Navan, sagte. \"Bei Navan nutzen wir generative KI f\u00fcr alle unsere Produkte und Dienstleistungen, wie z. B. f\u00fcr unser virtuelles Reiseb\u00fcro und unseren konversationellen Business Intelligence Agent. Die Basismodelle von MosaicML bieten hochmoderne Sprachf\u00e4higkeiten und sind gleichzeitig extrem effizient bei der Feinabstimmung und der Bereitstellung von Inferenzen im gro\u00dfen Ma\u00dfstab.\"\u00a0<\/span><\/p>\n

MPT-30B, jetzt erh\u00e4ltlich \u00fcber den HuggingFace Hub<\/span><\/a>Die Software ist vollst\u00e4ndig quelloffen und kann von Entwicklern mit ihren Daten feinabgestimmt werden.\u00a0<\/span><\/p>\n

Mosaic soll es Unternehmen erm\u00f6glichen, leistungsstarke Open-Source-Modelle in ihre Arbeitsabl\u00e4ufe zu integrieren und dabei die Datenhoheit zu behalten.\u00a0<\/span><\/p>\n

Der Open-Source-Vorteil<\/span><\/h2>\n

Open-Source-Modelle sind <\/span>die Kluft schnell zu \u00fcberbr\u00fccken<\/span><\/a> mit Wettbewerbern wie OpenAI.\u00a0<\/span><\/p>\n

Da die f\u00fcr das Trainieren und Bereitstellen von Modellen erforderlichen Rechenressourcen sinken, ben\u00f6tigen Open-Source-Entwickler keine millionenschweren Supercomputer mit Hunderten von High-End-Prozessoren mehr, um ihre Modelle zu trainieren.\u00a0<\/span><\/p>\n

Das Gleiche gilt f\u00fcr den Einsatz von Modellen - MPT-30B kann auf einem einzigen Grafikprozessor laufen, und der Open-Source-Gemeinschaft ist es sogar gelungen, eine abgespeckte Version des LLaMa-Modells auf einem Raspberry PI laufen zu lassen.<\/span><\/p>\n

\n

Ich habe sucefully lief LLaMA 7B Modell auf meinem 4GB RAM Raspberry Pi 4. Es ist super langsam \u00fcber 10sec\/Token. Aber es sieht aus, wir k\u00f6nnen leistungsstarke kognitive Pipelines auf einer billigen Hardware laufen. pic.twitter.com\/XDbvM2U5GY<\/a><\/p>\n

- Artem Andreenko \ud83c\uddfa\ud83c\udde6 (@miolini) 12. M\u00e4rz 2023<\/a><\/p><\/blockquote>\n

Dar\u00fcber hinaus bieten Open-Source-Modelle den Unternehmen strategische Vorteile. <\/span><\/p>\n

Unternehmen in Branchen wie dem Gesundheitswesen und dem Bankensektor m\u00f6chten ihre Daten m\u00f6glicherweise nicht mit OpenAI oder Google teilen.\u00a0<\/span><\/p>\n

Naveen Rao, Mitbegr\u00fcnder und CEO von MosaicML, bezeichnet Open-Source-Projekte als Verb\u00fcndete und erkl\u00e4rt, dass sie \"die L\u00fccke zu diesen Closed-Source-Modellen schlie\u00dfen\". Obwohl er die \u00dcberlegenheit von OpenAIs GPT-4 anerkennt, argumentiert er, dass Open-Source-Modelle \"die Schwelle \u00fcberschritten haben, wo diese Modelle tats\u00e4chlich extrem n\u00fctzlich sind\".<\/span><\/p>\n

Open-Source-KI entwickelt sich rasant, was einige dazu veranlasst, Big Tech zu beschuldigen, auf eine Regulierung zu dr\u00e4ngen, um ihr Wachstum zu bremsen. Unternehmen bauen bereits ihre eigenen Open-Source-KI-Stacks auf und sparen so Geld, das andernfalls in Unternehmen wie OpenAI flie\u00dfen w\u00fcrde.\u00a0<\/span><\/p>\n

A durchgesickertes Memo eines Google-Mitarbeiters<\/a> sagte, dass alle gro\u00dfen Tech-KI-Entwickler - Google, Microsoft, Anthropic und OpenAI - mit Open-Source-Entwicklern konkurrieren.\u00a0<\/span><\/p>\n

Open-Source-KI-Entwickler k\u00f6nnen Modelle schneller erstellen und iterieren als die gro\u00dfen Technologieunternehmen, wodurch sie die g\u00e4ngigen KI-Modelle ausman\u00f6vrieren k\u00f6nnen.<\/span><\/p>\n

Das ist nicht ungef\u00e4hrlich, denn Open-Source-Modelle sind schwer zu \u00fcberwachen und zu regulieren, sobald sie in \u00f6ffentliche H\u00e4nde gelangen.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

MosaicML hat seine neuen Open-Source-KI-Modelle MPT-30B Base, Instruct und Chat vorgestellt. Diese Open-Source-Modelle sind Teil der MPT (MosaicML Pre Trained Transformer)-Serie und gelten als die anspruchsvollsten in ihrer Kategorie, da sie GPT-3 in den meisten Schl\u00fcsselmetriken \u00fcbertreffen. Mosaic hat seine neuen Modelle mit den neuesten H100-Chips von NVIDIA trainiert, die Anfang dieses Jahres auf den Markt kamen. MPT-30B ist das erste \u00f6ffentlich bekannte LLM, das auf den High-End-GPUs NVIDIA H100 trainiert wurde. Seit ihrer Einf\u00fchrung am 5. Mai 2023 wurden die vorherigen MPT-7B-Modelle von Mosaic (Base, Instruct, Chat, StoryWriter) \u00fcber 3,3 Millionen Mal heruntergeladen. MPT-30B hat 30 Milliarden Parameter - weit weniger als die 175 Parameter von GPT-3.<\/p>","protected":false},"author":2,"featured_media":2040,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[83],"tags":[115,102,105,110],"class_list":["post-2039","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product","tag-chatgpt","tag-google","tag-machine-learning","tag-open-source"],"yoast_head":"\nMosaicML unveils the MPT-30B family of open-source AI models | DailyAI<\/title>\n<meta name=\"description\" content=\"MPT-30B Base, Instruct, and Chat.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/de\/2023\/06\/mosaicml-unveils-the-mpt-30b-family-of-open-source-ai-models\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta 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