{"id":113821,"date":"2024-02-23T22:02:47","date_gmt":"2024-02-23T20:02:48","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/google-otkryl-ai-model-gemma-osnovannuyu-na-tehnologiyah-obshhih-s-chat-botom-gemini"},"modified":"2024-02-23T22:02:47","modified_gmt":"2024-02-23T20:02:48","slug":"google-otkryl-ai-model-gemma-osnovannuyu-na-tehnologiyah-obshhih-s-chat-botom-gemini","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/news\/google-otkryl-ai-model-gemma-osnovannuyu-na-tehnologiyah-obshhih-s-chat-botom-gemini","title":{"rendered":"O Google lan\u00e7ou o modelo de IA Gemma, baseado em tecnologias comuns com o chatbot Gemini","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

A Google anunciou a publica\u00e7\u00e3o de um grande modelo de linguagem de aprendizado de m\u00e1quina chamado Gemma, constru\u00eddo usando tecnologias aplicadas no modelo do chatbot Gemini, que tenta competir com o ChatGPT. O modelo est\u00e1 dispon\u00edvel em quatro variantes, abrangendo 2 e 7 bilh\u00f5es de par\u00e2metros, em vers\u00f5es b\u00e1sica e otimizada para sistemas de di\u00e1logo. As variantes com 2 bilh\u00f5es de par\u00e2metros s\u00e3o adequadas para uso em aplicativos de consumo e seu processamento \u00e9 suficiente com CPU. As variantes com 7 bilh\u00f5es de par\u00e2metros exigem hardware mais potente e a presen\u00e7a de GPU ou TPU. <\/p>\n

Entre as \u00e1reas de aplica\u00e7\u00e3o do modelo Gemma, est\u00e3o a cria\u00e7\u00e3o de sistemas de di\u00e1logo e assistentes virtuais, gera\u00e7\u00e3o de texto, formula\u00e7\u00e3o de respostas a perguntas em linguagem natural, resumo e generaliza\u00e7\u00e3o de conte\u00fado, explica\u00e7\u00e3o do significado de conceitos e termos, corre\u00e7\u00e3o de erros no texto, e aux\u00edlio no aprendizado de idiomas. Suporta a cria\u00e7\u00e3o de diversos tipos de dados textuais, incluindo poesias, c\u00f3digo em linguagens de programa\u00e7\u00e3o, reescrita de obras com outras palavras, e formula\u00e7\u00e3o de cartas a partir de um modelo. O modelo tem um tamanho relativamente pequeno, permitindo seu uso em equipamentos com recursos limitados, como notebooks e PCs comuns. <\/p>\n

A licen\u00e7a do modelo permite o uso e distribui\u00e7\u00e3o gratuitos n\u00e3o apenas em projetos de pesquisa e pessoais, mas tamb\u00e9m em produtos comerciais. Tamb\u00e9m \u00e9 permitido criar e publicar variantes modificadas do modelo. As condi\u00e7\u00f5es de uso pro\u00edbem a aplica\u00e7\u00e3o do modelo para realiza\u00e7\u00f5es de a\u00e7\u00f5es maliciosas e prescrevem a utiliza\u00e7\u00e3o da vers\u00e3o mais recente do Gemma nos seus produtos sempre que poss\u00edvel. <\/p>\n

O suporte para trabalhar com modelos Gemma j\u00e1 foi adicionado ao conjunto de ferramentas Transformers e ao Responsible Generative AI Toolkit. Para otimizar o modelo, voc\u00ea pode usar o framework Keras e backends para TensorFlow, JAX e PyTorch. Tamb\u00e9m \u00e9 poss\u00edvel utilizar o Gemma com frameworks MaxText, NVIDIA NeMo e TensorRT-LLM. <\/p>\n

O tamanho do contexto considerado pelo modelo Gemma \u00e9 de 8 mil tokens (o n\u00famero de tokens que o modelo pode processar e reter ao gerar texto). Para compara\u00e7\u00e3o, o tamanho do contexto dos modelos Gemini e GPT-4 \u00e9 de 32 mil tokens, enquanto o modelo GPT-4 Turbo suporta at\u00e9 128 mil. O modelo suporta apenas o idioma ingl\u00eas. Em termos de desempenho, o modelo Gemma-7B \u00e9 ligeiramente inferior ao modelo LLama 2 70B Chat e um pouco superior aos modelos DeciLM-7B, PHI-2 (2.7B) e Mistral-7B-v0.1. Em compara\u00e7\u00e3o, o modelo Gemma-7B supera ligeiramente os modelos LLama 2 7B\/13B e Mistral-7B.

\"O<\/center>
\n
Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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ChatGPT.","og:url":"https:\/\/prohoster.info\/pt\/blog\/news\/google-otkryl-ai-model-gemma-osnovannuyu-na-tehnologiyah-obshhih-s-chat-botom-gemini","og:image":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:secure_url":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:width":350,"og:image:height":350,"article:published_time":"2024-02-23T20:02:48+00:00","article:modified_time":"2024-02-23T20:02:48+00:00","article:publisher":"https:\/\/www.facebook.com\/prohoster","article:author":"https:\/\/www.facebook.com\/prohoster"},"aioseo_meta_data":[],"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/posts\/113821","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/comments?post=113821"}],"version-history":[{"count":0,"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/posts\/113821\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/media\/113822"}],"wp:attachment":[{"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/media?parent=113821"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/categories?post=113821"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prohoster.info\/pt\/wp-json\/wp\/v2\/tags?post=113821"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}