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Conforme descrito pela DeepSeek, esse modelo, treinado através de aprendizado de reforço em grande escala (large-scale reinforcement learning, ROL) sem ajuste fino supervisionado (supervised fine adjustment, SFT), pode alcançar desempenho comparável ao OpenAI-o1 em tarefas de matemática, código e raciocínio. Com base no benchmark publicado pela DeepSeek, o DeepSeek-R1 ocupa o primeiro lugar no ranking dos modelos de código aberto, competindo com os modelos de código aberto mais avançados do mundo. Agora, os clientes podem solicitar uma versão preliminar prévia do DeepSeek-R1 no ”}],\r\n \"top_p\": 0,\r\n \"temperature\": 0.6,\r\n }' \\\r\nhttps://.snowflakecomputing.com/api/v2/cortex/inference:complete\r\n","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1256558119":{"id":"blog-text-c5878ef05f","text":"Próximos passos
\nDe acordo com a DeepSeek, esse é o primeiro modelo de código aberto a demonstrar que os recursos de raciocínio dos LLMs podem ser incentivados puramente por meio de RL, sem a necessidade de SFT. O Cortex AI oferece fácil integração por meio de funções SQL e APIs REST, e o Cortex Guard permite que os clientes implementem os controles de segurança necessários. A equipe de pesquisa de IA da Snowflake planeja melhorar o DeepSeek-R1 para reduzir ainda mais os custos de inferência. Com o DeepSeek-R1, os clientes podem obter eficiências de desempenho de custos e agilizar o fornecimento de aplicativos de IA generativa. Esse avanço abre caminho para futuros avanços nessa área.
\n
\nObservação: esta página contém declarações prospectivas, inclusive sobre nossas futuras ofertas de produtos. Elas não constituem compromissos de fornecimento ou produção de quaisquer ofertas de produtos. Os resultados e os produtos reais podem diferir e estão sujeitos a riscos e incertezas conhecidos e desconhecidos. Consulte nosso documento 10-Q mais recente para obter mais informações.
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