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DeepSeek-R1 achieved prime scores on AIME and ProverBench, matching or outperforming OpenAI o1 and GPT-4 in math and logic tasks. Pre-train on massive multilingual corpora, then fine-tune on industry datasets—minimizing over-parameterization and maximizing accuracy on specialised tasks. By leveraging a pre-export stockpile of Nvidia A100 and H800 GPUs—and a small, multidisciplinary team—DeepSeek pioneered open-weight LLMs underneath an MIT license, democratizing access to cost-effective generative AI worldwide. In the quickly evolving world of generative AI and budget-friendly machine studying, one Hangzhou startup is proving you don’t need billion-dollar budgets to build top-tier LLMs. Businesses, researchers, builders, and inventive professionals can benefit from DeepSeek’s advanced AI capabilities. Sure, DeepSeek permits customers to fine-tune parameters like response size, tone, and specificity for tailored results.
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- DeepSeek Coder is a series of eight models, 4 pretrained (Base) and 4 instruction-finetuned (Instruct).
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- DeepSeek-V3 is our general-purpose model nice for most tasks.
From zero to clever AI integration in minutes — whether you are a casual user or enterprise developer. DeepSeek-R1 trained through pure RL — no supervised fine-tuning in any respect. Achieves 60% memory discount and 30% sooner training throughput in comparison with BF16 baselines.
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After training, it was deployed on clusters of H800 GPUs. The DeepSeek group performed extensive low-level engineering to improve efficiency. The DeepSeek-Coder V2 series included V2-Base, V2-Lite-Base, V2-Instruct, and V20-Lite-Instruct.. They skilled the Lite model to help “additional analysis and improvement on MLA and DeepSeekMoE”. The two V2-Lite models have been smaller, and educated equally. For instance, RL on reasoning may enhance over more coaching steps.
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