DeepSeek aI App: free Deep Seek aI App For Android/iOS

The AI race is heating up, and DeepSeek AI is positioning itself as a pressure to be reckoned with. When small Chinese artificial intelligence (AI) firm DeepSeek launched a household of extraordinarily environment friendly and extremely aggressive AI fashions final month, it rocked the worldwide tech group. It achieves an impressive 91.6 F1 score in the 3-shot setting on DROP, outperforming all other fashions in this category. On math benchmarks, DeepSeek-V3 demonstrates distinctive efficiency, considerably surpassing baselines and setting a brand new state-of-the-artwork for non-o1-like fashions. DeepSeek r1-V3 demonstrates aggressive performance, standing on par with top-tier fashions similar to LLaMA-3.1-405B, GPT-4o, and Claude-Sonnet 3.5, while significantly outperforming Qwen2.5 72B. Moreover, DeepSeek-V3 excels in MMLU-Pro, a extra challenging educational knowledge benchmark, the place it carefully trails Claude-Sonnet 3.5. On MMLU-Redux, a refined version of MMLU with corrected labels, DeepSeek-V3 surpasses its friends. This success could be attributed to its advanced information distillation approach, which successfully enhances its code technology and drawback-solving capabilities in algorithm-focused duties.

On the factual data benchmark, SimpleQA, DeepSeek-V3 falls behind GPT-4o and Claude-Sonnet, primarily as a result of its design focus and resource allocation. Fortunately, early indications are that the Trump administration is contemplating further curbs on exports of Nvidia chips to China, in keeping with a Bloomberg report, with a deal with a potential ban on the H20s chips, a scaled down model for the China market. We use CoT and non-CoT methods to evaluate mannequin performance on LiveCodeBench, the place the data are collected from August 2024 to November 2024. The Codeforces dataset is measured utilizing the share of rivals. On high of them, retaining the training information and the other architectures the identical, we append a 1-depth MTP module onto them and train two models with the MTP technique for comparison. As a result of our environment friendly architectures and comprehensive engineering optimizations, DeepSeek-V3 achieves extraordinarily excessive training effectivity. Furthermore, tensor parallelism and professional parallelism techniques are incorporated to maximise effectivity.

DeepSeek V3 and R1 are massive language fashions that provide excessive performance at low pricing. Measuring massive multitask language understanding. DeepSeek differs from different language models in that it is a group of open-source massive language fashions that excel at language comprehension and versatile utility. From a more detailed perspective, we compare DeepSeek-V3-Base with the opposite open-source base models individually. Overall, DeepSeek-V3-Base comprehensively outperforms DeepSeek-V2-Base and Qwen2.5 72B Base, and surpasses LLaMA-3.1 405B Base in the majority of benchmarks, basically becoming the strongest open-supply mannequin. In Table 3, we examine the bottom mannequin of DeepSeek-V3 with the state-of-the-artwork open-supply base fashions, together with DeepSeek-V2-Base (DeepSeek-AI, 2024c) (our previous launch), Qwen2.5 72B Base (Qwen, 2024b), and LLaMA-3.1 405B Base (AI@Meta, 2024b). We evaluate all these fashions with our inside evaluation framework, and be certain that they share the same analysis setting. DeepSeek-V3 assigns extra coaching tokens to be taught Chinese information, leading to distinctive performance on the C-SimpleQA.

From the desk, we can observe that the auxiliary-loss-free strategy consistently achieves better mannequin efficiency on a lot of the evaluation benchmarks. As well as, on GPQA-Diamond, a PhD-level evaluation testbed, DeepSeek-V3 achieves outstanding results, ranking simply behind Claude 3.5 Sonnet and outperforming all different competitors by a substantial margin. As DeepSeek-V2, DeepSeek-V3 also employs further RMSNorm layers after the compressed latent vectors, and multiplies additional scaling elements at the width bottlenecks. For mathematical assessments, AIME and CNMO 2024 are evaluated with a temperature of 0.7, and the outcomes are averaged over sixteen runs, while MATH-500 employs greedy decoding. This vulnerability was highlighted in a latest Cisco examine, which found that DeepSeek failed to dam a single harmful immediate in its security assessments, including prompts associated to cybercrime and misinformation. For reasoning-associated datasets, including these focused on arithmetic, code competition issues, and logic puzzles, we generate the data by leveraging an inside DeepSeek-R1 mannequin.

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