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 drive to be reckoned with. When small Chinese artificial intelligence (AI) company DeepSeek launched a household of extremely environment friendly and extremely aggressive AI models last month, it rocked the global tech community. It achieves a powerful 91.6 F1 score within the 3-shot setting on DROP, outperforming all different models on this category. On math benchmarks, DeepSeek-V3 demonstrates exceptional efficiency, considerably surpassing baselines and setting a brand new state-of-the-artwork for non-o1-like models. DeepSeek-V3 demonstrates aggressive efficiency, standing on par with top-tier models such as LLaMA-3.1-405B, GPT-4o, and Claude-Sonnet 3.5, whereas significantly outperforming Qwen2.5 72B. Moreover, DeepSeek-V3 excels in MMLU-Pro, a extra difficult academic knowledge benchmark, the place it intently trails Claude-Sonnet 3.5. On MMLU-Redux, a refined version of MMLU with corrected labels, DeepSeek-V3 surpasses its friends. This success will be attributed to its superior data distillation technique, which successfully enhances its code era and downside-fixing capabilities in algorithm-targeted duties.

On the factual data benchmark, SimpleQA, DeepSeek-V3 falls behind GPT-4o and Claude-Sonnet, primarily due to 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, based on a Bloomberg report, with a give attention to a possible ban on the H20s chips, a scaled down model for the China market. We use CoT and non-CoT methods to evaluate model efficiency on LiveCodeBench, the place the information are collected from August 2024 to November 2024. The Codeforces dataset is measured utilizing the share of rivals. On top of them, keeping the training data and the opposite architectures the same, we append a 1-depth MTP module onto them and practice two fashions with the MTP technique for comparability. Because of our efficient architectures and comprehensive engineering optimizations, DeepSeek-V3 achieves extraordinarily excessive coaching effectivity. Furthermore, tensor parallelism and professional parallelism strategies are incorporated to maximise effectivity.

DeepSeek V3 and R1 are giant language models that offer high performance at low pricing. Measuring huge multitask language understanding. DeepSeek differs from different language fashions in that it's a collection of open-supply giant language fashions that excel at language comprehension and versatile utility. From a more detailed perspective, we compare DeepSeek-V3-Base with the opposite open-supply base fashions individually. Overall, DeepSeek-V3-Base comprehensively outperforms DeepSeek-V2-Base and Qwen2.5 72B Base, and surpasses LLaMA-3.1 405B Base in nearly all of benchmarks, primarily turning into the strongest open-supply mannequin. In Table 3, we compare the base mannequin of DeepSeek-V3 with the state-of-the-art open-supply base fashions, together with DeepSeek-V2-Base (DeepSeek-AI, 2024c) (our earlier release), Qwen2.5 72B Base (Qwen, 2024b), and LLaMA-3.1 405B Base (AI@Meta, 2024b). We consider all these models with our internal analysis framework, and ensure that they share the identical evaluation setting. DeepSeek-V3 assigns extra training tokens to study Chinese information, resulting in distinctive efficiency on the C-SimpleQA.

From the table, we can observe that the auxiliary-loss-Free Deepseek Online chat strategy persistently achieves higher model performance on a lot of the evaluation benchmarks. In addition, on GPQA-Diamond, a PhD-level evaluation testbed, DeepSeek Ai Chat-V3 achieves outstanding results, rating just behind Claude 3.5 Sonnet and outperforming all different opponents by a considerable margin. As DeepSeek-V2, DeepSeek-V3 additionally employs extra RMSNorm layers after the compressed latent vectors, and multiplies further scaling elements on the width bottlenecks. For mathematical assessments, AIME and CNMO 2024 are evaluated with a temperature of 0.7, and the outcomes are averaged over 16 runs, whereas MATH-500 employs greedy decoding. This vulnerability was highlighted in a latest Cisco research, which found that DeepSeek failed to block a single dangerous prompt in its safety assessments, including prompts related to cybercrime and misinformation. For reasoning-related datasets, including these focused on arithmetic, code competitors problems, and logic puzzles, we generate the information by leveraging an inside DeepSeek-R1 mannequin.

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