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 power to be reckoned with. When small Chinese synthetic intelligence (AI) company DeepSeek launched a family of extraordinarily efficient and highly competitive AI fashions final month, it rocked the global tech community. It achieves an impressive 91.6 F1 score within the 3-shot setting on DROP, outperforming all other models 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 models. DeepSeek-V3 demonstrates competitive performance, standing on par with high-tier models reminiscent of LLaMA-3.1-405B, GPT-4o, and Claude-Sonnet 3.5, while considerably outperforming Qwen2.5 72B. Moreover, DeepSeek-V3 excels in MMLU-Pro, a extra difficult educational knowledge benchmark, where it intently trails Claude-Sonnet 3.5. On MMLU-Redux, a refined model of MMLU with corrected labels, DeepSeek-V3 surpasses its peers. This success can be attributed to its superior data distillation approach, which effectively enhances its code technology and downside-fixing capabilities in algorithm-focused tasks.

On the factual data benchmark, SimpleQA, DeepSeek-V3 falls behind GPT-4o and Claude-Sonnet, primarily resulting from its design focus and useful resource allocation. Fortunately, early indications are that the Trump administration is contemplating additional curbs on exports of Nvidia chips to China, in line with 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 strategies to guage model efficiency on LiveCodeBench, the place the information are collected from August 2024 to November 2024. The Codeforces dataset is measured utilizing the proportion of rivals. On high of them, retaining the coaching information and the other architectures the identical, we append a 1-depth MTP module onto them and train two fashions with the MTP strategy for comparability. As a result of our environment friendly architectures and comprehensive engineering optimizations, DeepSeek-V3 achieves extraordinarily high coaching efficiency. Furthermore, tensor parallelism and expert parallelism methods are incorporated to maximise effectivity.

DeepSeek V3 and R1 are giant language fashions that provide excessive efficiency at low pricing. Measuring massive multitask language understanding. DeepSeek differs from different language fashions in that it's a collection of open-source giant language models that excel at language comprehension and versatile utility. From a more detailed perspective, we evaluate DeepSeek-V3-Base with the other open-source 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 the vast majority of benchmarks, basically turning into the strongest open-supply mannequin. In Table 3, we evaluate the bottom mannequin of DeepSeek-V3 with the state-of-the-artwork open-source base models, 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 evaluate all these fashions with our inside evaluation framework, and be certain that they share the same evaluation setting. DeepSeek-V3 assigns extra training tokens to study Chinese data, leading to distinctive performance on the C-SimpleQA.

From the desk, we are able to observe that the auxiliary-loss-Free DeepSeek Chat strategy constantly achieves better mannequin efficiency on most of the evaluation benchmarks. In addition, on GPQA-Diamond, a PhD-level analysis testbed, DeepSeek-V3 achieves outstanding results, rating just behind Claude 3.5 Sonnet and outperforming all different opponents by a substantial margin. As DeepSeek-V2, DeepSeek-V3 also employs further RMSNorm layers after the compressed latent vectors, and multiplies further scaling factors on the width bottlenecks. For mathematical assessments, AIME and CNMO 2024 are evaluated with a temperature of 0.7, and the results are averaged over 16 runs, while MATH-500 employs greedy decoding. This vulnerability was highlighted in a recent Cisco research, which discovered that DeepSeek failed to block a single dangerous immediate in its security assessments, including prompts related to cybercrime and misinformation. For reasoning-associated datasets, including those focused on arithmetic, code competitors issues, and logic puzzles, we generate the info by leveraging an internal DeepSeek-R1 mannequin.

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