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

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) firm DeepSeek launched a family of extremely efficient and extremely competitive AI models final month, it rocked the worldwide tech community. It achieves a powerful 91.6 F1 score within the 3-shot setting on DROP, outperforming all other models on this category. On math benchmarks, DeepSeek-V3 demonstrates exceptional performance, considerably surpassing baselines and setting a brand new state-of-the-art for non-o1-like models. DeepSeek-V3 demonstrates aggressive efficiency, standing on par with top-tier models akin to 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 more difficult instructional information benchmark, the place it closely trails Claude-Sonnet 3.5. On MMLU-Redux, a refined version of MMLU with corrected labels, DeepSeek-V3 surpasses its friends. This success may be attributed to its superior knowledge distillation approach, which successfully enhances its code technology and problem-solving capabilities in algorithm-focused tasks.

On the factual information benchmark, SimpleQA, DeepSeek-V3 falls behind GPT-4o and Claude-Sonnet, primarily because of its design focus and resource allocation. Fortunately, early indications are that the Trump administration is considering further curbs on exports of Nvidia chips to China, DeepSeek according to 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 judge mannequin performance on LiveCodeBench, where the information are collected from August 2024 to November 2024. The Codeforces dataset is measured utilizing the proportion of opponents. On high of them, preserving the coaching information and the opposite architectures the same, we append a 1-depth MTP module onto them and train two fashions with the MTP technique for comparison. Attributable to our efficient architectures and complete engineering optimizations, DeepSeek-V3 achieves extremely high training effectivity. Furthermore, tensor parallelism and expert parallelism strategies are integrated to maximize effectivity.

DeepSeek V3 and R1 are giant language fashions that offer high performance at low pricing. Measuring large multitask language understanding. DeepSeek differs from other language fashions in that it's a collection of open-source giant language fashions that excel at language comprehension and versatile software. From a more detailed perspective, we evaluate DeepSeek-V3-Base with the opposite 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 majority of benchmarks, essentially changing into the strongest open-supply mannequin. In Table 3, we evaluate the bottom mannequin of DeepSeek-V3 with the state-of-the-art open-supply base fashions, together with DeepSeek-V2-Base (DeepSeek-AI, 2024c) (our earlier launch), Qwen2.5 72B Base (Qwen, 2024b), and LLaMA-3.1 405B Base (AI@Meta, 2024b). We evaluate all these models with our internal analysis framework, and ensure that they share the identical evaluation setting. DeepSeek-V3 assigns more coaching tokens to be taught Chinese data, leading to distinctive efficiency on the C-SimpleQA.

From the table, we are able to observe that the auxiliary-loss-Free DeepSeek Chat strategy persistently achieves higher model efficiency on many of the analysis benchmarks. As well as, on GPQA-Diamond, a PhD-degree evaluation testbed, DeepSeek-V3 achieves exceptional results, rating just behind Claude 3.5 Sonnet and outperforming all other competitors by a substantial margin. As DeepSeek-V2, DeepSeek-V3 also employs extra RMSNorm layers after the compressed latent vectors, and multiplies further scaling components 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, whereas MATH-500 employs greedy decoding. This vulnerability was highlighted in a current Cisco examine, which discovered that DeepSeek failed to block a single harmful prompt in its safety assessments, including prompts associated to cybercrime and misinformation. For reasoning-related datasets, together with those centered on arithmetic, code competitors problems, and logic puzzles, we generate the info by leveraging an internal DeepSeek-R1 mannequin.