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

• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

The AI race is heating up, and DeepSeek AI is positioning itself as a force to be reckoned with. When small Chinese artificial intelligence (AI) firm DeepSeek launched a family of extremely efficient and highly competitive AI models last month, it rocked the worldwide tech group. It achieves a powerful 91.6 F1 score within the 3-shot setting on DROP, outperforming all other models in this class. On math benchmarks, DeepSeek-V3 demonstrates distinctive performance, significantly surpassing baselines and setting a brand new state-of-the-art for non-o1-like fashions. DeepSeek-V3 demonstrates aggressive efficiency, standing on par with top-tier fashions such as LLaMA-3.1-405B, GPT-4o, and Claude-Sonnet 3.5, whereas considerably outperforming Qwen2.5 72B. Moreover, DeepSeek-V3 excels in MMLU-Pro, a extra difficult academic data benchmark, the place 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 may be attributed to its superior data distillation approach, which effectively enhances its code generation and problem-fixing capabilities in algorithm-targeted duties.

On the factual information benchmark, SimpleQA, DeepSeek-V3 falls behind GPT-4o and Claude-Sonnet, primarily attributable to its design focus and resource allocation. Fortunately, early indications are that the Trump administration is considering extra curbs on exports of Nvidia chips to China, in response to a Bloomberg report, with a concentrate on a potential ban on the H20s chips, a scaled down version for the China market. We use CoT and non-CoT strategies to judge model efficiency on LiveCodeBench, where the data are collected from August 2024 to November 2024. The Codeforces dataset is measured utilizing the proportion of rivals. On top of them, conserving the training data and the opposite architectures the identical, we append a 1-depth MTP module onto them and practice two models with the MTP technique for comparability. As a result of our efficient architectures and complete engineering optimizations, DeepSeek-V3 achieves extremely excessive coaching effectivity. Furthermore, tensor parallelism and professional parallelism methods are integrated to maximise efficiency.

DeepSeek V3 and R1 are large language fashions that supply excessive efficiency at low pricing. Measuring massive multitask language understanding. DeepSeek differs from different language fashions in that it is a set 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 other 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 vast majority of benchmarks, basically becoming the strongest open-source model. In Table 3, we examine the bottom model of DeepSeek-V3 with the state-of-the-artwork open-source base fashions, including DeepSeek-V2-Base (DeepSeek-AI, 2024c) (our previous release), Qwen2.5 72B Base (Qwen, 2024b), and LLaMA-3.1 405B Base (AI@Meta, 2024b). We evaluate all these fashions with our inner analysis framework, and ensure that they share the same analysis setting. Free DeepSeek online-V3 assigns extra training tokens to learn Chinese information, resulting in distinctive performance on the C-SimpleQA.

From the table, we will observe that the auxiliary-loss-free Deep seek technique consistently achieves better model efficiency on most of the analysis benchmarks. In addition, on GPQA-Diamond, a PhD-degree analysis testbed, DeepSeek-V3 achieves outstanding outcomes, ranking simply behind Claude 3.5 Sonnet and outperforming all different rivals by a substantial margin. As Deepseek free-V2, DeepSeek-V3 also employs additional RMSNorm layers after the compressed latent vectors, and multiplies additional scaling components 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 recent Cisco examine, which found that DeepSeek failed to dam a single dangerous immediate in its security assessments, together with prompts related to cybercrime and misinformation. For reasoning-related datasets, including these focused on mathematics, code competitors issues, and logic puzzles, we generate the info by leveraging an inside DeepSeek-R1 model.

If you have any concerns pertaining to where and ways to use free Deep seek, you can call us at our web-page.