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 drive to be reckoned with. When small Chinese artificial intelligence (AI) company DeepSeek released a family of extremely efficient and highly competitive AI models last month, it rocked the global tech community. It achieves a formidable 91.6 F1 rating within the 3-shot setting on DROP, outperforming all different fashions in this category. On math benchmarks, DeepSeek-V3 demonstrates distinctive efficiency, considerably surpassing baselines and setting a brand new state-of-the-art for non-o1-like models. DeepSeek-V3 demonstrates competitive efficiency, standing on par with high-tier models akin to 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 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 peers. This success might be attributed to its advanced data distillation method, which successfully enhances its code technology and downside-fixing capabilities in algorithm-focused duties.

On the factual knowledge 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 contemplating additional curbs on exports of Nvidia chips to China, in accordance with a Bloomberg report, with a deal with a potential ban on the H20s chips, a scaled down version for the China market. We use CoT and non-CoT strategies to guage mannequin performance on LiveCodeBench, the place the data are collected from August 2024 to November 2024. The Codeforces dataset is measured using the share of competitors. On top of them, holding the training information and the other architectures the same, we append a 1-depth MTP module onto them and practice two models with the MTP strategy for comparison. Due to our environment friendly architectures and complete engineering optimizations, Deepseek Online chat online-V3 achieves extraordinarily excessive training efficiency. Furthermore, tensor parallelism and professional parallelism methods are incorporated to maximize efficiency.

DeepSeek V3 and R1 are massive language models that offer high performance at low pricing. Measuring large multitask language understanding. DeepSeek differs from different language fashions in that it is a set of open-source giant language fashions that excel at language comprehension and versatile software. From a extra detailed perspective, we evaluate Deepseek Online chat-V3-Base with the opposite open-supply 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, essentially becoming the strongest open-supply mannequin. In Table 3, we compare the bottom model of DeepSeek-V3 with the state-of-the-artwork open-source 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 consider all these models with our internal analysis framework, and make sure that they share the identical analysis setting. DeepSeek-V3 assigns extra training tokens to study Chinese data, resulting in distinctive performance on the C-SimpleQA.

From the desk, we can observe that the auxiliary-loss-free strategy persistently achieves better mannequin efficiency on a lot of the evaluation benchmarks. In addition, on GPQA-Diamond, a PhD-stage analysis testbed, DeepSeek-V3 achieves remarkable results, ranking just behind Claude 3.5 Sonnet and outperforming all different competitors by a substantial margin. As DeepSeek-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 sixteen runs, whereas MATH-500 employs greedy decoding. This vulnerability was highlighted in a latest Cisco research, which discovered that DeepSeek failed to block a single dangerous immediate in its security assessments, together with prompts associated to cybercrime and misinformation. For reasoning-associated datasets, including these centered on mathematics, code competition issues, and logic puzzles, we generate the information by leveraging an inner DeepSeek-R1 model.