Our evaluation results exhibit that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, notably within the domains of code, mathematics, and reasoning. 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, basically turning into the strongest open-source mannequin. We leverage pipeline parallelism to deploy completely different layers of a model on totally different GPUs, and for every layer, the routed experts might be uniformly deployed on 64 GPUs belonging to eight nodes. Each MoE layer consists of 1 shared professional and 256 routed specialists, where the intermediate hidden dimension of each expert is 2048. Among the many routed specialists, eight consultants can be activated for every token, and each token might be ensured to be despatched to at most 4 nodes. At the big scale, we train a baseline MoE model comprising 228.7B complete parameters on 540B tokens. At the small scale, we train a baseline MoE mannequin comprising 15.7B whole parameters on 1.33T tokens. POSTSUPERSCRIPT to 64. We substitute all FFNs aside from the primary three layers with MoE layers. As DeepSeek-V2, DeepSeek-V3 additionally employs additional RMSNorm layers after the compressed latent vectors, and multiplies additional scaling elements on the width bottlenecks.

As well as, compared with DeepSeek-V2, the brand new pretokenizer introduces tokens that combine punctuations and line breaks. The pretokenizer and training data for our tokenizer are modified to optimize multilingual compression efficiency. Finally, the training corpus for DeepSeek-V3 consists of 14.8T high-high quality and diverse tokens in our tokenizer. The tokenizer for DeepSeek-V3 employs Byte-stage BPE (Shibata et al., 1999) with an extended vocabulary of 128K tokens. Standardized exams embody AGIEval (Zhong et al., 2023). Note that AGIEval consists of both English and Chinese subsets. Reference disambiguation datasets include CLUEWSC (Xu et al., 2020) and WinoGrande Sakaguchi et al. Following our previous work (DeepSeek-AI, 2024b, c), we undertake perplexity-based mostly analysis for datasets together with HellaSwag, PIQA, WinoGrande, RACE-Middle, RACE-High, MMLU, MMLU-Redux, MMLU-Pro, MMMLU, ARC-Easy, ARC-Challenge, C-Eval, CMMLU, C3, and CCPM, and adopt technology-based analysis for TriviaQA, NaturalQuestions, DROP, MATH, GSM8K, MGSM, HumanEval, MBPP, LiveCodeBench-Base, CRUXEval, BBH, AGIEval, CLUEWSC, CMRC, and CMath. Reading comprehension datasets include RACE Lai et al. Thank you for reading! 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 models with the MTP strategy for comparison.

As well as, we perform language-modeling-primarily based evaluation for Pile-check and use Bits-Per-Byte (BPB) as the metric to ensure truthful comparison among models using totally different tokenizers. Note that as a result of adjustments in our evaluation framework over the previous months, the performance of DeepSeek-V2-Base exhibits a slight distinction from our previously reported outder our coaching framework and infrastructures, training DeepSeek-V3 on every trillion tokens requires only 180K H800 GPU hours, which is much cheaper than training 72B or 405B dense fashions. In Table 3, we examine the bottom mannequin 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 consider all these models with our internal evaluation framework, and make sure that they share the identical analysis setting. POSTSUPERSCRIPT till the mannequin consumes 10T training tokens. 0.Three for the first 10T tokens, and to 0.1 for the remaining 4.8T tokens.

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