Sign up to save your podcasts
Or
Share 【第114期】DeepSeek V3技术报告
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
【第114期】DeepSeek V3技术报告
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：DeepSeek-V3 Technical ReportSummary
The document details DeepSeek-V3, a 671B-parameter Mixture-of-Experts large language model. It covers the model's architecture, including Multi-Head Latent Attention and an innovative auxiliary-loss-free load balancing strategy for DeepSeekMoE. The training process, encompassing pre-training on 14.8 trillion tokens and post-training using supervised fine-tuning and reinforcement learning, is described. Extensive evaluations demonstrate DeepSeek-V3's strong performance across various benchmarks, surpassing many open-source and achieving results comparable to leading closed-source models. Finally, the document explores infrastructure optimizations, including an FP8 mixed-precision framework, and suggests improvements for future AI hardware design.
本文详细介绍了DeepSeek-V3，一种拥有6710亿参数的专家混合（Mixture-of-Experts）大型语言模型。内容涵盖了模型架构，包括多头潜在注意力（Multi-Head Latent Attention）以及针对DeepSeekMoE设计的创新无辅助损失负载平衡策略。文中描述了训练过程，包括对14.8万亿标记的预训练，以及通过监督微调和强化学习的后训练。广泛的评估表明，DeepSeek-V3 在多个基准测试中表现强劲，超越了许多开源模型，并达到与领先的闭源模型相当的水平。最后，文章探讨了基础设施优化，包括FP8混合精度框架，并提出了对未来AI硬件设计的改进建议。
原文链接：https://arxiv.org/abs/2412.19437
View all episodes
By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：DeepSeek-V3 Technical ReportSummary
The document details DeepSeek-V3, a 671B-parameter Mixture-of-Experts large language model. It covers the model's architecture, including Multi-Head Latent Attention and an innovative auxiliary-loss-free load balancing strategy for DeepSeekMoE. The training process, encompassing pre-training on 14.8 trillion tokens and post-training using supervised fine-tuning and reinforcement learning, is described. Extensive evaluations demonstrate DeepSeek-V3's strong performance across various benchmarks, surpassing many open-source and achieving results comparable to leading closed-source models. Finally, the document explores infrastructure optimizations, including an FP8 mixed-precision framework, and suggests improvements for future AI hardware design.
本文详细介绍了DeepSeek-V3，一种拥有6710亿参数的专家混合（Mixture-of-Experts）大型语言模型。内容涵盖了模型架构，包括多头潜在注意力（Multi-Head Latent Attention）以及针对DeepSeekMoE设计的创新无辅助损失负载平衡策略。文中描述了训练过程，包括对14.8万亿标记的预训练，以及通过监督微调和强化学习的后训练。广泛的评估表明，DeepSeek-V3 在多个基准测试中表现强劲，超越了许多开源模型，并达到与领先的闭源模型相当的水平。最后，文章探讨了基础设施优化，包括FP8混合精度框架，并提出了对未来AI硬件设计的改进建议。
原文链接：https://arxiv.org/abs/2412.19437