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English(EN) Squid: Long Context as a New Modality for Energy-Efficient On-Device Language Models

Dolphin模型为长上下文AI提供10倍能效

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的解码器-解码器架构Dolphin，旨在实现设备端语言模型中长上下文的高效处理。该方法使用一个较小的解码器将广泛的上下文提炼成内存嵌入，从而缩短主解码器的输入长度。通过将长文本视为一种独特的模态，类似于图像嵌入，Dolphin在不影响响应质量的情况下，实现了十倍的能效提升和五倍的延迟降低。该模型已在Hugging Face上公开可用，旨在在资源受限的环境中实现更复杂的AI能力。 AI

影响 通过提高长上下文处理的能效和降低延迟，在边缘设备上实现更复杂的AI能力。

排序理由 该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Dolphin模型为长上下文AI提供10倍能效

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该集群包含一篇详细介绍新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Wei Chen, Zhiyuan Li, Shuo Xin, Yihao Wang ·

    Squid：长上下文作为一种新的模态，用于高能效的设备端语言模型

    arXiv:2408.15518v3 Announce Type: replace Abstract: This paper presents Dolphin, a novel decoder-decoder architecture for energy-efficient processing of long contexts in language models. Our approach addresses the significant energy consumption and latency challenges inherent in …

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