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English(EN) ProxyFormer: A Dual-Stream Proxy Architecture for Ultra-Long Context and High-Resolution Generation

ProxyFormer架构赋能超长上下文语言模型

研究人员开发了ProxyFormer,这是一种新颖的双流架构,旨在克服处理超长上下文和高分辨率数据在语言模型中的计算限制。该架构使用代理令牌压缩细粒度的局部特征,从而在缩减的空间中实现昂贵的全局交互。这种方法能够显著训练更长的序列长度,其中一个模型展示了以高检索精度处理多达1,048,576个令牌的能力。ProxyFormer在初步的图像生成实验中也显示出潜力。 AI

影响 ProxyFormer的架构可以显著降低处理长上下文的计算成本,可能为需要广泛数据分析的领域带来新应用。

排序理由 该集群描述了一篇研究论文中提出的新架构。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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

ProxyFormer架构赋能超长上下文语言模型

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Tool
该集群描述了一篇研究论文中提出的新架构。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    ProxyFormer:一种用于超长上下文和高分辨率生成的双流代理架构

    The quadratic growth of attention computation and key-value (KV) cache with respect to sequence length is a central bottleneck for ultra-long-context language models and high-resolution generative models. We propose ProxyFormer, a general dual-stream architecture built upon proxy…