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English(EN) DiffusionGemma Technical Report

DiffusionGemma 语言模型实现每秒 1,500 个 token 的速度

研究人员推出了 DiffusionGemma,这是一款专为高速文本生成设计的实验性开放权重语言模型。与顺序处理 token 的传统自回归模型不同,DiffusionGemma 使用离散扩散并行优化 256 个 token 的块。该模型源自 Gemma 4 混合专家模型,在单个 NVIDIA H100 GPU 上可实现约每秒 1,500 个输出 token 的速度,显著优于传统方法。 AI

影响 为生成速度和模型能力建立了新的帕累托前沿,有可能加速需要快速文本输出的 AI 应用。

排序理由 在 arXiv 上发布了详细介绍新型实验性语言模型的技朧报告。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

DiffusionGemma 语言模型实现每秒 1,500 个 token 的速度

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在 arXiv 上发布了详细介绍新型实验性语言模型的技朧报告。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · DiffusionGemma Team, Adrien Ali Ta\"iga, James Assiene, Daniele Calandriello, Rahma Chaabouni, Jo\~ao Gante, Tamara von Glehn, Nate Keating, Chris Knutsen, Martin Kukla, Tianlin Liu, Ivan Lobov, Ofir Nabati, Jo\~ao Gabriel Oliveira, Nicolas Perez-Nieves,… ·

    DiffusionGemma 技术报告

    arXiv:2608.00146v1 Announce Type: new Abstract: We introduce DiffusionGemma, an experimental open-weight language model that uses discrete diffusion to generate text at exceptionally high speed. Rather than decoding one token at a time, DiffusionGemma iteratively refines blocks o…

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

    DiffusionGemma 技术报告

    We introduce DiffusionGemma, an experimental open-weight language model that uses discrete diffusion to generate text at exceptionally high speed. Rather than decoding one token at a time, DiffusionGemma iteratively refines blocks of 256 tokens in parallel, avoiding the sequentia…