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English(EN) Scaling Categorical Flow Maps

Apple ML Research 扩展流图以实现更快的语言生成

作者 PulseAugur 编辑部 · [1 个来源] ·

Apple Machine Learning Research 发表了一篇论文，详细介绍了在语言建模中扩展分类流图 (CFM) 的进展。研究团队成功地在 2.1 万亿个 token 上训练了一个 17 亿参数的流模型，并将其提炼成一个 CFM，该 CFM 能够在仅 4 个推理步骤内生成多样化的高质量文本。这种方法保持了接近数据级别的 token 熵，并有望用于离散数据生成，解决了先前在可扩展性方面存在的局限性。 AI

影响 这项研究可能带来更高效、更快速的文本生成模型，并可能影响语言模型的训练和部署方式。

排序理由 发表了一篇详细介绍语言建模新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Apple Machine Learning Research 阅读 →

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

Apple ML Research 扩展流图以实现更快的语言生成

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发表了一篇详细介绍语言建模新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    Scaling Categorical Flow Maps

    Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they unlock a host of advantages currently reserved for continuous modalities, including accelerated sampling and tilting. Recently, s…

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