RAM 评分:衡量模型下载表现的新指标

Nathan Lambert · @natolambert · X·2026-08-20 02:33·15天前
AI 导读

Nathan Lambert 与 xeophon 为 ATOM 工作设计了 RAM 评分,用于衡量模型在 HuggingFace 上的下载表现。该指标计算模型在特定时间点(如 30 天)的累计下载量,与其所在参数量级别历史前 10 名模型下载量的比值。得分超过 1 意味着该模型有望成为该级别下载量前 10 的模型,对 50B+ 参数模型最具参考意义。

Nathan Lambert@natolambert
36AI 编辑部评分,满分 100

RAM 评分:衡量模型下载表现的新指标

2026-08-20 02:33· 15天前
AI 导读

Nathan Lambert 与 xeophon 为 ATOM 工作设计了 RAM 评分,用于衡量模型在 HuggingFace 上的下载表现。该指标计算模型在特定时间点(如 30 天)的累计下载量,与其所在参数量级别历史前 10 名模型下载量的比值。得分超过 1 意味着该模型有望成为该级别下载量前 10 的模型,对 50B+ 参数模型最具参考意义。

I think the RAM score @xeophon and I made for ATOM work is a really solid metric. If I just rank by the top RAM scores @ 30days within our artifacts curation, all the models are really solid.

RAM score @ t= (model's cumulative downloads) @ t / (top-10 downloads for size class) @ t

RAM is essentially looking at a model's downloads at specific points in time (this case t is 30 days), relative to the downloads of the top 10 models of all time in that size bracket. A score of above 1 means it's on track to be a top 10 downloaded model at that size category. Imo most meaningful for 50B+ models.

Downloads alone normally isn't great on HuggingFace, but it's rare a large MoE gets a ton of adoption without being a solid model.