挖掘模型仓库中的隐藏瑰宝

摘要

公共代码库托管着数百万微调模型，但社区使用量仍不成比例地集中在少数基础模型上。本研究探讨这种集中现象究竟反映了有效的市场选择，还是存在系统性忽略优质模型的情况。通过对2000多个模型的大规模评估，我们发现了"隐藏瑰宝"现象——部分冷门微调模型的表现显著优于热门模型。以Llama-3.1-8B系列为例，某些罕见下载的检查点在不增加推理成本的情况下，将数学推理能力从83.2%提升至96.0%。然而通过穷举评估每个上传模型来发现优质模型在计算上是不可行的。为此，我们将模型发现问题建模为多臂老虎机问题，通过共享查询集和激进淘汰机制加速序列二分搜索算法。我们的方法仅需对每个候选模型进行50次查询即可定位最优模型，实现超过50倍的发现效率提升。

English

Public repositories host millions of fine-tuned models, yet community usage remains disproportionately concentrated on a small number of foundation checkpoints. We investigate whether this concentration reflects efficient market selection or if superior models are systematically overlooked. Through an extensive evaluation of over 2,000 models, we show the prevalence of "hidden gems", unpopular fine-tunes that significantly outperform their popular counterparts. Notably, within the Llama-3.1-8B family, we find rarely downloaded checkpoints that improve math performance from 83.2% to 96.0% without increasing inference costs. However, discovering these models through exhaustive evaluation of every uploaded model is computationally infeasible. We therefore formulate model discovery as a Multi-Armed Bandit problem and accelerate the Sequential Halving search algorithm by using shared query sets and aggressive elimination schedules. Our method retrieves top models with as few as 50 queries per candidate, accelerating discovery by over 50x.

挖掘模型仓库中的隐藏瑰宝

Discovering Hidden Gems in Model Repositories

摘要

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