Since late 2022, Large Language Models (LLMs) have become very prominent with LLMs like ChatGPT and Bard receiving millions of users. Hundreds of new LLMs are announced each week, many of which are deposited to Hugging Face, a repository of machine learning models and datasets. To date, nearly 16,000 Text Generation models have been uploaded to the site. Given the huge influx of LLMs, it is of interest to know which LLM backbones, settings, training methods, and families are popular or trending. However, there is no comprehensive index of LLMs available. We take advantage of the relatively systematic nomenclature of Hugging Face LLMs to perform hierarchical clustering and identify communities amongst LLMs using n-grams and term frequency-inverse document frequency. Our methods successfully identify families of LLMs and accurately cluster LLMs into meaningful subgroups. We present a public web application to navigate and explore Constellation, our atlas of 15,821 LLMs. Constellation rapidly generates a variety of visualizations, namely dendrograms, graphs, word clouds, and scatter plots. Constellation is available at the following link: https://constellation.sites.stanford.edu/.

翻译：自2022年底以来，大型语言模型（LLMs）变得极为突出，如ChatGPT和Bard等大语言模型获得了数百万用户。每周都有数百个新LLM被发布，其中许多被提交至Hugging Face——一个机器学习模型与数据集的存储库。截至目前，已有近16,000个文本生成模型被上传至该网站。鉴于LLM的大量涌入，了解哪些LLM主干架构、设置、训练方法和系列流行或趋势化具有重要意义。然而，目前尚无全面的LLM索引可用。我们利用Hugging Face LLM相对系统的命名规则，通过n-gram和词频-逆文档频率执行层次聚类并识别LLM之间的社群。我们的方法成功识别了LLM系列，并将LLM准确聚类为有意义的子群。我们展示了一个公开的Web应用程序，用于导航和探索Constellation——我们包含15,821个LLM的图谱。Constellation能够快速生成多种可视化结果，包括树状图、关系图、词云和散点图。Constellation可通过以下链接访问：https://constellation.sites.stanford.edu/。