  Large language models have gained tremendous popularity in domains such as
e-commerce, finance, healthcare, and education. Fine-tuning is a common
approach to customize an LLM on a domain-specific dataset for a desired
downstream task. In this paper, we present a valuable resource for fine-tuning
LLMs developed for the Spanish language to perform a variety of tasks such as
classification, masked language modeling, clustering, and others. Our resource
is a collection of handwritten notary records from the seventeenth century
obtained from the National Archives of Argentina. This collection contains a
combination of original images and transcribed text (and metadata) of 160+
pages that were handwritten by two notaries, namely, Estenban Agreda de Vergara
and Nicolas de Valdivia y Brisuela nearly 400 years ago. Through empirical
evaluation, we demonstrate that our collection can be used to fine-tune Spanish
LLMs for tasks such as classification and masked language modeling, and can
outperform pre-trained Spanish models and ChatGPT-3.5/ChatGPT-4o. Our resource
will be an invaluable resource for historical text analysis and is publicly
available on GitHub.
