  The swift advancement and widespread availability of foundational Large
Language Models (LLMs), complemented by robust fine-tuning methodologies, have
catalyzed their adaptation for innovative and industrious applications.
Enabling LLMs to recognize and interpret geospatial data, while offering a
linguistic access to vast cartographic datasets, is of significant importance.
OpenStreetMap (OSM) is the most ambitious open-source global initiative
offering detailed urban and rural geographic data, curated by a community of
over 10 million contributors, which constitutes a great potential for LLM
applications. In this study, we demonstrate the proof of concept and details of
the process of fine-tuning a relatively small scale (1B parameters) LLM with a
relatively small artificial dataset curated by a more capable teacher model, in
order to provide a linguistic interface to the OSM data of an arbitrary urban
region. Through this interface, users can inquire about a location's
attributes, covering a wide spectrum of concepts, such as its touristic appeal
or the potential profitability of various businesses in that vicinity. The
study aims to provide an initial guideline for such generative artificial
intelligence (AI) adaptations and demonstrate early signs of useful emerging
abilities in this context even in minimal computational settings. The
embeddings of artificially curated prompts including OSM data are also
investigated in detail, which might be instrumental for potential geospatially
aware urban Retrieval Augmented Generation (RAG) applications.
