  The development of domain-specific language models has significantly advanced
natural language processing applications in various specialized fields,
particularly in biomedicine. However, the focus has largely been on
English-language models, leaving a gap for less-resourced languages such as
Italian. This paper introduces Igea, the first decoder-only language model
designed explicitly for biomedical text generation in Italian. Built on the
Minerva model and continually pretrained on a diverse corpus of Italian medical
texts, Igea is available in three model sizes: 350 million, 1 billion, and 3
billion parameters. The models aim to balance computational efficiency and
performance, addressing the challenges of managing the peculiarities of medical
terminology in Italian. We evaluate Igea using a mix of in-domain biomedical
corpora and general-purpose benchmarks, highlighting its efficacy and retention
of general knowledge even after the domain-specific training. This paper
discusses the model's development and evaluation, providing a foundation for
future advancements in Italian biomedical NLP.
