  Large language models (LLMs) excel in many tasks in NLP and beyond, but most
open models have very limited coverage of smaller languages and LLM work tends
to focus on languages where nearly unlimited data is available for pretraining.
In this work, we study the challenges of creating LLMs for Finnish, a language
spoken by less than 0.1% of the world population. We compile an extensive
dataset of Finnish combining web crawls, news, social media and eBooks. We
pursue two approaches to pretrain models: 1) we train seven monolingual models
from scratch (186M to 13B parameters) dubbed FinGPT, 2) we continue the
pretraining of the multilingual BLOOM model on a mix of its original training
data and Finnish, resulting in a 176 billion parameter model we call BLUUMI.
For model evaluation, we introduce FIN-bench, a version of BIG-bench with
Finnish tasks. We also assess other model qualities such as toxicity and bias.
Our models and tools are openly available at https://turkunlp.org/gpt3-finnish.
