  Despite the rapid expansion of types of large language models, there remains
a notable gap in models specifically designed for the Dutch language. This gap
is not only a shortage in terms of pretrained Dutch models but also in terms of
data, and benchmarks and leaderboards. This work provides a small step to
improve the situation. First, we introduce two fine-tuned variants of the Llama
2 13B model. We first fine-tuned Llama 2 using Dutch-specific web-crawled data
and subsequently refined this model further on multiple synthetic instruction
and chat datasets. These datasets as well as the model weights are made
available. In addition, we provide a leaderboard to keep track of the
performance of (Dutch) models on a number of generation tasks, and we include
results of a number of state-of-the-art models, including our own. Finally we
provide a critical conclusion on what we believe is needed to push forward
Dutch language models and the whole eco-system around the models.
