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.

尽管大型语言模型的类型不断扩大，但荷兰语模型仍存在显著差距，该研究提供了一小步来改善这一状况，引入两个经过微调的 Llama 2 13B 模型变种，使用荷兰特定网络爬取数据进行微调，随后在多个合成指令和聊天数据集上进一步优化模型，同时提供数据集和模型权重，并提供一个评估指标来追踪（荷兰）模型在多个生成任务上的性能，包括一些先进模型的结果，最后对需要推进荷兰语模型以及整个模型生态系统所需的关键点进行了关键性结论。