The number of open source language models that can produce Turkish is
increasing day by day, as in other languages. In order to create the basic
versions of such models, the training of multilingual models is usually
continued with Turkish corpora. The alternative is to train the model with only
Turkish corpora. In this study, we first introduce the cosmosGPT models that we
created with this alternative method. Then, we introduce new finetune datasets
for basic language models to fulfill user requests and new evaluation datasets
for measuring the capabilities of Turkish language models. Finally, a
comprehensive comparison of the adapted Turkish language models on different
capabilities is presented. The results show that the language models we built
with the monolingual corpus have promising performance despite being about 10
times smaller than the others.

通过用纯土耳其语语料库训练建立的 cosmosGPT 模型和适应土耳其语的语言模型的全面比较，研究结果显示，尽管相较于其他模型，我们用单语料库建立的语言模型规模较小约 10 倍，但其表现仍然有可观的性能。