  In this technical report, we present three novel datasets of Kotlin code:
KStack, KStack-clean, and KExercises. We also describe the results of
fine-tuning CodeLlama and DeepSeek models on this data. Additionally, we
present a version of the HumanEval benchmark rewritten by human experts into
Kotlin - both the solutions and the tests. Our results demonstrate that small,
high-quality datasets (KStack-clean and KExercises) can significantly improve
model performance on code generation tasks, achieving up to a 16-point increase
in pass rate on the HumanEval benchmark. Lastly, we discuss potential future
work in the field of improving language modeling for Kotlin, including the use
of static analysis tools in the learning process and the introduction of more
intricate and realistic benchmarks.
