  Large language models (LLMs) have received a lot of attention in natural
language processing (NLP) research because of their exceptional performance in
understanding and generating human languages. However, low-resource languages
are left behind due to the unavailability of resources. In this work, we focus
on enhancing the LLaMA-2-Amharic model by integrating task-specific and
generative datasets to improve language model performance for Amharic. We
compile an Amharic instruction fine-tuning dataset and fine-tuned
LLaMA-2-Amharic model. The fine-tuned model shows promising results in
different NLP tasks. We open-source our dataset creation pipeline, instruction
datasets, trained models, and evaluation outputs to promote language-specific
studies on these models.
