  Large language models (LLMs) have gained popularity recently due to their
outstanding performance in various downstream Natural Language Processing (NLP)
tasks. However, low-resource languages are still lagging behind current
state-of-the-art (SOTA) developments in the field of NLP due to insufficient
resources to train LLMs. Ethiopian languages exhibit remarkable linguistic
diversity, encompassing a wide array of scripts, and are imbued with profound
religious and cultural significance. This paper introduces EthioLLM --
multilingual large language models for five Ethiopian languages (Amharic,
Ge'ez, Afan Oromo, Somali, and Tigrinya) and English, and Ethiobenchmark -- a
new benchmark dataset for various downstream NLP tasks. We evaluate the
performance of these models across five downstream NLP tasks. We open-source
our multilingual language models, new benchmark datasets for various downstream
tasks, and task-specific fine-tuned language models and discuss the performance
of the models. Our dataset and models are available at the
https://huggingface.co/EthioNLP repository.
