  Large Language Models (LLMs) like GPT-4 and LLaMA have shown incredible
proficiency at natural language processing tasks and have even begun to excel
at tasks across other modalities such as vision and audio. Despite their
success, LLMs often struggle to perform well on low-resource languages because
there is so little training data available. This shortcoming is especially
prevalent with open source models. In this work, we explore training LLaMA-2 to
speak Amharic, a language which is spoken by over 50 million people world wide,
but has orders of magnitude less data available than languages like English. We
employ methods previously used for training LLMs on other languages with data
scarcity, and use open source translation models to perform data augmentation
and grow our dataset from millions of tokens to billions. We further enhance
the capabilities of our model by connecting an image encoder and training on a
translated visual instruction tuning dataset in the same manner as LLaVA,
resulting in a multimodal Amharic LLM that can understand images along with
text. We introduce an Amharic version of a popular benchmarking dataset to
evaluate our work. Our models and dataset are open sourced and available on
GitHub.
