  Training large language models (LLMs) in low-resource languages such as
Hebrew poses unique challenges. In this paper, we introduce DictaLM2.0 and
DictaLM2.0-Instruct, two LLMs derived from the Mistral model, trained on a
substantial corpus of approximately 200 billion tokens in both Hebrew and
English. Adapting a pre-trained model to a new language involves specialized
techniques that differ significantly from training a model from scratch or
further training existing models on well-resourced languages such as English.
We outline these novel training methodologies, which facilitate effective
learning and adaptation to the linguistic properties of Hebrew. Additionally,
we fine-tuned DictaLM2.0-Instruct on a comprehensive instruct dataset to
enhance its performance on task-specific instructions. To rigorously evaluate
our models, we introduce a new benchmark suite for Hebrew LLM evaluation,
covering a diverse set of tasks including Question Answering, Sentiment
Analysis, Winograd Schema Challenge, Translation, and Summarization. Our work
not only addresses the intricacies of training LLMs in low-resource languages
but also proposes a framework that can be leveraged for adapting other LLMs to
various non-English languages, contributing to the broader field of
multilingual NLP.
