Pretrained language models underpin several AI applications, but their high
computational cost for training limits accessibility. Initiatives such as BLOOM
and StarCoder aim to democratize access to pretrained models for collaborative
community development. However, such existing models face challenges: limited
multilingual capabilities, continual pretraining causing catastrophic
forgetting, whereas pretraining from scratch is computationally expensive, and
compliance with AI safety and development laws. This paper presents Aurora-M, a
15B parameter multilingual open-source model trained on English, Finnish,
Hindi, Japanese, Vietnamese, and code. Continually pretrained from
StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion
tokens in total training token count. It is the first open-source multilingual
model fine-tuned on human-reviewed safety instructions, thus aligning its
development not only with conventional red-teaming considerations, but also
with the specific concerns articulated in the Biden-Harris Executive Order on
the Safe, Secure, and Trustworthy Development and Use of Artificial
Intelligence. Aurora-M is rigorously evaluated across various tasks and
languages, demonstrating robustness against catastrophic forgetting and
outperforming alternatives in multilingual settings, particularly in safety
evaluations. To promote responsible open-source LLM development, Aurora-M and
its variants are released at
this https URL .

Aurora-M is a multilingual open-source pretrained language model that addresses challenges such as limited multilingual capabilities and catastrophic forgetting, surpassing 2 trillion training tokens. It aligns with safety considerations and outperforms alternatives in multilingual settings.