  "Bigger the better" has been the predominant trend in recent Large Language
Models (LLMs) development. However, LLMs do not suit well for scenarios that
require on-device processing, energy efficiency, low memory footprint, and
response efficiency. These requisites are crucial for privacy, security, and
sustainable deployment. This paper explores the "less is more" paradigm by
addressing the challenge of designing accurate yet efficient Small Language
Models (SLMs) for resource constrained devices. Our primary contribution is the
introduction of an accurate and fully transparent open-source 0.5 billion
(0.5B) parameter SLM, named MobiLlama, catering to the specific needs of
resource-constrained computing with an emphasis on enhanced performance with
reduced resource demands. MobiLlama is a SLM design that initiates from a
larger model and applies a careful parameter sharing scheme to reduce both the
pre-training and the deployment cost. Our work strives to not only bridge the
gap in open-source SLMs but also ensures full transparency, where complete
training data pipeline, training code, model weights, and over 300 checkpoints
along with evaluation codes is available at :
https://github.com/mbzuai-oryx/MobiLlama.
