Hala Technical Report: Building Arabic-Centric Instruction & Translation
  Models at Scale
Authors: name: Hasan Abed Al Kader Hammoud, name: Mohammad Zbeeb, name: Bernard Ghanem
Organization: Image and Video Understanding Lab

We present Hala, a family of Arabic-centric instruction and translation
models built with our translate-and-tune pipeline. We first compress a strong
ARleftrightarrowEN teacher to FP8 (yielding sim2times higher
throughput with no quality loss) and use it to create high-fidelity bilingual
supervision. A lightweight language model LFM2-1.2B is then fine-tuned on this
data and used to translate high-quality English instruction sets into Arabic,
producing a million-scale corpus tailored to instruction following. We train
Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to
balance Arabic specialization with base-model strengths. On Arabic-centric
benchmarks, Hala achieves state-of-the-art results within both the "nano"
(leq2B) and "small" (7-9B) categories, outperforming their bases. We release
models, data, evaluation, and recipes to accelerate research in Arabic NLP.