@inproceedings{hammoud-etal-2026-hala,
    title = "Hala Technical Report Building {A}rabic-Centric Instruction {\&} Translation Models at Scale",
    author = "Hammoud, Hasan Abed Al Kader  and
      Zbib, Mohamad Bilal  and
      Ghanem, Bernard",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.32/",
    pages = "236--244",
    abstract = "We present HALA, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong AR{\ensuremath{\leftrightarrow}}EN teacher to FP8 (yielding {\textasciitilde}2{\texttimes} 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'' ({\ensuremath{\leq}}2B) and ``small'' (7{--}9B) categories, outperforming their bases. We are committed to release models, data, evaluation, and recipes to accelerate research in Arabic NLP."
}