Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale

• URL: http://arxiv.org/abs/2509.14008v1
• Date: Wed, 17 Sep 2025 14:19:28 GMT
• Title: Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
• Authors: Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, Bernard Ghanem,
• Abstract summary: We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline.<n>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.<n>We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths.
• Score: 51.41777906371754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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$\leftrightarrow$EN teacher to FP8 (yielding $\sim$2$\times$ 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" ($\leq$2B) and "small" (7-9B) categories, outperforming their bases. We release models, data, evaluation, and recipes to accelerate research in Arabic NLP.

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