Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
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arXiv
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| Format: | Preprint |
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2025
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| author | Hammoud, Hasan Abed Al Kader Zbeeb, Mohammad Ghanem, Bernard |
| author_facet | Hammoud, Hasan Abed Al Kader Zbeeb, Mohammad Ghanem, Bernard |
| contents | 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$2B) and "small" (7-9B) categories, outperforming their bases. We release models, data, evaluation, and recipes to accelerate research in Arabic NLP. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_14008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale Hammoud, Hasan Abed Al Kader Zbeeb, Mohammad Ghanem, Bernard Computation and Language Artificial Intelligence Machine Learning We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong AR$\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. |
| title | Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.14008 |