Hala Technical Report: Building Arabic-Centric Instruction & Translation
  Models at Scale
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.
68cbb58d5a7803ff3be42ecc
Hasan Abed Al Kader Hammoud
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2025-09-19T13:11:31.736Z
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Hasan Abed Al Kader Hammoud
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hammh0a
68cbb58d5a7803ff3be42ecd
Mohammad Zbeeb
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2025-09-18T13:25:47.410Z
665ee96b9f29909d03abfa37
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Mohammad Zbeeb
user
zbeeb
68cbb58d5a7803ff3be42ece
Bernard Ghanem
Hala Technical Report: Building Arabic-Centric Instruction & Translation
  Models at Scale
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.
Hala, a family of Arabic-centric instruction and translation models, achieves state-of-the-art results using a translate-and-tune pipeline, slerp merging, and fine-tuning on high-quality bilingual supervision.
translate-and-tune pipeline FP8 bilingual supervision lightweight language model fine-tuning slerp merging Arabic-centric benchmarks nano category small category