{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Daily Paper Cast","title":"Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale","html":"","width":"100%","height":180,"duration":1296,"description":"\n 🤗 Upvotes: 67 | cs.CL, cs.AI, cs.LG\n\n Authors:\n Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, Bernard Ghanem\n\n Title:\n Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale\n\n Arxiv:\n http://arxiv.org/abs/2509.14008v1\n\n Abstract:\n 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.\n ","thumbnail_url":"https://img.transistorcdn.com/8lOVNnuwhrA3rxrDMv7Osu4j_t1-jORooO6NfGcQhcw/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Zjg1/YzRhODczMDU4MmE4/OGMwN2FiNDlmYzI2/MDliMi5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}