Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi

• URL: http://arxiv.org/abs/2603.03508v1
• Date: Tue, 03 Mar 2026 20:31:25 GMT
• Title: Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi
• Authors: Shiza Fatimah, Aniket Sen, Sophia Falk, Florian Mai, Lucie Flek, Nicholas Kluge Corrêa,
• Abstract summary: LilMoo is a 0.6-billion- parameter Hindi language model trained entirely from scratch.<n>It is developed through a fully transparent and reproducible pipeline optimized for limited compute environments.<n>Across comprehensive evaluation suites, LilMoo consistently outperforms comparably sized multilingual baselines.
• Score: 9.65814816271915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The dominance of large multilingual foundation models has widened linguistic inequalities in Natural Language Processing (NLP), often leaving low-resource languages underrepresented. This paper introduces LilMoo, a 0.6-billion-parameter Hindi language model trained entirely from scratch to address this gap. Unlike prior Hindi models that rely on continual pretraining from opaque multilingual foundations, LilMoo is developed through a fully transparent and reproducible pipeline optimized for limited compute environments. We construct a high-quality Hindi corpus (GigaLekh) filtered through both heuristic and learned (LLM-as-a-judge) methods, complemented by bilingual augmentation with curated English data. Using this dataset, we explore various training recipes for small-scale language models. Across comprehensive evaluation suites, LilMoo consistently outperforms comparably sized multilingual baselines such as Qwen2.5-0.5B and Qwen3-0.6B, demonstrating that well-designed language-specific pretraining can rival large multilingual models at the sub-billion-parameter range.

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