AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda
Current large language models excel at broad, general-purpose tasks, but
consistently underperform when exposed to highly specialized domains that
require deep cultural, linguistic, and subject-matter expertise. In particular,
traditional medical systems such as Ayurveda embody centuries of nuanced
textual and clinical knowledge that mainstream LLMs fail to accurately
interpret or apply. We introduce AyurParam-2.9B, a domain-specialized,
bilingual language model fine-tuned from Param-1-2.9B using an extensive,
expertly curated Ayurveda dataset spanning classical texts and clinical
guidance. AyurParam's dataset incorporates context-aware, reasoning, and
objective-style Q&A in both English and Hindi, with rigorous annotation
protocols for factual precision and instructional clarity. Benchmarked on
BhashaBench-Ayur, AyurParam not only surpasses all open-source
instruction-tuned models in its size class (1.5--3B parameters), but also
demonstrates competitive or superior performance compared to much larger
models. The results from AyurParam highlight the necessity for authentic domain
adaptation and high-quality supervision in delivering reliable, culturally
congruent AI for specialized medical knowledge.
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Mohd Nauman
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2025-11-13T13:07:19.395Z
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Mohd Nauman
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user
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Sravan Gvm
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Vijay Devane
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2025-11-05T19:56:53.345Z
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Vijay Devane
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user
vjdevane
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Shyam Pawar
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Viraj Thakur
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Kundeshwar Pundalik
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Piyush Sawarkar
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Rohit Saluja
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Maunendra Desarkar
690b9bbf60494e4fa7675594
Ganesh Ramakrishnan
AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda
Current large language models excel at broad, general-purpose tasks, but
consistently underperform when exposed to highly specialized domains that
require deep cultural, linguistic, and subject-matter expertise. In particular,
traditional medical systems such as Ayurveda embody centuries of nuanced
textual and clinical knowledge that mainstream LLMs fail to accurately
interpret or apply. We introduce AyurParam-2.9B, a domain-specialized,
bilingual language model fine-tuned from Param-1-2.9B using an extensive,
expertly curated Ayurveda dataset spanning classical texts and clinical
guidance. AyurParam's dataset incorporates context-aware, reasoning, and
objective-style Q&A in both English and Hindi, with rigorous annotation
protocols for factual precision and instructional clarity. Benchmarked on
BhashaBench-Ayur, AyurParam not only surpasses all open-source
instruction-tuned models in its size class (1.5--3B parameters), but also
demonstrates competitive or superior performance compared to much larger
models. The results from AyurParam highlight the necessity for authentic domain
adaptation and high-quality supervision in delivering reliable, culturally
congruent AI for specialized medical knowledge.
AyurParam-2.9B, a domain-specialized bilingual language model fine-tuned for Ayurveda, outperforms other models in its size class and demonstrates competitive performance on specialized medical knowledge tasks.
domain-specialized bilingual language model fine-tuned Ayurveda dataset context-aware reasoning objective-style Q&A BhashaBench-Ayur domain adaptation high-quality supervision