AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda
Authors: name: Mohd Nauman, name: Sravan Gvm, name: Vijay Devane, name: Shyam Pawar, name: Viraj Thakur, name: Kundeshwar Pundalik, name: Piyush Sawarkar, name: Rohit Saluja, name: Maunendra Desarkar, name: Ganesh Ramakrishnan
Organization: BharatGen AI

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.