Airavata: Introducing Hindi Instruction-tuned LLM
- Jay P. Gala,
- Thanmay Jayakumar,
- Jaavid Aktar Husain,
- Kumar M. Aswanth,
- Mohammed Safi Ur Rahman Khan,
- Diptesh Kanojia
- Indian Institute of Technology Madras,
- Indian Institute of Information Technology, Design and Manufacturing (Kancheepuram),
- University of Surrey,
- Agency for Science, Technology and Research (A*Star),
- National Institute Of Information And Communications Technology, Japan,
- IBM Research India
Research Output:
Other contribution
Other contribution
Open access
Publication Information
Output type
Research Output:
Other contribution
Other contribution
Original language
EnglishPublication milestones
- Published - 2024
Publication status
Published - 2024
Volume
abs/2401.15006Abstract
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additionally, we present evaluation benchmarks and a framework for assessing LLM performance across tasks in Hindi. Currently, Airavata supports Hindi, but we plan to expand this to all 22 scheduled Indic languages. You can access all artifacts at this https URL.