FiMI: A Domain-Specific Language Model for Indian Finance Ecosystem
Authors:
Aboli Kathar,
Aman Kumar,
Anusha Kamath,
Araveeti Srujan,
Ashish Sharma,
Chandra Bhushan,
Divya Sorate,
Duddu Prasanth Kumar,
Evan Acharya,
Harsh Sharma,
Hrithik Kadam,
Kanishk Singla,
Keyur Doshi,
Kiran Praveen,
Kolisetty Krishna SK,
Krishanu Adhikary,
Lokesh MPT,
Mayurdeep Sonowal,
Nadeem Shaikh,
Navya Prakash,
Nimit Kothari,
Nitin Kukreja,
Prashant Devadiga,
Rakesh Paul,
Ratanjeet Pratap Chauhan
, et al. (15 additional authors not shown)
Abstract:
We present FiMI (Finance Model for India), a domain-specialized financial language model developed by National Payments Corporation of India (NPCI) for Indian digital payment systems. We develop two model variants: FiMI Base and FiMI Instruct. FiMI adapts the Mistral Small 24B architecture through a multi-stage training pipeline, beginning with continuous pre-training on 68 Billion tokens of curat…
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We present FiMI (Finance Model for India), a domain-specialized financial language model developed by National Payments Corporation of India (NPCI) for Indian digital payment systems. We develop two model variants: FiMI Base and FiMI Instruct. FiMI adapts the Mistral Small 24B architecture through a multi-stage training pipeline, beginning with continuous pre-training on 68 Billion tokens of curated financial, multilingual (English, Hindi, Hinglish), and synthetic data. This is followed by instruction fine-tuning and domain-specific supervised fine-tuning focused on multi-turn, tool-driven conversations that model real-world workflows, such as transaction disputes and mandate lifecycle management. Evaluations reveal that FiMI Base achieves a 20\% improvement over the Mistral Small 24B Base model on finance reasoning benchmark, while FiMI Instruct outperforms the Mistral Small 24B Instruct model by 87\% on domain-specific tool-calling. Moreover, FiMI achieves these significant domain gains while maintaining comparable performance to models of similar size on general benchmarks.
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Submitted 13 February, 2026; v1 submitted 5 February, 2026;
originally announced February 2026.