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This document presents the development of a bilingual Hindi-English Large Language Model (LLM) named Mantra-14B, which improves performance on both languages without the need for resource-intensive techniques. The model was fine-tuned using a curated dataset of 485K samples, achieving a 3% average improvement in benchmark scores over existing models. The authors aim to enhance multilingual capabilities while maintaining native performance and provide their training code and datasets for further research on

Author

Hamza Shahid

Language

EN