@inproceedings{singh-etal-2026-nanda,
title = "Nanda Family: Open-Weights Generative Large Language Models for {H}indi",
author = "Singh, Aaryamonvikram and
Banerjee, Debopriyo and
Sahnan, Dhruv and
Choudhury, Monojit and
Chauhan, Shivam and
Das, Rocktim Jyoti and
Han, Xudong and
Li, Haonan and
Jadhav, Alok Anil and
Agarwal, Utkarsh and
Choudhary, Mukund and
Koto, Fajri and
Bhat, Junaid Hamid and
Shukla, Awantika and
Ghosh, Samujjwal and
Kamboj, Samta and
Pandit, Onkar and
Pradhan, Lalit and
Pal, Rahul and
Sahu, Sunil Kumar and
Mullah, Parvez and
El Filali, Ali and
Quraishi, Zainul Abedien Ahmed and
Sengupta, Neha and
Ramakrishnan, Gokulakrishnan and
Joshi, Rituraj and
Gosal, Gurpreet and
Sheinin, Avraham and
Vassilieva, Natalia and
Nakov, Preslav",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.288/",
pages = "6086--6108",
ISBN = "979-8-89176-380-7",
abstract = "Large language models remain predominantly English-centric, which limits their utility for underrepresented languages. We help bridge this gap for Hindi with Llama-3-Nanda-10B-Chat (aka Nanda-10B) and Llama-3.1-Nanda-87B-Chat (aka Nanda-87B), forming the Nanda family of open-weight bilingual models (https://github.com/MBZUAI-IFM/Nanda-Family). Our approach integrates: (i) a tokenizer extending Llama{'}s vocabulary with 20{\%} Hindi-specific tokens, thus halving Hindi tokenization fertility while preserving English efficiency, (ii) Hindi-first parameter-efficient continual pretraining using Llama Pro on a 65B-token corpus spanning Devanagari script, code-mixed, and Romanized Hindi, and (iii) bilingual instruction and safety alignment on a large culturally grounded dataset. The resulting Nanda models outperform open-weight LLMs of comparable size: Nanda-87B yields high generative quality, and Nanda-10B shows competitive general-purpose performance. Nanda-87B demonstrates state-of-the-art performance on summarization, translation, transliteration, and instruction following. Moreover, both models achieve state-of-the-art performance in safety and in cultural knowledge. Our results demonstrate that careful tokenizer design, data curation, and continual pretraining can yield capable and safe LLMs for resource-poor languages without compromising English performance."
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<abstract>Large language models remain predominantly English-centric, which limits their utility for underrepresented languages. We help bridge this gap for Hindi with Llama-3-Nanda-10B-Chat (aka Nanda-10B) and Llama-3.1-Nanda-87B-Chat (aka Nanda-87B), forming the Nanda family of open-weight bilingual models (https://github.com/MBZUAI-IFM/Nanda-Family). Our approach integrates: (i) a tokenizer extending Llama’s vocabulary with 20% Hindi-specific tokens, thus halving Hindi tokenization fertility while preserving English efficiency, (ii) Hindi-first parameter-efficient continual pretraining using Llama Pro on a 65B-token corpus spanning Devanagari script, code-mixed, and Romanized Hindi, and (iii) bilingual instruction and safety alignment on a large culturally grounded dataset. The resulting Nanda models outperform open-weight LLMs of comparable size: Nanda-87B yields high generative quality, and Nanda-10B shows competitive general-purpose performance. Nanda-87B demonstrates state-of-the-art performance on summarization, translation, transliteration, and instruction following. Moreover, both models achieve state-of-the-art performance in safety and in cultural knowledge. Our results demonstrate that careful tokenizer design, data curation, and continual pretraining can yield capable and safe LLMs for resource-poor languages without compromising English performance.</abstract>
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%0 Conference Proceedings
%T Nanda Family: Open-Weights Generative Large Language Models for Hindi
%A Singh, Aaryamonvikram
%A Banerjee, Debopriyo
%A Sahnan, Dhruv
%A Choudhury, Monojit
%A Chauhan, Shivam
%A Das, Rocktim Jyoti
%A Han, Xudong
%A Li, Haonan
%A Jadhav, Alok Anil
%A Agarwal, Utkarsh
%A Choudhary, Mukund
%A Koto, Fajri
%A Bhat, Junaid Hamid
%A Shukla, Awantika
%A Ghosh, Samujjwal
%A Kamboj, Samta
%A Pandit, Onkar
%A Pradhan, Lalit
%A Pal, Rahul
%A Sahu, Sunil Kumar
%A Mullah, Parvez
%A El Filali, Ali
%A Quraishi, Zainul Abedien Ahmed
%A Sengupta, Neha
%A Ramakrishnan, Gokulakrishnan
%A Joshi, Rituraj
%A Gosal, Gurpreet
%A Sheinin, Avraham
%A Vassilieva, Natalia
%A Nakov, Preslav
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F singh-etal-2026-nanda
%X Large language models remain predominantly English-centric, which limits their utility for underrepresented languages. We help bridge this gap for Hindi with Llama-3-Nanda-10B-Chat (aka Nanda-10B) and Llama-3.1-Nanda-87B-Chat (aka Nanda-87B), forming the Nanda family of open-weight bilingual models (https://github.com/MBZUAI-IFM/Nanda-Family). Our approach integrates: (i) a tokenizer extending Llama’s vocabulary with 20% Hindi-specific tokens, thus halving Hindi tokenization fertility while preserving English efficiency, (ii) Hindi-first parameter-efficient continual pretraining using Llama Pro on a 65B-token corpus spanning Devanagari script, code-mixed, and Romanized Hindi, and (iii) bilingual instruction and safety alignment on a large culturally grounded dataset. The resulting Nanda models outperform open-weight LLMs of comparable size: Nanda-87B yields high generative quality, and Nanda-10B shows competitive general-purpose performance. Nanda-87B demonstrates state-of-the-art performance on summarization, translation, transliteration, and instruction following. Moreover, both models achieve state-of-the-art performance in safety and in cultural knowledge. Our results demonstrate that careful tokenizer design, data curation, and continual pretraining can yield capable and safe LLMs for resource-poor languages without compromising English performance.
%U https://aclanthology.org/2026.eacl-long.288/
%P 6086-6108
Markdown (Informal)
[Nanda Family: Open-Weights Generative Large Language Models for Hindi](https://aclanthology.org/2026.eacl-long.288/) (Singh et al., EACL 2026)
ACL
- Aaryamonvikram Singh, Debopriyo Banerjee, Dhruv Sahnan, Monojit Choudhury, Shivam Chauhan, Rocktim Jyoti Das, Xudong Han, Haonan Li, Alok Anil Jadhav, Utkarsh Agarwal, Mukund Choudhary, Fajri Koto, Junaid Hamid Bhat, Awantika Shukla, Samujjwal Ghosh, Samta Kamboj, Onkar Pandit, Lalit Pradhan, Rahul Pal, Sunil Kumar Sahu, Parvez Mullah, Ali El Filali, Zainul Abedien Ahmed Quraishi, Neha Sengupta, Gokulakrishnan Ramakrishnan, Rituraj Joshi, Gurpreet Gosal, Avraham Sheinin, Natalia Vassilieva, and Preslav Nakov. 2026. Nanda Family: Open-Weights Generative Large Language Models for Hindi. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6086–6108, Rabat, Morocco. Association for Computational Linguistics.