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Nanda Family: Open-Weights Generative Large Language Models for Hindi

Paper · 2026 · Aaryamonvikram Singh, Debopriyo Banerjee, Dhruv Sahnan et al.

Summary

Most LLMs are built mainly around English. This leaves Hindi underserved, even though it is one of the most widely spoken languages. Hindi also lacks large, clean corpora and culturally aware models. The authors present the Nanda family, two open-weight bilingual Hindi–English chat models: Nanda-10B, based on Llama-3-8B, and Nanda-87B, based on Llama-3.1-70B.

The training recipe has four parts:

  • Corpus: a 65B-token Hindi collection covering Devanagari, Romanized and Hindi–English code-mixed text, cleaned with a dedicated pipeline.
  • Tokenizer: the Llama-3 vocabulary, extended with frequent Hindi tokens.
  • Continual pretraining: done with Llama Pro, which keeps the original layers frozen and adds new decoder blocks.
  • Alignment: bilingual instruction tuning plus safety tuning on culturally grounded data.

Compared with open-weight models of similar size, Nanda-87B leads on generative tasks such as summarization, translation, transliteration and instruction following. Nanda-10B is a strong general-purpose model. Both score best on safety and cultural knowledge and match other models on MCQ benchmarks. The work shows that a careful tokenizer, data curation and block expansion can adapt LLMs to lower-resource languages without hurting English.

Key points

  • Tokenizer: Llama-3-ExtVocab20 adds 20% Hindi tokens (vocabulary 153,856). It cuts Hindi fertility from 2.61 to 1.19 and leaves English unchanged at 1.35. Adding 30% gave almost no further gain.
  • Data pipeline: standardization, filtering, cleaning, re-filtering and LSH fuzzy deduplication. Sources include HPLT and the IIT-Bombay English–Hindi Parallel Corpus.
  • Architecture: Llama Pro expansion to 40 blocks (Nanda-10B) and 100 blocks (Nanda-87B). New token embeddings are initialized from the 5 most similar base tokens, found with text-embedding-3-large (Wechsel-style).
  • Alignment data: a culturally grounded safety-tuning set of about 200K examples. Machine-translated instruction data was checked by humans.
  • System prompt results (Nanda-87B, nanda-basic): BLEU 45.62 on the internal test set and 35.8 on Flores; ROUGE-LSum 46.76 internal and 23.16 on CrossSum. Safety scores reach roughly 95–96% with the other prompt variants.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates124 works · 5,676 citations🔬Inception Institute of Artificial IntelligenceAbu Dhabi · United Arab Emirates6 works · 292 citations🏢Cerebras SystemsSunnyvale · United States4 works · 294 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as 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. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.288

@inproceedings{singh2026nanda,
  title = {{Nanda Family: Open-Weights Generative Large Language Models for Hindi}},
  author = {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},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.288},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.288}
}

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