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Nemotron-3-Nano-30B-A3B adapted for Indian languages with vocabulary expansion

By PulseAugur Editorial · [1 sources] ·

Researchers have adapted the Nemotron-3-Nano-30B-A3B model for eight Indian languages by expanding its vocabulary and performing continual pretraining. This process involved continued byte-pair encoding and AdaptMix rebalancing, which reduced tokens per word by 38.6%. While the annealed checkpoint showed a significant improvement on the Indic MILU benchmark, it experienced a decline in English MMLU performance. AI

IMPACT This research demonstrates a method for adapting large language models to new languages, potentially expanding their global applicability and accessibility.

RANK_REASON The cluster contains an academic paper detailing model adaptation and training methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Nemotron-3-Nano-30B-A3B adapted for Indian languages with vocabulary expansion

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The cluster contains an academic paper detailing model adaptation and training methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Avinash Benki, Prasanjith Pasunuti, Sutanshu Saksena Raj, Parijat Chatterjee, Nayanjit Sarkar, Daksh Maan ·

    In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B

    arXiv:2610.15373v1 Announce Type: cross Abstract: We adapted Nemotron-3-Nano-30B-A3B for eight Indian languages without changing its backbone architecture. We expanded the vocabulary in place from 131,072 to 163,072 entries using continued byte-pair encoding and AdaptMix rebalanc…

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