# Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline

Published: 2026-10-16 · Updated: 2026-10-16 · Category: Open Source

Source page: https://www.sipply.dev/story/scaling-indic-pretraining-2-8-trillion-tokens-and-an-open-1975a251 · 中文: https://www.sipply.dev/zh/story/scaling-indic-pretraining-2-8-trillion-tokens-and-an-open-1975a251

## Summary

NVIDIA and IIT Madras have collaborated to create a groundbreaking pretraining corpus named Nemotron-pretraining-Indic-v1, which consists of around 2.8 trillion tokens across 22 different Indian languages. This substantial dataset is designed to improve the accessibility and quality of training data for Indic language models, addressing a significant gap in resources for these languages. Additionally, the initiative includes an open data generation pipeline, enabling developers to generate customized datasets tailored to their specific needs, thereby fostering innovation and research in the field of natural language processing for Indic languages.

## Key takeaways

- The new dataset contains 2.8 trillion tokens, significantly expanding the resources available for Indic language models.
- An open data generation pipeline allows developers to create tailored datasets, promoting customization and flexibility.
- This initiative aims to enhance the representation of Indian languages in AI applications and research.

## Why it matters

This development is crucial for advancing AI capabilities in underrepresented languages, ensuring that technology is more inclusive and accessible to diverse populations.

- **Product managers:** For product managers, this initiative opens up new opportunities to develop products that cater to a wider audience by leveraging high-quality datasets for Indic languages.
- **Developers:** Developers can utilize the open data generation pipeline to create specialized datasets, enhancing their projects and contributing to the growth of NLP applications in Indic languages.
- **Students and job seekers:** Students and job-seekers can benefit from this initiative by gaining access to rich datasets for research and projects, positioning themselves at the forefront of advancements in AI for Indic languages.

## Sources

- [Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline](https://huggingface.co/blog/nvidia/scaling-indic-pretraining) (Hugging Face Blog, 2026-10-16)

## 中文

### 扩展印度语言预训练：2.8万亿个标记和开放数据生成管道

NVIDIA与印度理工学院马德拉斯合作创建了一个开创性的预训练语料库，名为Nemotron-pretraining-Indic-v1，包含约2.8万亿个标记，涵盖22种不同的印度语言。该数据集旨在改善印度语言模型的训练数据的可获取性和质量，填补这些语言资源的重大空白。此外，该项目还包括一个开放数据生成管道，使开发者能够生成符合其特定需求的定制数据集，从而促进印度语言自然语言处理领域的创新和研究。

**要点：**

- 新数据集包含2.8万亿个标记，显著扩展了印度语言模型的资源。
- 开放数据生成管道允许开发者创建定制数据集，促进个性化和灵活性。
- 该倡议旨在增强印度语言在AI应用和研究中的表现。

**为什么重要：** 这一发展对于提升在代表性不足语言中的AI能力至关重要，确保技术对多样化人群更加包容和可获取。

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