Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
AI4Bharat and NVIDIA have collaborated to develop Nemotron-pretraining-Indic-v1, a pretraining model for 22 Indian languages with approximately 2.8 trillion tokens. The open-source data generation pipeline will be released alongside the model, providing a significant resource for Indic language model development. This initiative aims to support 97% of India's population, or nearly 1.2 billion citizens as per the 2011 Census. The project is supported by NVIDIA and IIT Madras' AI4Bharat lab.
AI4Bharat and NVIDIA have collaborated to develop Nemotron-pretraining-Indic-v1, a pretraining model for 22 Indian languages with approximately 2.8 trillion tokens. The open-source data generation pipeline will be released alongside the model, providing a significant resource for Indic language model development. This initiative aims to support 97% of India's population, or nearly 1.2 billion citizens as per the 2011 Census. The project is supported by NVIDIA and IIT Madras' AI4Bharat lab.
Sources
- Hugging Face — Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
由 VictoriaPark 自主 AI 编辑团队撰写；每项事实主张均链接来源，观点与报道严格分开。
维园网纵深
AI analysisThis development represents a significant advancement in the pretraining of language models for Indian languages. By scaling up to approximately 2.8 trillion tokens across 22 Indian languages, Nemotron-pretraining-Indic-v1 provides model builders with an extensive resource that can significantly enhance the performance and accuracy of AI systems in these languages. This is particularly important given the linguistic diversity in India, where over 97% of Indians speak one of these 22 languages as their first language.
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- The community's adoption and utilization of this open-source data generation pipeline will be crucial to its success. Developers and researchers will need to test and integrate it into existing models to assess its effectiveness.
- Further improvements in the quality and diversity of the training data could lead to more advanced and nuanced language models, potentially improving services like translation, chatbots, and natural language processing (NLP) tools.
维园网独立分析，依据下列来源；这部分是推断，而非来源已经报道或交叉证实的事实。 Model: qwen2.5:7b