BharatGen is set to launch Param2 17B at the India AI Summit

2026-02-13 · Source: AIM Network · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

BharatGen is set to release Param 2, a 17-billion parameter Mixture of Experts (MoE) model, aiming to provide "AI independence" for India. This initiative addresses the lack of culturally relevant AI tools, which often recommend non-Indian specific solutions for health or daily life. Param 2 is designed for accessibility and lower inference costs, supporting 22 Indian languages despite challenges like script diversity, dialect variance, and data scarcity. BharatGen has amassed over 20 trillion tokens and 3 petabytes of data through on-the-ground efforts, partnerships with heritage groups for digitization, and synthetic data generation. The model's development includes safeguards for quality and bias mitigation, with early testing showing improved learning for Hindi-adjacent languages. BharatGen plans to release Param 2 on Hugging Face and AI Coach, providing pre-trained checkpoints, benchmarks, datasets, and research papers to foster a robust Indian AI ecosystem.

Key takeaway

For Machine Learning Engineers and NLP Engineers building for India's diverse user base, prioritize developing models that are culturally and linguistically relevant, rather than merely adapting Western models. Focus on data sourcing strategies for low-resource languages and consider Mixture of Experts architectures to ensure cost-effective deployment, making AI accessible and inclusive for the next billion users.

Key insights

India is pursuing "AI independence" through culturally relevant, cost-efficient, multilingual models like BharatGen's Param 2.

Principles

• AI models must be culturally relevant.
• Cost accessibility drives AI adoption.
• Data scarcity requires active sourcing.

Method

BharatGen builds multilingual MoE models by actively sourcing diverse Indian language data, partnering with heritage groups for digitization, and generating synthetic data, alongside developing models for data classification and filtering.

In practice

• Utilize MoE architectures for cost-efficient inference.
• Engage industry and government early for application alignment.
• Publish models and research openly to foster ecosystem growth.

Topics

• BharatGen Param 2
• AI Sovereignty
• Multilingual AI
• Mixture of Experts
• Digital Public Infrastructure

Best for: Machine Learning Engineer, NLP Engineer, CTO, AI Engineer, AI Product Manager, Entrepreneur

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Editorial summary, takeaway, and curation by AIssential. Original article published by AIM Network.