How to Verify if an “Indigenous” LLM is Truly Built in India?

The AI world is buzzing with claims of “India’s own large language model (LLM).” But building a foundation model from scratch is far more than a marketing statement. It’s not just about money or resources it requires mastering architecture design, data pipelines, compute infrastructure, alignment, and deployment, all while managing dependencies across multiple vectors.

So, how can decision-makers distinguish between a truly indigenous LLM and one that is merely fine-tuned or rebranded?

Key Triggers to Question Legitimacy

• Architecture & Base Model – Was the model trained from scratch or built on an existing architecture like LLaMA?

• Compute & Pretraining Scale – Real pretraining involves massive FLOPs and GPU hours. If details are vague, it’s likely not scratch-built.

• Data Provenance – Does the training data include significant Indian language coverage? How was it cleaned and curated?

• Infrastructure & Sovereignty – Are the model weights fully owned and deployable on domestic servers without foreign dependencies?

• Alignment & Safety – Was the RLHF or SFT pipeline executed in-house? Are preference datasets auditable?

• Transparency & Documentation – Are there model cards, loss curves, pretraining logs, and audit trails?

Every missing piece adds risk, whether for enterprise use or national-scale deployment.

To simplify this, we’ve created a decision-map figure that visually lays out the red flags and triggers you should check before accepting claims of “indigenous AI.”

Building a true foundational model is hard, expensive, and complex. Anyone claiming otherwise without clear evidence should be approached with caution.

Technical Questions to Verify an “Indigenous” LLM

Architecture & Base Model

1. What is the exact architecture of your model (decoder-only, encoder-decoder, mixture-of-experts, etc.)?

2. Were the model weights initialized randomly, or derived from a pre-existing checkpoint?

3. What positional encoding method and tokenizer did you implement?

4. Vocabulary size and Indic language coverage?

5. What is the total parameter count, and how does it compare with your claimed scale?

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Pretraining Scale & Compute

6. How many tokens were used for pretraining?

7. What was the total compute spent (GPU-hours or FLOPs)?

8. What optimizer, learning rate schedule, and batch size did you use?

9. What was the final pretraining loss and perplexity?

10. Did you encounter gradient instabilities, and how were they addressed?

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Data Provenance

11. What were the main sources of your training data?

12. What percentage of data is in Indian languages vs global content?

13. How did you clean, deduplicate, and filter the corpus?

14. Were any proprietary or foreign datasets used?

15. How did you handle low-resource Indic languages?

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Infrastructure & Deployment

16. Was training done on-premise or cloud? Which provider and hardware?

17. Can the model run fully air-gapped?

18. Who owns the final weights? Are there any licensing restrictions?

19. Are inference servers hosted domestically?

20. Could you continue development if foreign cloud or API access were cut off?

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Alignment & Safety

21. Was supervised fine-tuning (SFT) used? RLHF or DPO?

22. Size and composition of the preference dataset?

23. Was alignment multilingual, especially in Indian languages?

24. How is the safety layer implemented — baked in or separate classifier?

25. Any audit trails or documentation for alignment choices?

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Transparency & Validation

26. Can you provide pretraining logs, loss curves, and checkpoints?

27. Which benchmarks were used to evaluate performance?

28. How does it compare with publicly known models (e.g., LLaMA, GPT)?

29. Hallucination rate and language-specific performance metrics?

30. Are model cards and audit reports available?

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Interpretation Tips for Decision-Makers

• Precise answers + data + logs → likely genuine.

• Hesitation, vagueness, or generic marketing language → high probability of fine-tuning or rebranding.

• Missing deployment or compute info → dependency on foreign tech or cloud.

Machine Learning Paradigms: From Learning to Unlearning

Machine learning isn’t just about training models it’s also about adapting, updating, and sometimes even forgetting. Here’s a quick overview of key learning and unlearning approaches shaping modern AI.

1. Exact Unlearning

Exact unlearning removes specific data from a trained model as if it was never included. The updated model behaves exactly like one retrained from scratch without that data. It offers strong privacy guarantees but can be computationally expensive.

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2. Approximate Unlearning

Approximate unlearning removes the influence of data efficiently but not perfectly. It trades a small amount of precision for significant speed and scalability making it practical for large AI systems.

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3. Online Learning

Online learning updates the model continuously as new data arrives. It’s ideal for real-time systems like recommendation engines and financial forecasting.

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4. Incremental Learning

Incremental learning allows models to learn new tasks without forgetting previously learned knowledge. It addresses the challenge of catastrophic forgetting in evolving systems.

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5. Transfer Learning

Transfer learning reuses knowledge from one task to improve performance on another. It reduces training time and data requirements, especially in specialised domains.

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6. Federated Learning

Federated learning trains models across decentralised devices without sharing raw data. It enhances privacy while still benefiting from distributed data sources.

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7. Supervised Learning

Supervised learning uses labeled data to train models for classification and regression tasks. It’s the most widely used learning approach in industry.

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8. Unsupervised Learning

Unsupervised learning discovers hidden patterns in unlabeled data. Common applications include clustering and dimensionality reduction.

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9. Reinforcement Learning

Reinforcement learning trains agents through rewards and penalties. It powers game AI, robotics, and autonomous decision-making systems.

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10. Active Learning

Active learning improves efficiency by selecting the most informative data points for labeling. It reduces annotation costs while maintaining performance.

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11. Self-Supervised Learning

Self-supervised learning generates labels from the data itself. It has become foundational in modern large language and vision models.

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Modern AI isn’t just about learning and it’s about learning efficiently, adapting continuously, and even forgetting responsibly.

Can Quantum Computers “Undelete” Today’s Data?

1.    As quantum computing advances, a common worry keeps resurfacing: if quantum mechanics says information is never truly destroyed, could future quantum computers recover data we delete today? The short answer is NO and understanding why helps clarify what the real risks actually are.

2.    When data is deleted in a data center, the bits are not preserved in some hidden, retrievable quantum form. Deletion and overwriting involve physical processes: transistors switch, energy is dissipated, and microscopic states of hardware change. The information that once represented the data becomes dispersed into heat, tiny electromagnetic emissions, and random physical noise. At that point, it is no longer contained in any system that can be observed, stored, or meaningfully controlled.

3.    Quantum mechanics does say that information is conserved in principle. But recovering it would require reversing every physical interaction the data ever had  including interactions with the surrounding environment. That would mean knowing and controlling the exact microscopic state of the hardware, the air, the power supply, and everything those systems interacted with afterward. This is not a problem of computation. It is a problem of reality. Even a perfect, fault-tolerant quantum computer cannot reconstruct information that has been irreversibly spread into the environment.

4.    So where does the real quantum risk lie? Not in undeleting erased data, but in breaking encryption. Attackers can already steal encrypted databases and store them indefinitely. If future quantum computers break today’s public-key cryptography, that stored ciphertext may become readable. In that case, the data was never truly gone , it was just locked.

5.    This is why modern security focuses on cryptography, not physics. Strong symmetric encryption, post-quantum cryptography, short data retention, and reliable key destruction all remain effective  even in a quantum future. Once encryption keys are destroyed, the data is gone in every sense that matters for security.

6.    Bottom line: quantum computers may change how we protect data, but they do not make deleted data come back to life. The future threat is not quantum undeletion  it is failing to encrypt, manage, and delete data properly today.