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Tiny Transformers: Making Generative AI Work on Phones

Learn how Tiny Transformers are enabling generative NLP in Indian mobile apps using model compression, quantization, and Agentic AI frameworks. Ideal for developers and learners in generative AI training.

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Tiny Transformers: Making Generative AI Work on Phones

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  1. Tiny Transformers: NLP Compression for Indian Mobile Apps Enabling Generative AI for the Indian Edge

  2. Why Tiny Transformers Matter in India - Budget smartphones dominate - Real-time AI needs in healthcare, edtech, retail - Cloud dependency is costly & unreliable

  3. What Are Tiny Transformers? - Compressed NLP models - Fast, low memory, mobile-ready - Examples: DistilBERT, TinyBERT, MiniLM, ALBERT

  4. Model Compression Techniques - Quantization: Reduce precision - Distillation: Student learns from teacher - Pruning & parameter sharing

  5. Indian Use Cases of Tiny Transformers - Chatbots in local languages - Voice-to-text for farmers - EdTech summarizers - Retail review analysis

  6. Agentic AI + Tiny Transformers - Local agents that reason & act - Use compressed models on-device - Rural healthcare, tutoring, kiosks

  7. Developer’s Roadmap - Pick light models (T5-small, DistilBERT) - Compress using ONNX or TFLite - Fine-tune on Indian data (IndicNLP, AI4Bharat)

  8. Learning & Certification Paths - Join Generative AI training programs - Look for model deployment + Agentic AI modules - AI course in Bangalore or online options

  9. Conclusion - Tiny Transformers - Inclusive NLP - Make apps faster, cheaper, local - India’s mobile-first future depends on model efficiency

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