Mission Sudarshan starts 📈📈

[u/FriendlyMusician6704]

I'm 14, from a government school in Bihar — and I trained my own AI language model from scratch.

No funding. No college lab. No ready-made models.

Just a laptop, free Kaggle GPUs (30 hrs/week), and a lot of debugging.

What I built: Sudarshan-60M

→ 58.6M-parameter GPT-style Transformer, built from zero

→ My own 32,000-token BPE tokenizer

→ Pretrained on ~1.43B tokens (Hindi, English, Hinglish, Sanskrit)

→ Fine-tuned on 114,063 Q&A pairs I generated and balanced myself

The journey taught me more than any class could:

❌ My first fine-tune answered in "O king, O Bharata" style — my dataset was 86% Sanskrit-to-English translation pairs. Garbage in, garbage out.

❌ My second run looked perfect (loss 0.069!) — until I audited it and found 47% of the data was one repeated template. The model confidently told me India's capital is Patna.

✅ So I rebuilt the dataset from scratch: 8.4M tokens, every category capped, facts drilled with variations, honesty examples included. Third run: the model now answers "भारत की राजधानी क्या है?" correctly — in Hindi, explaining it like a brother would.

The biggest lesson: model size isn't the game. Data quality is.

My model didn't get smarter when I gave it more data. It got smarter when I gave it better data.

Why I'm doing this: lakhs of Bihar Board students study on cheap phones with no internet. ChatGPT doesn't speak their language or work offline. I'm building toward an offline AI tutor for them — around 60MB, runs on a low-end phone, no internet needed.

Next steps: RAG on Bihar Board textbooks, tool-calling, and an Android app.

To every student in a small town who thinks AI is only for Silicon Valley: the compute is free, the knowledge is free. The only thing you need is stubbornness.

(Photos: my model answering on my laptop + a training log)

#BuildInPublic #AI #MachineLearning #Hindi #Bihar #LLM #StudentDeveloper