How to Build Your Own Language-Specific LLM
SMRTR summary
A software engineer decided to build an AI chatbot from scratch, in Urdu, and document every step. The result is a remarkably detailed technical walkthrough that strips away the mystery behind large language models like ChatGPT and GPT-4.
The project covers the full pipeline: scraping and cleaning raw Urdu text, training a custom tokenizer, building a transformer neural network from the ground up, and deploying a working chatbot online, all using free tools like Google Colab and Hugging Face.
What makes this particularly compelling is the honesty about limitations. The finished model, at just 23 million parameters, hallucinates frequently and stumbles on questions far from its training data. But that's almost the point. As the engineer puts it, the goal was never to replicate ChatGPT. It was to understand, concretely and hands-on, why building a truly capable LLM requires massive datasets, months of computing time, and resources most of us simply don't have.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
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