Show HN: I built a tiny LLM to demystify how language models work
Why it matters: This project offers an accessible, low-resource method for developers to understand and customize LLM mechanics.
- A developer built a ~9M parameter LLM from scratch to understand how language models function.
- The LLM uses a vanilla transformer architecture and was trained on 60,000 synthetic conversations.
- The project is implemented in approximately 130 lines of PyTorch and trains in 5 minutes on a free Colab T4.
- Users can fork the project to swap the LLM's personality for their own character.
A developer created a compact, 9-million parameter Large Language Model (LLM) from scratch using a vanilla transformer architecture and 60,000 synthetic conversations, aiming to demystify the inner workings of these complex systems. This tiny LLM, built with only 130 lines of PyTorch, trains rapidly in just five minutes on a free Colab T4, offering an accessible tool for others to explore and customize its 'personality.'
