I saw what big companies and research labs were doing at massive scale and tried to adapt those ideas to extreme compression in tiny models. Here’s what happened. When OpenAI launched the Parameter Golf challenge, the rules were brutal: train a small language model that must fit inside a 16 megabyte compressed file and finish training in just 10 minutes on powerful hardware. Most participants focused on proven techniques that were already working on the leaderboard. I took a different approach. I read papers and articles about what large companies and research labs were doing at massive scale and tried to adapt those concepts to the extreme constraints of this challenge. The Experiments I Tried Aggressive Int4 Quantization Inspired by frontier quantization research from big labs showing that very low-bit weights could work in larger models, I pushed hard on Int4. I believed that if I could make aggressive 4-bit quantization stable in a tiny model, it would give me a massive space advantage.…