We fit a 24M-parameter LLM into 15MB with per-row MSE quantization

Working on OpenAI's Parameter Golf challenge (train best LLM possible, must fit in 16MB). Hit Top-3 on the leaderboard. The quantization trick: instead of fixed-percentile INT8 clipping, we search 5 clip values per weight row and keep whichever gives lowest reconstruction MSE. Costs 5x quantization time (~0.7s total), gives measurable BPB improvement.