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Train the smallest LM you can that fits in 16MB. Best model wins!
5,124 starsPython
GolfStudent v2 - 24M-param LLM in 15MB using GPTQ-lite + Muon
by whitestone1121·Mar 25, 2026·1 point·0 comments
AI Analysis
●●SolidBig BrainWizardry
24M params in 15MB using GPTQ-lite and Muon optimizer for OpenAI's Parameter Golf challenge.
Strengths
- •Aggressive constraint optimization combining GPTQ-lite quantization with Muon+EMA.
- •Schedule-Free optimization and value residuals push parameter efficiency boundaries.
- •Part of OpenAI-sponsored challenge with $1M compute credits for participants.
Weaknesses
- •Challenge PR submission, not a standalone usable product or library.
- •Niche appeal limited to ML researchers interested in parameter-constrained training.
Category
Target Audience
ML researchers, model optimization engineers, constraint optimization enthusiasts
Similar To
NanoGPT Speedrun · Parameter Golf Challenge
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