The blog post explores MuonClip, an advanced optimization technique designed to address the issue of attention score explosions in large-scale transformer training, particularly within the Kimi-K2 model. As transformers scale to handle billions of parameters and trillions of tokens, traditional optimizers like AdamW struggle with stability, leading to problems such as NaNs and gradient issues. MuonClip enhances the Muon optimizer by implementing a mechanism called qk-clip, which resizes query and key weights (W_q and W_k) to prevent attention scores from becoming excessively large, thereby maintaining stability during training. This method retains the efficiency and balance of Muon's updates while ensuring that attention scores remain within a manageable range, effectively preventing training crashes. Interactive visualizations and examples are provided to illustrate how MuonClip operates in practice, highlighting its ability to democratize large-model training by making it more accessible to startups, thus enabling them to manage big datasets without the computational pitfalls associated with traditional methods.