A deep technical guide on building and training a Kimi-K2-style model using DeepSeek-V3 components. Covers the key architectural differences between Kimi-K2 and DeepSeek-V3, including aggressive MoE sparsity scaling (384 vs 256 experts) and reduced attention heads (64 vs 128) for efficient long-context inference. Introduces the MuonClip optimizer, which combines the Muon optimizer with QK-Clip — a per-head weight-clipping mechanism that prevents attention logit explosion during large-scale training. Also details training data improvements including synthetic rephrasing strategies for knowledge and math data. Includes full PyTorch implementation of Multi-Head Latent Attention with max logit tracking, the MuonClip optimizer class, and a complete training pipeline using Hugging Face Trainer.
