Table of Contents

Building and Training a Kimi-K2 Model Using DeepSeek-V3 Components

Kimi-K2 vs DeepSeek-V3: Key Architecture Differences in LLM Design

Mixture of Experts Scaling in Kimi-K2: Model Size, Sparsity, and Efficiency

Attention Head Optimization in Kimi-K2 for Efficient Long-Context LLMs

MuonClip Optimizer: Stabilizing Large-Scale LLM Training in Kimi-K2

Token Efficiency in LLM Training: Why It Matters for Kimi-K2

Attention Logit Explosion in LLMs: Training Instability and Challenges

QK-Clip: Preventing Attention Logit Explosion in Kimi-K2 Training

Training Data Optimization for Kimi-K2: Improving Token Utility in LLMs

Token Utility in LLM Training: Maximizing Learning per Token

Knowledge Data Rephrasing for LLMs: Improving Training Data Quality

Kimi-K2 Implementation: Training an Open-Source LLM with DeepSeek-V3

Multi-Head Latent Attention (MLA) with Max Logit Tracking in Kimi-K2

Implementing the MuonClip Optimizer for Stable LLM Training

Complete Kimi-K2 Training Pipeline: Setup,…