Read our How to Run Kimi K2.7 Code Guide! See Unsloth Dynamic 2.0 GGUFs for our quantization benchmarks. To run Kimi K2.7 Code in full precision lossless, run Q8 (UD Q8 K XL), which is 595GB and only 10GB bigger than Q4 (UD Q4 K XL). See our Kimi K2.7 Code guide for quantization analysis and instructions. Kimi K2.7 Code 1. Model Introduction Kimi K2.7 Code is a coding focused agentic model built upon Kimi K2.6. With substantial improvements on real world long horizon coding tasks, it strengthens end to end task completion across complex software engineering workflows while improving token efficiency, reducing thinking token usage by approximately 30% compared with Kimi K2.6. 2. Model Summary : : : : Architecture Mixture of Experts (MoE) Total Parameters 1T Activated Parameters 32B Number of Layers (Dense layer included) 61 Number of Dense Layers 1 Attention Hidden Dimension 7168 MoE Hidden Dimension (per Expert) 2048 Number of Attention Heads 64 Number of Experts 384 Selected Experts per Token 8 Number of Shared Experts 1 Vocabulary Size 160K Context Length 256K Attention Mechanism MLA Activation Function SwiGLU Vision Encoder MoonViT Parameters of Vision Encoder 400M 3. Evaluation…
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