Kimi

Miles recipes for the Moonshot family — Kimi K2.6 / K2.5 (multimodal, 1 T / 32 B-A) and Kimi K2 / K2-Thinking.

Miles supports Moonshot’s MoE line from top to bottom. The latest Kimi K2.6 and K2.5 are natively multimodal agentic models at 1 T total / 32 B active per token, and the text-only Kimi K2 (Instruct and Thinking variants) runs at the same 1 T / 32 B scale. K2-Thinking is the canonical INT4 QAT target, and the K2.5 / K2.6 recipe trains an INT4 actor under the same QAT path.

Variants

Model	Active / Total	HF ID	Recipe
Kimi-K2.6	32 B / 1 T	moonshotai/Kimi-K2.6	kimi-k2.5
Kimi-K2.5	32 B / 1 T	moonshotai/Kimi-K2.5	kimi-k2.5
Kimi-K2-Instruct	32 B / 1 T	moonshotai/Kimi-K2-Instruct	kimi-k2
Kimi-K2-Thinking	32 B / 1 T	moonshotai/Kimi-K2-Thinking	kimi-k2

Fastest path to train

The single-node Kimi-K2.5 2-layer smoke test, before scaling the full model across many nodes:

python scripts/run_kimi_k25.py full-train --model-name Kimi-K2.5-2layer --num-nodes 1

See the Kimi K2.5 page for the full multi-node recipe, or Kimi K2 for the 16-node K2-Thinking recipe (including the one-line model_type patch that lets Miles treat K2 as a DeepSeek-V3-shaped architecture).

Which variant do I pick?

• Latest multimodal agentic model → Kimi-K2.6 or Kimi-K2.5 (kimi-k2.5).
• Frontier-scale instruction-tuned MoE → Kimi-K2-Instruct (kimi-k2).
• Reasoning-style training, INT4 QAT target → Kimi-K2-Thinking (kimi-k2).