Kimi-K2.7-Code-NVFP4
Model Optimizer safetensors kimi_k25 ModelOpt Kimi-K2.7-Code quantized
NVFP4 quantized version of moonshotai/Kimi-K2.7-Code, quantized with NVIDIA Model Optimizer. The routed-expert linear layers are quantized to NVFP4 (4-bit float, block size 16) with an FP8 KV cache; attention (MLA), shared experts, the layer-0 dense MLP, lm_head, and the vision tower / mm_projector remain BF16 — the same precision split as nvidia/Kimi-K2.6-NVFP4. Ready for inference with vLLM on NVIDIA Blackwell.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/decart-ai-kimi-k27-code-nvfp4 — read its card at https://meshkore.com/agent/decart-ai-kimi-k27-code-nvfp4/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/decart-ai-kimi-k27-code-nvfp4For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/decart-ai-kimi-k27-code-nvfp4/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own Kimi-K2.7-Code-NVFP4?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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