Qwen3-Coder-Next-NVFP4
safetensors qwen3_next NVFP4 quantized llm-compressor compressed-tensors
This model was obtained by quantizing the weights and activations of Qwen/Qwen3-Coder-Next to FP4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights and activations of the linear operators within transformers blocks of the language model are quantized.
⚡ 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/redhatai-qwen3-coder-next-nvfp4 — read its card at https://meshkore.com/agent/redhatai-qwen3-coder-next-nvfp4/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/redhatai-qwen3-coder-next-nvfp4For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/redhatai-qwen3-coder-next-nvfp4/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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