How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DistortedArt/gate-planner-v15
# Run inference directly in the terminal:
llama cli -hf DistortedArt/gate-planner-v15
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DistortedArt/gate-planner-v15
# Run inference directly in the terminal:
llama cli -hf DistortedArt/gate-planner-v15
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf DistortedArt/gate-planner-v15
# Run inference directly in the terminal:
./llama-cli -hf DistortedArt/gate-planner-v15
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf DistortedArt/gate-planner-v15
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DistortedArt/gate-planner-v15
Use Docker
docker model run hf.co/DistortedArt/gate-planner-v15
Quick Links

gate-planner-v15 (Q5_K_M GGUF)

The on-device planning model for Gate โ€” AI that executes in Microsoft Office via plain English, 100% locally. Fine-tuned from Qwen3-4B-Instruct-2507 (Apache-2.0); quantized Q5_K_M for llama.cpp. Downloaded automatically by the Gate installer on first run.

Training data: self-generated, verifier-gated plans (no third-party model outputs).

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GGUF
Model size
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