28 lines
1.5 KiB
Desktop File
28 lines
1.5 KiB
Desktop File
[Unit]
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Description=llama.cpp router (wm, GTX 1080 Ti) — OpenAI-compatible API on 0.0.0.0:11343
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Documentation=file:///home/coja/.config/llamacpp/README.md
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# A USER unit (fl's llama.service is a system unit under a service user): it runs as coja, so the
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# prebuilt binary in ~/.local/opt, the presets in ~/.config/llamacpp and the GGUFs in ~/Documents/models
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# need no extra permissions. Starts with the login session (default.target); for a true at-boot start
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# without logging in: `loginctl enable-linger coja`.
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After=graphical-session-pre.target
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[Service]
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Type=simple
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# Same command as the old `llamaserver` abbr. The wrapper execs the CUDA 12.8 prebuilt and raises the
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# PTX JIT cache cap (Pascal is JIT-compiled from PTX; first start after a build update ≈ 50 s).
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# 0.0.0.0 = every IPv4 interface (wired + wifi office LAN, ZeroTier, docker0) and still 127.0.0.1;
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# keyless, by choice. IPv4 only: `localhost` → ::1 is refused, so clients use 127.0.0.1.
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ExecStart=/home/coja/.local/bin/llama-server-cuda --host 0.0.0.0 --port 11343 --cors-origins localhost --models-max 1 --models-preset /home/coja/.config/llamacpp/config.ini
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# Presets are read once at startup → `systemctl --user restart llama` after editing config.ini.
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Restart=on-failure
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RestartSec=3
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# The MoE presets run their CPU experts on all 6 cores: yield to interactive apps (GPU work unaffected).
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Nice=19
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# Idle router holds no model (sleep-idle-seconds per preset), so keeping it up costs nothing.
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StandardOutput=journal
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StandardError=journal
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[Install]
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WantedBy=default.target
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