[Sync] Lua Hyprland chain, licence cleanup

wm/.config/llamacpp/llama.service

@@ -0,0 +1,27 @@
+[Unit]
+Description=llama.cpp router (wm, GTX 1080 Ti) — OpenAI-compatible API on 0.0.0.0:11343
+Documentation=file:///home/coja/.config/llamacpp/README.md
+# A USER unit (fl's llama.service is a system unit under a service user): it runs as coja, so the
+# prebuilt binary in ~/.local/opt, the presets in ~/.config/llamacpp and the GGUFs in ~/Documents/models
+# need no extra permissions. Starts with the login session (default.target); for a true at-boot start
+# without logging in: `loginctl enable-linger coja`.
+After=graphical-session-pre.target
+
+[Service]
+Type=simple
+# Same command as the old `llamaserver` abbr. The wrapper execs the CUDA 12.8 prebuilt and raises the
+# PTX JIT cache cap (Pascal is JIT-compiled from PTX; first start after a build update ≈ 50 s).
+# 0.0.0.0 = every IPv4 interface (wired + wifi office LAN, ZeroTier, docker0) and still 127.0.0.1;
+# keyless, by choice. IPv4 only: `localhost` → ::1 is refused, so clients use 127.0.0.1.
+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
+# Presets are read once at startup → `systemctl --user restart llama` after editing config.ini.
+Restart=on-failure
+RestartSec=3
+# The MoE presets run their CPU experts on all 6 cores: yield to interactive apps (GPU work unaffected).
+Nice=19
+# Idle router holds no model (sleep-idle-seconds per preset), so keeping it up costs nothing.
+StandardOutput=journal
+StandardError=journal
+
+[Install]
+WantedBy=default.target