45 lines
1.9 KiB
Django/Jinja
45 lines
1.9 KiB
Django/Jinja
[Unit]
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Description=llama-server (tool-calling) — Mistral-Small-24B-Instruct-2501 Q3_K_M (OpenAI-compatible inference)
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After=network.target nvidia-persistenced.service
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Wants=nvidia-persistenced.service
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[Service]
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Type=simple
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User={{ llm_service_user }}
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Group={{ llm_service_user }}
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Environment="HOME=/home/{{ llm_service_user }}"
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ExecStart={{ llm_binary_path }} \
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--model {{ llm_toolcall_model_path }} \
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--host {{ llm_bind_address }} \
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--port {{ llm_toolcall_port }} \
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--ctx-size {{ llm_toolcall_ctx_size }} \
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--n-gpu-layers {{ llm_toolcall_gpu_layers }} \
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--parallel {{ llm_toolcall_parallel }} \
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--jinja \
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--chat-template-file {{ llm_toolcall_chat_template_file }} \
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--metrics
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# --jinja + --chat-template-file: Mistral-Small-24B-Instruct-2501's own
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# embedded chat template has NO tool-calling support (verified via /props ->
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# chat_template_caps.supports_tools=false). We serve Mistral-Nemo-Instruct-
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# 2407's tool-calling-capable Jinja template instead (same Mistral instruct
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# format family, adds [AVAILABLE_TOOLS]/[TOOL_CALLS] handling). See
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# docs/validation-log.md for the investigation and validation probe results.
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# NOTE: --host is the private LAN IP (10.1.71.130 by default), NOT 0.0.0.0.
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# NOTE: --parallel 1 is deliberate (plan §1/§2) — tool-calling profiles are
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# single-session-at-a-time per Claude Code profile; lower parallelism reduces
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# KV overhead and lowers hallucination surface from context bleed between
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# concurrent slots.
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# IMPORTANT: this endpoint MUST pass the plan §7 validation harness
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# (docs/validation-log.md in this role) before any Claude Code / tool-calling
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# Hermes profile is pointed at it. Mistral-Small shares lineage concerns
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# flagged for Qwen2.5/Qwen3 hallucinated tool_calls — do not assume safety.
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Restart=on-failure
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RestartSec=10
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TimeoutStartSec=600
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StandardOutput=journal
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StandardError=journal
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SyslogIdentifier=llama-server-toolcall
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[Install]
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WantedBy=multi-user.target
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