llm-inference-multimodel: fix tool-calling support (jinja template + gpu-layers=20 for VRAM fit)

ansible/roles/llm-inference-multimodel/templates/llama-server-toolcall.service.j2

@@ -15,9 +15,15 @@ ExecStart={{ llm_binary_path }} \
     --ctx-size {{ llm_toolcall_ctx_size }} \
     --n-gpu-layers {{ llm_toolcall_gpu_layers }} \
     --parallel {{ llm_toolcall_parallel }} \
+    --jinja \
+    --chat-template-file {{ llm_toolcall_chat_template_file }} \
     --metrics
-# NOTE: no --chat-template flag — let llama-server auto-detect Mistral-Small's
-# own embedded chat template from GGUF metadata.
+# --jinja + --chat-template-file: Mistral-Small-24B-Instruct-2501's own
+# embedded chat template has NO tool-calling support (verified via /props ->
+# chat_template_caps.supports_tools=false). We serve Mistral-Nemo-Instruct-
+# 2407's tool-calling-capable Jinja template instead (same Mistral instruct
+# format family, adds [AVAILABLE_TOOLS]/[TOOL_CALLS] handling). See
+# docs/validation-log.md for the investigation and validation probe results.
 # NOTE: --host is the private LAN IP (10.1.71.130 by default), NOT 0.0.0.0.
 # NOTE: --parallel 1 is deliberate (plan §1/§2) — tool-calling profiles are
 # single-session-at-a-time per Claude Code profile; lower parallelism reduces