102 lines
5.7 KiB
YAML
102 lines
5.7 KiB
YAML
---
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# ------------------------------------------------------------------------------
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# FILE: roles/llm-inference-multimodel/defaults/main.yml
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# DESCRIPTION: Overridable defaults for the llm-inference-multimodel role.
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# Deploy target: astro-orbiter (10.1.71.130, RTX 3090 24GB).
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# Built ALONGSIDE roles/llm-inference (not a replacement) — that
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# role's CUDA/build/driver phases are the prerequisite; this role
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# assumes /opt/llama.cpp/build/bin/llama-server already exists.
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#
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# See /home/hermes/astro-orbiter-multi-model-plan.md for the full
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# approved design (VRAM math, rationale, rollback story).
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# ------------------------------------------------------------------------------
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# Shared
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llm_service_user: jarvis
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llm_binary_path: /opt/llama.cpp/build/bin/llama-server
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llm_models_dir: /opt/models
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# Bind address — deliberately NOT 0.0.0.0 (see plan §5). Default to the private
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# LAN interface so both instances are reachable from Hermes but not the world.
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# Override to 127.0.0.1 if even LAN-wide reachability is unwanted and a reverse
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# proxy/localhost-only tunnel is used instead.
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llm_bind_address: "10.1.71.130"
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# Firewall scoping (Phase 3) — subnet/hosts allowed to reach the ports above.
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# Override per-environment; default assumes Hermes runs somewhere on this /24.
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llm_allowed_source_cidr: "10.1.70.0/24"
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# --- Aux / classification instance (port 8000, Phi-4-14B) -------------------
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# Text-only instruction model, no tool-calling training — safe offload target
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# per the auxiliary-task-offload skill's "no tool_calls emission risk" bar.
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llm_aux_port: 8000
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llm_aux_model_path: "{{ llm_models_dir }}/phi-4-14b-instruct-Q4_K_M.gguf"
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llm_aux_model_url: "https://huggingface.co/bartowski/phi-4-GGUF/resolve/main/phi-4-Q4_K_M.gguf"
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llm_aux_model_min_bytes: 8000000000 # guard threshold; complete file ~8.5GB
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llm_aux_ctx_size: 8192
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llm_aux_parallel: 2
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llm_aux_gpu_layers: 99
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llm_aux_service_name: llama-server-aux
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llm_aux_model_id: phi-4-14b-instruct # served model name for OpenAI-compat API
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# --- Tool-calling instance (port 8001, Mistral-Small-24B) --------------------
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# Native function-calling support; deployed at Q3_K_M per plan §1 Option B
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# to fit VRAM budget. MUST pass the §7 validation harness before any
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# Claude-Code-capable profile is pointed at this port.
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llm_toolcall_port: 8001
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llm_toolcall_model_path: "{{ llm_models_dir }}/mistral-small-24b-instruct-2501-Q3_K_M.gguf"
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llm_toolcall_model_url: "https://huggingface.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF/resolve/main/Mistral-Small-24B-Instruct-2501-Q3_K_M.gguf"
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llm_toolcall_model_min_bytes: 11000000000 # guard threshold; complete file ~11.7GB
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llm_toolcall_ctx_size: 4096
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llm_toolcall_parallel: 1
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# 99 (full offload) OOMs on a 24GB GPU when co-resident with llama-server
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# (phi-4, port 8000, ~10.4GB). 20 layers validated stable alongside it with
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# headroom to spare (see docs/validation-log.md).
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llm_toolcall_gpu_layers: 20
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# Chat template shipped with Mistral-Small-24B-Instruct-2501 has no tool-call
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# support (no [AVAILABLE_TOOLS]/[TOOL_CALLS] handling) — see
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# docs/validation-log.md for the investigation. We use Mistral-Nemo-Instruct-2407's
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# tool-calling-capable template instead; Mistral-Small-24B shares the same
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# Mistral tokenizer/instruct format family so the template is compatible.
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llm_toolcall_chat_template_file: "{{ llm_models_dir }}/templates/mistral-small-tool-use.jinja"
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llm_toolcall_service_name: llama-server-toolcall
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llm_toolcall_model_id: mistral-small-24b-instruct-2501
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# --- Shadow instance (port 8002) ---------------------------------------------
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# History (2026-08-06): Qwen2.5-14B-Instruct (base) was deployed to this slot
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# and DISQUALIFIED — live /v1/models meta reported n_ctx_train=32768, well
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# under the 64K Hermes floor (the model card's "128K" figure conflated
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# YaRN-extended inference-time scaling with actual trained context; disabled
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# by default, not baked in). Llama-3.1-8B-Instruct was tried next — cleared
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# the context gate (verified live n_ctx_train=131072) but failed the
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# tool-calling validation harness badly (8/10 hallucination-stress prompts
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# triggered spurious tool_calls even at temp=0.1 with the correct official
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# chat template) — purged from disk and Ansible entirely, see git log.
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# Current candidate: Qwen2.5-14B-Instruct-1M (bartowski GGUF) — distinct
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# checkpoint with genuine additional long-context pretraining, NOT the same
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# weights as the disqualified base model above. MUST re-verify n_ctx_train
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# live from /v1/models before trusting any claim — the base-Qwen and
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# Llama-3.1 lessons both apply here.
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# GATE: VRAM co-residency with Phi-4(8000)+Mistral(8001) not guaranteed —
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# stop llama-server-toolcall for the shadow-test window if needed (proven
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# pattern from prior attempts). llm_qwen_service_enabled defaults to false so
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# a normal playbook run will template the unit but NOT start it.
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llm_qwen_service_enabled: false
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llm_qwen_port: 8002
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llm_qwen_model_path: "{{ llm_models_dir }}/Qwen2.5-14B-Instruct-1M-Q5_K_M.gguf"
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llm_qwen_model_url: "https://huggingface.co/bartowski/Qwen2.5-14B-Instruct-1M-GGUF/resolve/main/Qwen2.5-14B-Instruct-1M-Q5_K_M.gguf"
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llm_qwen_model_min_bytes: 9500000000 # guard threshold; complete file ~10.5GB
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llm_qwen_ctx_size: 65536
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llm_qwen_parallel: 1
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llm_qwen_gpu_layers: 99
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llm_qwen_batch_size: 2048
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llm_qwen_ubatch_size: 512
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llm_qwen_service_name: llama-server-qwen
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llm_qwen_model_id: qwen2.5-14b-instruct-1m
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llm_qwen_expected_vram_gb: 16.5
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# --- Existing Gemma baseline (rollback target — never modified by this role) -
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# Populated by Phase 0 discovery (tasks/discover.yml) if not already known.
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# Set here only as a fallback name to search for; discovery is authoritative.
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llm_existing_gemma_service_name_guess: llama-server
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