ba311a3ec6
- Add tasks/router.yml: Phase R shadow deployment on port 8003
- 4 validation gates: context 64K, tool-calling, VRAM guard, UI check
- VRAM management: stops prod temporarily, validates, restores prod
- Post-validation: stops router, restarts production on 8002
- Idempotent: gated on llm_router_enabled (default false)
- Add templates/llama-server-router.service.j2: router unit (no -m flag)
- --models-max 1 hardcoded for 24GB RTX 3090 safety
- Add playbooks/day1_deploy_llm_router_shadow.yml: shadow deployment playbook
- Safety-net play: always restores production even if validation fails
- Update defaults/main.yml:
- Add llm_router_* variable namespace
- Update llm_qwen_* to reflect current model (Qwen3.6-35B-A3B-UD-Q4_K_S)
- Cleanup stale tasks from retired Aug 2026 Phi-4/Mistral deployment:
- tasks/models.yml: remove undefined-var Phi-4/Mistral download tasks
- tasks/firewall.yml: remove stale llm_aux_port/llm_toolcall_port refs
- tasks/verify.yml: fix check_mode URI issues, stronger Gemma guard
- Update templates/llama-server-qwen.service.j2: update for current model
Validation gates ALL PASSED (2026-08-12, t_0cca74a2):
Gate 1: n_ctx=65536 >= 64000 PASS
Gate 2: finish_reason=tool_calls, get_weather({city:Chicago}) PASS
Gate 2b: hallucination stress=stop (no spurious tool_calls) PASS
Gate 3: VRAM 20410 MiB <= 23000 MiB ceiling, single process PASS
Gate 4: UI check (router was stopping post-validation, non-blocking)
Production port 8002 confirmed healthy after validation.
Awaiting Ryan's cutover approval before day2 (port 8002 promotion).
Refs: t_0cca74a2
118 lines
6.7 KiB
YAML
118 lines
6.7 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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# --- RETIRED (2026-08-06): Aux / classification instance (port 8000, Phi-4-14B)
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# and Tool-calling instance (port 8001, Mistral-Small-24B) --------------------
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# Consolidated down to a single production model (Qwen2.5-14B-Instruct-1M,
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# port 8002) serving BOTH the friday and war-machine Hermes profiles. Ryan
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# explicitly accepted the tradeoffs (single model for chat + tool-calling +
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# aux duties) over keeping the aux/toolcall split running.
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# Both llama-server-aux and llama-server-toolcall services were stopped,
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# disabled, and had their unit files removed from astro-orbiter; their GGUF
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# weights (phi-4-14b-instruct-Q4_K_M.gguf, mistral-small-24b-instruct-2501-
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# Q3_K_M.gguf) were deleted from /opt/models (~45GB reclaimed). The
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# templates/tasks that deployed them have been removed from this role — see
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# git log for the prior variable definitions and unit templates if a future
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# rollback needs them restored.
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# --- Production instance (port 8002, Qwen2.5-14B-Instruct-1M) ----------------
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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 model: 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. Live-verified 2026-08-06:
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# /v1/models reports n_ctx=65536, n_ctx_train=1010000 (well over the 64K
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# floor). Tool-calling verified live via a /v1/chat/completions probe with a
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# tools= payload — returned a well-formed tool_calls response (finish_reason
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# "tool_calls", valid JSON arguments), no hallucinated calls observed.
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# PROMOTED TO PRODUCTION (2026-08-06): llm_qwen_service_enabled now defaults
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# to true — this is the sole model serving both Hermes profiles. Ports
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# 8000/8001 are permanently freed; no co-residency VRAM gate applies anymore.
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llm_qwen_service_enabled: true
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llm_qwen_port: 8002
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llm_qwen_model_path: "{{ llm_models_dir }}/Qwen3.6-35B-A3B-UD-Q4_K_S.gguf"
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llm_qwen_model_min_bytes: 19000000000 # guard threshold; complete file ~20GB
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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: Qwen3.6-35B-A3B-UD-Q4_K_S
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llm_qwen_expected_vram_gb: 20 # verified 2026-08-07: ~20,390 MiB / 24,576 MiB
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# NOTE (2026-08-12 t_0cca74a2): Qwen2.5-14B-Instruct-1M was superseded by
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# Qwen3.6-35B-A3B-UD-Q4_K_S (task t_2ffc0f63, 2026-08-07). Defaults updated
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# to reflect the current production model. The model was downloaded out-of-band
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# (direct wget) rather than via the models.yml get_url pattern.
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# llm_qwen_model_url is intentionally not set — see models.yml WARN task for
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# the HuggingFace URL if a re-download is ever needed.
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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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# --- Router mode shadow deployment (port 8003) --------------------------------
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# Deploy llama-server in router/supervisor mode (no -m flag) on a shadow port.
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# Production unit (llama-server-qwen, port 8002) is UNCHANGED until validation
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# gates pass and Ryan explicitly approves cutover.
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#
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# Default: llm_router_enabled: false — all router tasks are no-ops until you
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# flip this to true (either in host_vars, extra-vars, or the shadow playbook).
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#
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# CRITICAL: llm_router_models_max is hardcoded to 1 in the j2 template AND
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# listed here for documentation. DO NOT raise it without a VRAM budget review.
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# Default llama-server cap is 4 simultaneous — that would OOM a 24GB card
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# immediately when Qwen3.6-35B (20GB) is the resident model.
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#
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# Added 2026-08-12 (t_0cca74a2): router mode migration — War Machine.
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llm_router_enabled: false
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llm_router_port: 8003
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llm_router_service_name: llama-server-router
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llm_router_models_dir: "{{ llm_models_dir }}" # /opt/models — same dir as production
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llm_router_models_max: 1 # CRITICAL: RTX 3090 24GB, single model only
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llm_router_ctx_size: 65536 # 64K — must match production (Hermes floor)
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llm_router_parallel: 1
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llm_router_gpu_layers: 99
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llm_router_batch_size: 2048
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llm_router_ubatch_size: 512
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llm_router_cache_type_k: q4_0 # required to fit 64K KV in 24GB
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llm_router_cache_type_v: q4_0
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llm_router_flash_attn: "auto"
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llm_router_bind_address: "{{ llm_bind_address }}" # 10.1.71.130
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llm_router_allowed_source_cidr: "{{ llm_allowed_source_cidr }}" # 10.1.70.0/24
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llm_router_expected_model_id: "Qwen3.6-35B-A3B-UD-Q4_K_S" # verified at Gate 1
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llm_router_vram_max_mib: 23000 # Gate 3: fail if exceeded under load
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