astro-orbiter's vLLM primary model changed 2026-09-01 from
DeepSeek-R1-Distill-Qwen-32B-AWQ to Gemma-4-26B-A4B-it-AWQ (Google,
Apache 2.0, US-origin). Same endpoint/API key — only the served model
name changed. max_model_len also bumped to 65536 (was 32768).
Gemma 4 does not emit an always-on <think> reasoning trace like
DeepSeek-R1 did, so HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS=4096
should have more effective headroom for real extraction output than
before, not less.
astro-orbiter's vLLM primary model changed 2026-09-01 from
Qwen2.5-32B-Instruct-AWQ to DeepSeek-R1-Distill-Qwen-32B-AWQ
(single-model deployment, see ansible/roles/deploy-vllm/README.md).
Same endpoint (http://astro-orbiter:8000/v1) and API key — only the
served model name changed.
Flagged for follow-up, not fixed here: DeepSeek-R1 emits a
<think>...</think> reasoning trace before its final answer; Hindsight's
fact-extraction prompt/parsing has not been specifically verified
against this reasoning-model output shape. Watch extraction quality
after this rolls out; HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS left
at 4096 pending confirmation that's enough headroom for a real retain
to complete past the reasoning trace.
Companion commit to 6bfcc76 (vllm permanent residency cutover). Now that
llama-swap is stopped+disabled and vLLM is the permanent serving layer:
- HINDSIGHT_API_LLM_BASE_URL: llama-swap :8001 -> vLLM :8000
- HINDSIGHT_API_LLM_MODEL: Qwen3.8-27B-Q4_K_M -> Qwen2.5-32B-Instruct-AWQ
- Added HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS=4096 (vLLM's
max_model_len=8192 is far below the 64000 Hindsight retain default;
uncapped this 400s on long retains -- found in t_e6facb19's validation)
- ExternalSecret HINDSIGHT_API_LLM_API_KEY source: 1Password 'nous' item
(unused fallback) -> 1Password 'vllm' item's api-key (vLLM validates
its bearer token, unlike llama-swap which accepted anything)
Embeddings intentionally UNCHANGED -- Hindsight stays on its bundled
local 384-dim embedder (HINDSIGHT_API_EMBEDDINGS_PROVIDER still unset).
Switching to vLLM's nomic-embed (768-dim) is a separate, destructive,
explicitly-approved-only migration (~1300 rows across ~20 banks) --
out of scope here, not part of the dashboard's 'stop llama-swap /
start vLLM' instruction.
Critical finding: flipping vllm_service_enabled/state=true/started and
restarting llama-swap alongside it broke llama-swap's ability to load
ANY of its own generative models -- every /v1/chat/completions request
against Qwen3.8-27B-Q4_K_M or Qwen3-8B aux models failed with
'upstream command exited prematurely' (llama-server OOM at spawn,
~1.8GB free on this 24GB card once vLLM's ~22.8GB was claimed).
Confirmed by direct A/B: same request 500s with vLLM running, 200s
seconds after stopping it.
This breaks 21 Hermes agent profiles' aux-model tasks (skills_hub,
approval, mcp, title_generation, profile_describer, compression) plus
OpenViking's VLM -- a far larger blast radius than Hindsight's single
LLM endpoint. Reverted:
- vllm_service_enabled/state back to role defaults (false/stopped) --
vLLM stays staged, startable for a brief validated shadow window,
NOT safe to leave resident in production.
- Hindsight's HINDSIGHT_API_LLM_BASE_URL back to llama-swap
(astro-orbiter:8001, Qwen3.8-27B-Q4_K_M) and the API key secret
source back to the Nous fallback item (pre-task state) --
the vLLM cutover, while functionally validated in isolation
(health, /v1/chat/completions, and a live hindsight_retain+recall
round-trip all succeeded), requires continuous vLLM availability
which is now known to be unsafe on this card.
Comment posted on t_6dff1ecc: teardown remains correctly blocked --
full cutover is not achievable within this card's VRAM budget as
currently scoped. Needs a human decision on aux-model migration
strategy (see roles/deploy-vllm README's 'Critical architectural
finding' section) before any further progress.
Default 64000 exceeded vLLM Qwen2.5-32B-Instruct-AWQ's --max-model-len
8192, causing every retain call to 500 with
'max_tokens=64000 cannot be greater than max_model_len=8192'.
llama-swap's Qwen3.8-27B ran at ctx=65536 so this never surfaced
before the vLLM cutover. Lowered to 4096.
HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai pointed at vLLM's
nomic-embed-text-v1.5 (768-dim) crash-looped hindsight-api:
'Cannot change embedding dimension from 384 to 768: memory_units
table contains 1289 rows with embeddings.' Hindsight was never
actually using astro-orbiter for embeddings (defaults to a bundled
local 384-dim sentence-transformers model when the env var is unset)
-- this was a genuine architecture assumption error, not a config
typo. Re-embedding all existing memory data across ~20 agent banks is
a destructive, irreversible operation requiring its own explicit,
approved task -- not something to trigger as a side effect of an
infra migration. Keeps the LLM cutover (astro-orbiter:8000, vLLM
Qwen2.5-32B-Instruct-AWQ) which is safe and already validated.
- vllm.service.j2: branch on role==embedding for --runner pooling
--convert embed, --no-enable-prefix-caching, per-model
trust_remote_code toggle (needed for nomic-embed-text-v1.5's custom
NomicBertModel code), and enforce_eager toggle (needed to avoid CUDA
graph capture OOM when co-resident with another vLLM process on this
24GB card).
- tasks/verify.yml: split completions vs embedding smoke tests --
embedding-mode instances don't serve /v1/completions. Assert a
non-empty embedding vector, not just HTTP 200.
- host_vars/astro-orbiter: enable nomic-embed-text-v1.5 (port 8020),
lower primary model's gpu_memory_utilization 0.95->0.90 + add
enforce_eager after finding 0.95 crash-looped 6-7x before stabilizing
with co-resident nomic-embed (real fix, confirmed via NRestarts=0
after clean stop/start, not luck).
- Hindsight (values.yaml + externalsecret.yaml): cut LLM + embeddings
over to vLLM (:8000, :8020), wire the previously-unset
HINDSIGHT_API_EMBEDDINGS_* env vars for the first time, and swap the
API key secret source from the Nous fallback item to vllm/api-key
(vLLM enforces real auth, llama-swap did not).
- README: document the embedding-mode branch, VRAM findings, and a
genuine architecture gap -- vLLM's one-model-per-process design
cannot replace llama-swap's 5-model LRU roster on this 24GB card, so
21 Hermes profiles' aux-model consumers (Qwen3-8B-no_think,
Phi-3.5-mini, Meta-Llama-3.1-8B, Qwen2.5-Coder-14B) and OpenViking's
VLM stay on llama-swap. Full teardown (t_6dff1ecc) needs a human
decision on the aux-model strategy before it can proceed.
t_d7f8cd65 — astro-orbiter is a single serial llama-swap process; Hindsight's
default LLM_MAX_CONCURRENT=32 burst N parallel retains -> node rejects/times
out extras -> 502. Cap global + retain concurrency to 1 (native semaphore
config, no code change). Raise client timeout 120->600s and ingress
proxy-read/send 300->600s so a single long retain (~90s under load) survives.
stepfun rejects Hindsight tagged fact-extraction (400 'missing tags');
solar-pro4:free is the previously-verified-good Nous model for retain/recall
(t_e3375410 / t_d0dffc3d). base_url + provider unchanged.
Scoped to the hindsight ingress (cosmic-rewind.local.mk-labs.cloud) only.
A slow agentic reflect (~132s on solar-pro4) exceeded the 60s default
proxy-read-timeout, causing nginx to return 504 before the app delivered 200.
Raising read+send to 300s lets the response land. No global configmap change.
Refs t_e3375410.
/metrics?model=Qwen3.6 forces the router to attempt loading Qwen3.6 every
90s scrape cycle, triggering a CUDA OOM error since VRAM is already consumed
by the resident Llama3+Phi3.5 models. This produces real GPU power spikes
(~110W load-attempt), not a benign counter read like the Llama3/Phi3.5 jobs.
nvidia_gpu_exporter (:9835) already provides GPU power/VRAM/utilization at
zero wake cost. No Grafana dashboard panel references Qwen3.6 model-specific
llama-server metrics. Ryan approved full removal.
Job is commented out (not deleted) for easy revert if Qwen3.6 is ever
re-added as a resident model. Llama3/Phi3.5 scrape jobs untouched.
Each /metrics?model=<id> request on the llama.cpp router wakes the GPU
sub-server to P2 (~110W). At 15s with two active jobs (llama3 + phi35),
combined scrape frequency (~7-8s effective) keeps the GPU continuously at
P2 despite zero real inference requests.
At 90s: each scrape wakes GPU for ~5-10s then it drops to P8 (~20W) for
~80s. Verified via iptables block test on 2026-08-13 (t_e7d547ea):
Before block: 110-115W P2 continuously
After block: 19-21W P8 consistently
After unblock: returned to 110W P2 within seconds
qwen3 interval also set to 90s (model not loaded so moot, but consistent).
Ref: t_e7d547ea
Port 8000 (gemma-2-27b-it-GGUF) is dead after the day2 router cutover on
2026-08-12. Production inference now runs through llama-server-router on
port 8002.
The router exposes per-model Prometheus metrics via /metrics?model=<id>.
Since a single /metrics request without ?model returns HTTP 400, replaced
the single stale job with three per-model jobs — one per model registered
in the router per /v1/models:
- Qwen3.6-35B-A3B-UD-Q4_K_S (currently unloaded but registered)
- Meta-Llama-3.1-8B-Instruct-Q4_K_M (loaded)
- Phi-3.5-mini-instruct-Q8_0 (loaded)
Static 'model' label carries the canonical llama.cpp model id (not alias).
Added 'endpoint: astro-orbiter-router' to identify the scrape origin.
Removed dead :8000 target entirely.
- Prometheus scrape configs for node/gpu/llama-server exporters on
astro-orbiter now declared in cluster/applications/monitoring/values.yaml
(additionalScrapeConfigs), applied via ArgoCD sync instead of an
imperative kubectl secret patch from the Ansible role.
- Grafana dashboard for astro-orbiter LLM inference added as a ConfigMap
in cluster/applications/monitoring/dashboards.yaml (grafana_dashboard=1
sidecar label), replacing the role's ad-hoc kubectl apply of a rendered
Jinja template.
- ansible/roles/llm-inference/tasks/monitoring.yml: removed the kubectl
get/patch/apply tasks and orphaned grafana-llm-dashboard.json.j2
template; role now only stands up node_exporter + nvidia_gpu_exporter
and verifies they're reachable — cluster-facing config lives in Git.
- host_vars/vars.yml + inventory.yml: finalize astro-orbiter as the
llama.cpp/RTX 3090 host (jarvis user, ssh key), drop stale Ollama/AMD
vars and ollama_server inventory group superseded by the ATX rebuild.
- Stable udev device symlinks (/dev/jmri/nce, /dev/jmri/loconet, /dev/jmri/lcc)
- jmri-monitor: polls Leviton Decora Smart switch to start/stop JMRI automatically
- Quiet hours 1-10 AM (no polling)
- 30s off-delay before shutdown
- LCRR config cloned from Gitea (ssh://gitea.mk-labs.cloud:2221/rblundon/LCRR.git)
- ~/.jmri symlinked to LCRR repo for GitOps config management
- jmri-gui: X11 remote GUI access (PanelPro/DecoderPro) via ssh -X as jmri user
- Stops daemon, launches GUI, restarts daemon on exit if layout still on
- jmri user gets login shell + SSH key for GUI sessions
- Full JRE installed (openjdk-21-jre) for AWT/X11 support
- minecraft-prometheus-exporter v3.1.2 plugin (port 9225)
- ServiceMonitor for Prometheus scraping
- Grafana dashboard ConfigMap (ID 20659, Minecraft server stats)
- metrics port added to Service and Deployment containerPorts
- PaperMC 26.1.2 via itzg/minecraft-server:2026.7.0
- Namespace: minecraft, Service: journey-into-imagination
- TCP port 10182 (non-standard) via ingress-nginx tcp forwarding
- Pure Storage CSI PVC (pure-block, 50Gi) for world data
- World seed hardcoded: -5177989977648707969
- RCON password via ExternalSecret + 1Password Connect
- SleepMost v5.6.2 plugin for single-player sleep
- Whitelist off at launch, toggle-ready
- ExternalDNS annotations for internal Technitium record
- Manual steps: UniFi port forward WAN:10182→10.1.71.80:10182,
Cloudflare A record + SRV for journey-into-imagination.mk-labs.cloud
