Autonomous overnight run under the operator's full-autonomy grant. End state:
fleet up, gen seat untouched, a new verified pen-test seat serving where fable was.
PPL on the orcarouter gen seat (fable downed to free GPU1 for a nospec probe,
probe torn down after): mean 7.07 / median 5.76, within noise of heresy 6.910 /
5.625 and identical to our recipe's usual 7.059. The gen-seat search is settled.
M.O.G.-SEC: chose Blackfrost-Research/M.O.G.-SEC-27B-1M-CTX-BF16 (rev deede677)
over the pre-made ModelOpt NVFP4, which was disqualified on W4A4 4-bit activations
(the AEON degradation mode, catastrophic on a 1M-context model), zero MTP tensors,
and ModelOpt format. Pulled, format-screened (P(<think>) 1.11e-05, clean), quanted
in-house to mixed NVFP4+FP8 (23.4 GB, MTP + vision preserved), and served in the
retired fable slot.
stacks/mog-sec ana-ml2 GPU1 :8019, KV 418,218 tok / 1.60x @ 262K
aliases mog-sec (non-thinking), mog-sec-reasoning (thinking)
gates surface 6/6, MTP 55.3%, format 0/15 leak, vision 7/3/1,
capability 4/4 (delivers offensive-security content)
Served at native 262K, NOT the card's 1M -- the 1M needs YaRN (absent from the
weights' config) plus the SGLang/DFlash2 path the repo ships a deployment kit for,
neither of which is our vLLM surface. A real 1M seat is a separate SGLang project.
Retired char-rp-reasoning + char-rp-fable (zero traffic, pointed at the downed
fable :8019; now 404 cleanly, not repointed -- a security model is not an RP model).
char-rp (meromero) untouched. Vision preprocessor built from the model's own
image_processor block, same trick as the MeroMero seat.
GPU0 seats (gen, meromero) were untouched and healthy throughout. The quant ran in
GPU1 free space with no production seat stopped except fable, which was replaced.
nevermore pinned LLAMA_SWAP_MODEL=granite-4.1-8b, an alias retired with the
granite seat on 2026-08-12. Every summarization call since then failed: 67
consecutive status=failure rows, 0 tokens, twice daily, entirely silently. The
briefing had been rendering with no LLM pass at all. Nothing alerts on
status=failure in the spend logs, so it took an unrelated question about
reranker VRAM to surface it.
It was also pinned to NEVERMORE_RERANK_MODEL=qwen3-reranker -- the incumbent
Brokkr R43 measured harming 80/90 fleet queries -- and was its ONLY caller,
while the production `reranker` alias sat at 0 calls for 4 days. The R43
cutover repointed the alias but never moved the consumer.
nevermore/.env LLAMA_SWAP_MODEL granite-4.1-8b -> summarizer
NEVERMORE_RERANK_MODEL qwen3-reranker -> reranker
(server-only; .env is excluded from the mirror both ways)
Verified against nevermore's exact call shape: summarizer returns clean content
with 0 reasoning chars at temperature 0.2 / max_tokens 4000; reranker scores
0.95 on-topic vs ~1e-5 off-topic; embedding returns dim-1024.
Retired alongside it:
vllm-rerank :8002 Qwen3-Reranker-0.6B + the qwen3-reranker alias
vllm-rerank-a4 :8014 gte-reranker-modernbert + its alias
vllm-granite :8004 Exited 8 days, dead service block
and vllm-rerank-a3 was promoted from a throwaway `docker run` into this stack
(the selection ledger's own open follow-up). Healthy in 55s. It keeps the
bake-off arm name so the ledger, memory and R43 record stay valid.
VLLM_VERSION is pinned latest -> v0.24.0. Every service in the stack shares that
one variable, so a bare `compose up -d` could have silently upgraded all of
them at once; both tags resolved to the same local image (4091d5593f77), so the
pin changed nothing at runtime.
GPU1 is down to 81,448 of 97,887 MiB -- 13.9 GB reclaimed tonight.
Correction: an earlier claim that A4 had no gateway alias was wrong. It did.
LiteLLM serves both config-defined and DB-defined models -- live showed 32
against config.yaml's 26 -- and grepping the file cannot see the difference.
/v1/models and /model/info (which flags db_model) are the ground truth. DB
models delete hot via POST /model/delete with no restart.
Left alone: reranker-a3-bge-v2-m3, a zero-call duplicate of `reranker` on the
same backend. It is Brokkr's cutover-verification handle -- redundant rather
than broken, and another agent's tooling is not mine to delete unilaterally.
Operator directive: lfm2.5-2.6b goes down permanently.
- stacks/vllm/compose.yaml vllm-lfm25 service removed (replaced by a
tombstone comment), pushed live to ana-ml2
- ana-ml2 container docker rm -f'd, 8,721 MiB freed on GPU1
(95,388 -> 86,667 of 97,887)
- litellm config lfm2.5-2.6b alias deleted, live + canonical,
28 -> 27 models
It was an EVAL-ONLY bake-off seat against granite-4.1-8b that never received
the operator ruling it was pending; the comparator was retired from the roster
on 2026-08-15; it was deliberately never wired into any default or fallback
routing chain; and spend logs show 0 calls in the 4-day window to 2026-08-21.
Weights stay in the shared HF cache -- nothing deleted from disk.
The gateway restart that makes the alias deletion take effect is HELD so it can
batch with a pending reranker change. Until then the name is still routable
in-memory and will error against a dead backend.
Auditing the three reranker seats while answering "why do we have three" turned
up a real problem. The design is one production, one rollback, one fallback --
but the traffic is backwards:
:8013 A3 bge-v2-m3 PRODUCTION, backs `reranker` 0 calls / 4 days
:8002 Qwen3-Reranker RETIRED incumbent, rollback only 7 calls, 12-hourly
:8014 A4 gte-modernbert "fallback" no alias at all
nevermore is hard-wired to the incumbent by name (NEVERMORE_RERANK_MODEL=
qwen3-reranker), so the R43 cutover never moved it -- the cutover repointed the
`reranker` alias and correctly left `qwen3-reranker` naming the Qwen model.
Brokkr R43 measured that model harming 80/90 fleet queries, so nevermore's
twice-daily rerank pass is likely degrading its own briefing.
Fix is one line in nevermore's .env plus a nevermore restart, and it must land
before :8002 is retired. Recorded in persistent-memory with the A4 alias also
noted as absent (global CLAUDE.md names reranker-a4-gte-modernbert; it does not
exist).
Operator: update LiteLLM to latest and repull; get rid of the spend-log
DB and cap its growth.
Upgrade: pinned v1.97.0 (latest stable point release; v1.98.0-rc.1 skipped
as a pre-release on the fleet gateway, v1.97.0-stable not yet cut). Image
pre-pulled, DB pg_dump'd (1.8GB gz, keys+config+schema) and .env backed up
before the Prisma migration, which applied cleanly.
DB was 6.08 GB, 6.02 GB of it LiteLLM_SpendLogs storing full prompt+
completion bodies (store_prompts_in_spend_logs: true). Purged via TRUNCATE
on the running 1.91 BEFORE the upgrade so the schema migration ran against
an empty table -- 6081 MB -> 16 MB, keys (32) and models (3) intact.
'Get rid of the db' read as the spend-log DATA, not the database: dropping
it would have destroyed every virtual key (incl. the Lobe key) and the
model config in the same DB.
Cap: store_prompts_in_spend_logs -> false (bodies no longer persisted;
lightweight cost/usage rows and cross-project spend tracking survive) plus
maximum_spend_logs_retention_period 7d / interval 1d as a hard age bound.
Verified post-upgrade: v1.97.0 running, liveliness 200, 31-model roster,
chat round-trip on master + scoped Lobe key, key scoping still enforced
(glm-5.2 blocked), ext-tts 200, and store_prompts confirmed off (a marked
prompt persisted 0 bodies; 4 lightweight rows). Rollback: .env
LITELLM_TAG=v1.91.0 + the 1.8GB dump, both on the host.
Sourced from upstream rather than tuned by hand. Qwen/Qwen3.8-27B card
'Best Practices' 1 and unsloth/Qwen3.8-27B 1 are byte-identical:
Thinking: temperature=1.0 top_p=0.95 top_k=20 min_p=0.0
presence_penalty=0.0 repetition_penalty=1.0
Instruct: temperature=0.7 top_p=0.80 top_k=20 min_p=0.0
presence_penalty=1.5 repetition_penalty=1.0
REAL BUG FIXED: gen-reasoning carried presence_penalty=1.5 -- the
INSTRUCT-mode value applied to a THINKING deployment, where canonical is
0.0. Corrected.
gen was already canonical; added the missing explicit min_p and
repetition_penalty so the full set is visible at the call site rather than
relying on backend defaults that happen to agree.
DELIBERATELY NOT canonicalised: summarizer, classifier, image-judge and
qwen-image-bench run temperature=0 (and the judges top_k=1,
repetition_penalty=1.05) because determinism is the point of those seats.
Forcing temperature=0.7 on a classifier to match a chat preset would break
their contract, so canonical is applied only where the alias is actually
doing open-ended generation.
Recorded against presence_penalty=1.5, which upstream itself hedges on
verbatim: 'you can adjust the presence_penalty parameter between 0 and 2
to reduce endless repetition. However, using a higher value may
occasionally result in language mixing and a slight decrease in model
performance.' 1.5 is high in that band and is the operator's suspected
trigger for the multi-turn degradation. Left at canonical so the baseline
is defensible, with the caveat and the 0.0-0.5 fallback documented inline
as the first dial to move if it recurs.
The comment still described TheDrummer Magidonia-24B-v4.3 Q6_K on
llama.cpp, which was replaced by the vLLM MeroMero-v2 NVFP4A16 seat on
2026-08-12. Routing was already correct (:8016 is MeroMero); only the
prose was wrong, so anyone reading the config got the wrong model family
entirely.
Records why the seat exists: char-rp-reasoning is a Qwen3.x derivative and
emits ~5-6k chars of CoT per turn regardless of which Qwen RP tune is
loaded. Measured 2026-08-16 on identical prompts -- Dark-Scarlett 6036 ch
vs Fable-Fusion 5323 ch -- so that is the base family, not the finetune,
and no swap within it fixes it. Gemma-4 is the non-thinking seat.
Also pins the mandatory --default-chat-template-kwargs
'{"enable_thinking": false}' rationale from b8f0f4c, and flags that the
temp 1.1 / min_p 0.10 samplers were tuned against the retired
Mistral-family seat and never re-tuned for Gemma-4.
Docs-only: no litellm_params touched, no routing change.
Operator-directed evaluation window. char-rp-reasoning now resolves to
Fable-Fusion 711 on :8019 instead of Dark-Scarlett on :8018; DS is DOWN
because GPU1 is zero-sum and FF occupies her slot.
This is an EXPLICIT substitution, not a silent alias swap: the config
block says so in place, carries the measured justification, and names the
rollback. char-rp-fable is added as the seat's honest name so the
evaluation can address it without depending on the temporary repoint, and
as a distinct model_name it gets its own litellm_params object rather than
sharing one (which is what bleeds sampler overrides between variants).
Samplers are unchanged from the DS entry and match the model card's
thinking-mode recommendation (temp 1.0 / top_p 0.95 / top_k 20). Verified
the FF chat template actually honours enable_thinking
(chat_template.jinja:44) rather than ignoring it -- the mismatch that
returned null content on the MeroMero seat.
Verified end-to-end through the gateway on both aliases: prose in content,
CoT in reasoning_content, finish=stop.
CONSUMER HAZARD: FF reasons heavily (2.1-4.6k chars). At max_tokens=1200
one of seven calls returned EMPTY content with finish_reason=length --
reasoning ate the whole budget. Not a refusal and not an alias fault. Use
max_tokens >= 3072; 6/6 clean there. No default is baked into the alias
because that would override caller intent silently.
vllm-lfm25 on ana-ml2 GPU1 :8021 (LiquidAI/LFM2.5-2.6B, util 0.09 into
unreserved slack, max-len 16384, no reasoning-parser so content is non-empty).
LiteLLM alias lfm2.5-2.6b with vendor sampling baked as default (temp 0.1;
top_k 50 + repetition_penalty 1.1 via extra_body). Eval-only, not in any
routing chain, pending operator ruling on LFM Open License production use.
The incumbent Qwen3-Reranker-0.6B was measured actively harming 80/90
fleet queries on main+knowledge_base (and inverting the bare-name region
behind Worldtree #389) — no-reranker beat it 89/90 vs 56/90. Brokkr's R43
bake-off selected BAAI/bge-reranker-v2-m3 (A3): 90/90 top-10, mean rank
0.19, multilingual (XLM-R), ~1.2 GB lighter than the incumbent.
Control arm (A2 = same Qwen weights, seq-cls head) scored identical to the
incumbent, proving the fault is a training prior, not the serving head —
which cancelled the expensive Qwen3-4B arm before it cost a GPU seat.
Cutover boundary 2026-08-06T17:37:48Z. The qwen3-reranker alias and the
:8002 backend are retained for one-edit rollback. Adds the process audit
trail at docs/pfi/reranker-selection-ledger.md.
The Heid panel plan uses Kimi's coding endpoint, not the general Moonshot API.
kimi-k3 now → openai/k3 @ https://api.kimi.com/coding/v1 (KIMI_CODE_API_KEY,
Vivace); the original general-endpoint entry is kept as kimi-k3-gen-api
(api.moonshot.ai, MOONSHOT_API_KEY). Both verified live through the gateway.
Same k3 constraints on both: temperature MUST be 1 (else 400), reasoning model
(reasoning_content vs content, needs adequate max_tokens).
Adds model_name kimi-k3 → openai/kimi-k3 @ https://api.moonshot.ai/v1
(OpenAI-compatible), keyed by MOONSHOT_API_KEY (compose env + .env.example
placeholder; real key on server only). Verified live through the gateway.
Two Moonshot constraints captured in the config comment + pinned: K3 accepts
ONLY temperature=1 (else 400), and it is a reasoning model (CoT in
reasoning_content, answer in content — needs adequate max_tokens or content
returns empty). Model id confirmed via /v1/models.
Operator-directed 2026-07-15. The dedicated Qwen-Image-Bench NVFP4 judge
backend on ana-ml2 GPU1 (:8014) was stopped to reclaim ~32GB after the
arbo -> gen hero-judge switch. Both LiteLLM gateway aliases that pointed at
it -- image-judge and qwen-image-bench -- now repoint to the gen backend
(:8015, qwen3.6-35b-a3b-heretic, vision-intact), held at deterministic
judge sampling (temp 0 / top_k 1 / rep_pen 1.05) with enable_thinking:false
(a reasoning preamble breaks json_object). Verified live: both answer with
:8014 down, so they are definitively on gen.
Incidental: backfilled the canonical char-rp-reasoning litellm block, which
had lagged live since the 2026-07-14 NVFP4+MTP seat repoint (model
deckard-pkd-27b -> char-rp-reasoning, top_k 40 -> 20, min_p dropped,
enable_thinking:true added). Required so pushing the canonical would not
clobber the correct live block.
Live changes applied out-of-band (config push + litellm restart + stack
stop on ana-ml2); live config backup at
config.yaml.bak-pre-imagejudge-20260715. Revert path documented in the
config comment. persistent-memory.md updated (parked item closed).
The gen seat's vLLM served-name was still qwen3.6-27b-aeon, a stale skin
left over from the AEON-27B → 35B-A3B-heretic swap — it named neither the
right family (aeon) nor size (27b vs 35B-A3B). Renamed the served-name to
qwen3.6-35b-a3b-heretic (+ -thinking) on ana-ml2 :8015 via the stack .env,
and repointed litellm's gen / gen-reasoning / summarizer-large model refs +
comments to match, so /v1/models, the gateway config, and spend-logs all
reveal the actual model in the request path.
Verified end-to-end: gen -> 'PIPELINE OK', gen-reasoning -> content + reasoning
surfaced, all three aliases healthy. char-rp / char-rp-reasoning untouched.
dvalin confirmed the live A/B-proven set IS canonical for Deckard as a dark-RP reasoning seat:
temp 1.0/top_p 0.95/top_k 40/min_p 0.05, no presence/rep penalty, DRY 0.8 server-side. Endorsed
over the card's base-thinking (top_k 20/min_p 0/presence 1.5). No value change; comment + memory
record the confirmation + tuning ladder (flat->min_p 0.08, loops->DRY 0.9, over-damped->DRY 0.6/off).
Operator wanted a reasoning-RP model that tolerates DRY (RpR-v4 forbids rep/DRY -> a
1/30 loop tail). Ran the full A/B on brokkr's 30-prompt D1 suite (content-only, slop-scored):
- Deckard-PKD (Qwen3.5-27B, DavidAU creative tune) WON: 0/30 loops, 0/30 refusals, clean
managed reasoning (native Qwen3.5 <think>/enable_thinking), DRY-tolerant, ~57 tok/s,
runs on the base llama-swap b8840 image. -> now the char-rp-reasoning seat (:8018).
- RpR-v4: 0 refusals but 1/30 loop (no-DRY). Pantheon-27B: clean slop but 7/30 explicit
refusals + needs the newer ggml-org/llama.cpp image (Qwen3.6 won't load on b8840).
Snowdrop + Gembrain (Gemma-4): floored (llama.cpp can't manage their reasoning without
the vetoed template hacks). Losers kept on disk as alternates.
- char-rp (Magidonia) unchanged; gen unchanged. gateway char-rp-reasoning -> Deckard
sampler (temp 1.0/top_p 0.95/top_k 40/min_p 0.05; DRY server-side).
char-rp -> TheDrummer Magidonia-24B-v4.3 Q6_K (Magistral prose, ~65 tok/s,
zero refusal, tight POV) via llama.cpp (:8016).
char-rp-reasoning -> ArliAI QwQ-32B-RpR-v4 Q5_K_M (abliterated managed reasoning,
~52 tok/s, reasoning surfaces in reasoning_content) via llama.cpp (:8018).
- New canonical stack stacks/char-rp-gguf/ (llama-server x2, GPU0-pinned, ~86/97G
co-resident with gen). GGUF sidesteps the vLLM-NVFP4 + Mistral-tokenizer traps that
killed the Angel serve. Never Ollama.
- Best-of-breed per seat: no single dense 24-32B is both an elite non-thinking prose
seat AND a clean managed-reasoning seat on llama.cpp (Magidonia [THINK] boundary is
loose; Cydonia-R1 <think> runs away; QwQ is template-managed). Pantheon-Reasoning-27B
stays rejected (re-censors in <think>; RpR-v4 abliterated reasoning is the fix).
- Gateway rewired: char-rp->:8016, char-rp-reasoning->:8018, Mistral/QwQ samplers,
dropped the Qwen enable_thinking kwarg. One-model Magidonia fallback documented.
- Retired the ms32-24b-angel stack.
New stacks/qwen36-27b-aeon: two co-located vLLM serves on ana-ml2 GPU0 —
gen (:8015, MTP off) and an RP seat (:8016, native MTP) — dense Qwen3.6-27B
(qwen3_5 GDN-hybrid, uncensored/abliterated), ModelOpt-NVFP4, multimodal,
256K context, depends_on-sequenced util split (~0.50/0.45). Each serve
carries a base + `-thinking` served-name so the `-reasoning` gateway records
target distinct LiteLLM deployments — otherwise a thinking-off request mutates
the shared litellm_params and clobbers enable_thinking (the shared-config
footgun that silently disabled char-rp-reasoning).
Gateway (stacks/litellm/conf/config.yaml): gen / gen-reasoning /
summarizer-large -> AEON :8015; char-rp / char-rp-reasoning added -> RP seat
:8016 (Qwen-RP sampler recs); gen-reasoning -> `-thinking`, char-rp-reasoning
-> `-rp-thinking`. Retired qwen3.5-122-a10b[-reasoning] + qwen-large[-reasoning]
(qwopus displaced; those named a 122B that no longer serves gen).
Probed live vs z.ai 2026-07-05: glm-5.2 = 1,048,576-token (1M) input context,
131,072 (128K) max output; no gateway-side cap (pure z.ai passthrough). Comment-only,
no runtime effect.
Displaced qwopus-122B on ana-ml2 GPU0:8013 with robbatt/Qwen3.6-40B-Deckard-NVFP4
(stock vLLM 0.23.0, loaded clean: hybrid attn + multimodal + fp4_gemm all green).
Repointed the 5 role aliases (gen, gen-reasoning, qwen-large, qwen-large-reasoning,
summarizer-large); added the qwen3.6-40b-deckard true-name record; left the true
names qwen3.5-122-a10b[-reasoning] to 404 (no-false-alias). Operator-directed
trial-by-fleet-traffic; revert path in the config banner + live backup
config.yaml.bak-pre-deckard-20260701-001036.
dvalin evidence pass: IBM canonical is temp 0; greedy-loop risk is an
open-ended-generation phenomenon, not summ/classify; temp 0.1 reduces
classification reproducibility without fixing loops (use repetition/presence
penalty if loops appear). image-judge stays 0 (Qwen judge card + W&B judge
practice = temp 0 for reproducibility; NVFP4-needs-0.1 unsupported). Both
gateway temps now 0, vendor-canonical.
Operator call: avoid pure-greedy rigidity/loop-risk on granite summ/classify
while staying near-deterministic; matches the house nonzero-temp-floor lean.
image-judge held at temp 0 (scoring reproducibility) pending operator review.
- granite-4.1-8b (+ summarizer/classifier): temperature 0 (IBM vendor-canonical
"temp 0 for inferencing"; top_p/top_k no-ops at temp 0, omitted). Deterministic
baseline for summ/classify; creative callers override.
- GLM family (z.ai cloud): temperature + top_p 0.95 only (the ONLY params z.ai
chat API accepts per its OpenAPI schema; top_k/min_p/penalties absent -> not set).
temp 1.0 for glm-5.1/5.2/5-turbo/4.7 + gen-frontier; temp 0.6 for glm-4.5-air.
Matches z.ai API defaults -> explicit-over-implicit, future-proofs vs vendor drift.
Round-2 dvalin-researched (provenance-labeled), verified live, granite+glm smoked 200.
Embeddings/rerankers excluded (no sampling). Fleet-wide canonical-defaults sweep complete.
The abliterated/NVFP4 Qwopus 122B "gen" model (+ qwen-large / summarizer-large
aliases) had no repetition control in its sampling defaults, causing degenerate
repetition loops. Add presence_penalty: 1.0 (Qwen-documented anti-repetition
lever, range 0-2) to all 7 qwen3.5-122-a10b gateway records. Overrideable
default; bake into the vLLM serving def once the value is validated.
Langfuse's ClickHouse member spewed ~94 GB of unrotated logs and filled ana-docker's
root disk (took the fleet host to 100%, 28/48 containers unhealthy). Its trace UI was
redundant with LiteLLM's native logging — store_prompts_in_spend_logs:true already
captures full prompts/responses/tokens/cost/latency at :4000/ui — and nothing used its
unique trace-grouping/eval features (it only received flat gateway success_callbacks).
Removed the callbacks (gateway observability stays fully native) and tore down the
6-container langfuse stack + volumes on ana-docker. Re-add the callbacks if it returns.
Capability aliases for the PAID frontier tier, mirroring glm-5.2 / glm-5.2-
reasoning (thinking off / on) → openai/glm-5.2 @ z.ai. Worldtree binds these for
a frontier-grade generation/reasoning capability so the backing frontier model
can be swapped gateway-side (operator jump-started WT's request). PAID: only
all-proxy-models / explicitly-scoped keys reach them; the free all-agents-local
key stays fenced off z.ai spend. Verified both resolve + route to GLM 5.2.
llama-swap (ana-ml2:9292) is decommissioned (:9292 confirmed down), so the
catch-all wildcard routed every unmatched / typo'd / stale model name to a DEAD
backend, surfacing a misleading "Connection error" instead of a clean
"model not found". This is the exact footgun that silently swallowed Worldtree's
defunct model names (mistral-small-4 etc.) instead of erroring. Removed (operator
call) so unknown models now 404 loudly. Verified: gateway healthy post-restart,
a bogus model name now returns a clean not-found error, real aliases (gen) still
serve. Re-add explicit per-model entries if a swappable zoo ever returns.
Stand up the gateway-side capability aliases for the role→capability model
indirection (worldtree-dev's transparent-swap direction; operator: no wt-
prefix, reuse the existing summarizer/classifier/gen alias convention).
- chat-judge -> selene-1-mini-8b (mode chat) — WT selene-judgment role.
- reranker -> qwen3-reranker (mode rerank) — generic name for the cap.
- scalar-judge -> Skywork-Reward-V2 via a pass_through_endpoint to ana-ml2:8003
(LiteLLM has no reward/pooling MODE, so it's a passthrough, gateway-key-gated;
consumers hit /scalar-judge/<route> e.g. /score|/pooling|/classify).
Deliberately NO generic `embedding` alias: embedding vectors are model-specific
(not swap-transparent), so that capability stays `qwen3-embedding` — the model-
specific name is the guardrail against treating it as freely swappable. Verified
all three live (chat-judge 200, reranker present, scalar-judge passthrough 200
returning a Skywork reward). Deployed + gateway health-gated.
Duplicate-entry aliases. classifier -> granite-4.1-8b (:8004, same backend as the
existing summarizer alias). summarizer-large -> gen/qwen3.5-122-a10b (:8013, thinking
off) for heavier summarization on the 122B Qwopus. summarizer -> granite already
existed (no-op). Config-staged + deployed without bouncing the gateway; like any
config-add these activate on the next restart (no live-add performed).
Source + deployed config cleaned without bouncing the gateway. NOTE: these were
config-loaded models, which the /model/delete API can't remove (DB-only -> 'not
found in db'), so the LIVE gateway still serves them until its next restart, at
which point the cleaned config drops them. No bounce performed.
Same Qwopus gen model as gen / gen-reasoning (served-name qwen3.5-122-a10b @
:8013, thinking off/on respectively), but each bakes a dummy 'noop' function tool
+ tool_choice:none into litellm_params so a NON-EMPTY tools array always reaches
vLLM — for consumers where the global strip_empty_tools hook isn't the right fix
(they need a valid tools structure present, not stripped). tool_choice:none means
the noop is never called. api_base = the real LAN endpoint http://10.250.50.54:8013
(the requested http://vllm:8000 template wouldn't resolve from the ana-docker
litellm container). Verified: gen-nt + gen-reasoning-nt both survive a client
tools:[] send; noop never invoked; reasoning split intact.
bjk110/Qwen3.5-122B-A10B-abliterated-NVFP4 on ana-ml2 GPU 0 (heretic downed):
- stacks/qwen3.5-122b/ — vLLM serve via the repo's text-only patch (Qwen3.5 MoE is a
multimodal arch but this checkpoint is text-only weights), --reasoning-parser qwen3,
GPU 0 pin, :8013; entrypoint+patch mounted from the model dir.
- serve-qwen3.5-122b.yaml — displace heretic + serve + verify.
- litellm: REMOVED dead mistral-small-4 / -reasoning; added qwen3.5-122-a10b[-reasoning]
+ aliases qwen-large[-reasoning] + repointed gen[-reasoning] -> qwen (thinking split via
chat_template_kwargs.enable_thinking + --reasoning-parser qwen3).
Verified live: qwen healthy on :8013; gen / qwen-large / qwen3.5-122-a10b route, and
gen-reasoning returns reasoning_content; mistral-small-4 removed.
NOTE: Worldtree character backend (was bound to mistral-small-4) is dark until repointed
(operator-acknowledged).
Duplicate-entry aliases (NOT router_settings.model_group_alias — that's hidden from
/v1/models and can be silently ignored in config per litellm #15020/#5524):
- summarizer -> granite-4.1-8b
- gen -> mistral-small-4
- gen-reasoning -> mistral-small-4-reasoning (reasoning_effort:high preserved)
Each alias is a real model_name co-located with its target (keep api_base in sync).
Verified live: all 3 in /v1/models + route end-to-end; gen-reasoning returns
reasoning_content.
GLM 5.2 released ~2026-06; confirmed reachable with our existing
Z_AI_API_KEY (z.ai /models lists glm-5.2; a live completion returned
clean). Added two model_list entries mirroring the glm-5.1 pattern:
glm-5.2 (thinking DISABLED by default, per the 2026-06-11 operator call)
and glm-5.2-reasoning (thinking ENABLED, opt-in). Deployed to
ana-docker /opt/docker/conf/litellm/config.yaml, litellm restarted,
both verified through the gateway (disabled -> reasoning_tokens 0;
reasoning -> 234).
vLLM's OpenAI server 400s on an empty tools array ("tools must not be an
empty array"), which broke every gateway call carrying tools:[] (clients
that send it to mean "no tools" -- OpenAI tolerates it, vLLM does not).
drop_params doesn't help: it drops unsupported PARAMS, not empty VALUES.
Add a CustomLogger async_pre_call_hook (conf/strip_empty_tools.py) that
pops an empty/None tools field (+ orphaned tool_choice) before forwarding,
registered globally via litellm_settings.callbacks so it covers every
vLLM-backed model, not just mistral-small-4. Mounted at
/app/strip_empty_tools.py beside config.yaml (LiteLLM resolves callbacks
relative to the config dir). Surgical: only fires when tools is present
and empty; real tools pass through untouched.
Verified on live gateway (1.87.0): mistral-small-4 and granite-4.1-8b
with tools:[] now 200 (were 400); no-tools baseline unchanged; a real
tool still passes through.
selene: AtlaAI Selene-1-Mini-Llama-3.1-8B judge restored on vLLM after the
llama-swap teardown took its Q6_K GGUF offline. FP8 (dynamic --quantization
fp8; FP8 >= the validated Q6_K fidelity, and text-only Llama so no vision-
tower-noise risk; NVFP4's W4A4 too aggressive for a precision judge). GPU1
util 0.13 (8.51 GiB weights + 2.53 GiB KV, 32K ctx, 1.27x concurrency),
~11 GB GPU1 buffer left. Gateway selene-1-mini-8b → :8011 (shadows the *
wildcard that used to reach it via llama-swap). Judge smoke: scored an
unfaithful claim 1/5 correctly.
mistral-small-4: max-model-len 131072 → 262144 (full native 256K) for
novel-length consistency-checking. KV pool is util-bound (~862K tokens), so
256K costs no extra VRAM — max concurrency just drops to 3.29x at full length.
max-num-seqs 64 → 32 keeps the warmup transient flat (scales with seqs × len),
so it fits the tight GPU0 (free unchanged at 5.2 GB). Verified loaded + healthy.
Operator needs a verified-working vision tower as the abliteration/tuning
baseline. vLLM 0.23.0 crashes Mistral multimodal at startup (#44911
fetch_images regression, ~0.22.1+). Pinned the Mistral container to
v0.22.0 — the last pre-regression release — which loads the NVFP4
(compressed-tensors) AND serves vision: verified a half-blue/half-red
image read correctly ('left blue, right red'). Dropped --limit-mm
(vision re-enabled). qwen36 stays on 0.23.0 (separate container; needs it
for its ModelOpt NVFP4).
- gateway: add mistral-small-4-reasoning. Operator asked for effort=medium
but Mistral's reasoning_effort is BINARY (none/high only — medium 400s);
set to 'high' (sole reasoning-ON level). NOTE: reasoning fires but
reasoning_content-splitting is unreliable on v0.22.0 (lands in content);
clean split would need 0.23.0, which breaks vision — vision prioritized.
- mistral-small-4 (instant) + mistral-small-4-reasoning both gateway-live.
Mistral-Small-4-119B-2603-NVFP4 (119B/6.5B-active MoE, 65.3 GiB) on the
freed GPU 0 (dedicated 96 GB Blackwell), vLLM 0.23.0, :8010. NVFP4 is the
only variant that fits one card (FP8 ~119 GB / bf16 ~238 GB need 2 GPUs).
- TEXT-ONLY: vLLM 0.23.0's Mistral multimodal processor crashes at startup
(fetch_images bug); loaded with --limit-mm-per-prompt image/video=0.
Remove the flag to restore vision once vLLM patches it.
- MLA attn (TRITON_MLA), mistral tool-call + reasoning parsers, util 0.93,
max-len 131072 (capped from native 256K), image pinned by 0.23.0 digest.
- litellm: mistral-small-4 → :8010, shadows the * wildcard.
- GPU 0 reassigned from the (now-offline) llama-swap zoo per operator.
The qwen3.6-35b-a3b VL checkpoint is a single hybrid model with a per-
request enable_thinking switch (Qwen3-style), defaulting thinking ON.
Make the default non-thinking and add an opt-in reasoning variant,
mirroring the existing glm-5.1 / glm-5.1-reasoning gateway split.
- qwen36-vl compose: add --reasoning-parser qwen3 (model-matched) so the
single :8007 endpoint splits <think> into reasoning_content when on and
routes all output to content when off — serving both modes cleanly.
- litellm gateway: base qwen3.6-35b-a3b pins chat_template_kwargs
enable_thinking=false (non-thinking default); new qwen3.6-35b-a3b-thinking
pins enable_thinking=true (opt-in reasoning). Same upstream checkpoint,
no extra VRAM/container.
Deployed + verified on ana-ml2 (vLLM recreated, healthy) and ana-docker
(litellm reloaded): default returns a direct answer with no reasoning_content;
-thinking returns cleanly-separated reasoning_content, no raw tag leak.
Retire qwen35-vl (Qwen3.5-9B); add qwen36-vl serving the official FP8
Qwen3.6-35B-A3B vision MoE on :8007 under its TRUE name only — no alias.
qwen3.5-9b-fp8 is killed at vLLM AND the litellm gateway (404/400); a model is
never served under a prior model's name. Consumer (comfy-dev/arbo) notified +
migrated; arbo vkeys flipped to all-proxy-models; shared all-agents-local key
repointed to qwen3.6-35b-a3b.
GPU-1 rebalance for the heavier FP8 weights (~34 GB): granite 0.35->0.24 /
131K->64K, embed/rerank 0.05->0.03 (reclaimed util-reservation waste). Verified:
vision correct, 20-concurrent/endpoint load test = no OOM (~7.5 GB headroom).
Drop the llama-swap qwen3.5-9b GPU-0 pin (GPU 0 freed for the creative-writing
hot-swap card). NVFP4 was the lighter fit (~21 GB) but its vLLM ModelOpt-MoE
loader is broken (KeyError w2_input_scale / lm_head.input_scale, vllm #44081);
revisit when fixed.
Qwen3.5-9B vision-language served FP8 on ana-ml2 GPU1 (co-located with
granite + the embed/rerank/reward trio; GPU0 kept free for hot-loading
large models), :8007, fronted by LiteLLM as qwen3.5-9b-fp8.
Pinned to vllm/vllm-openai nightly@sha256:49211ab2 — :latest (v0.19.1)
quantizes the VL vision tower under fp8 and garbles vision; the nightly
correctly excludes it (LM stays FP8, vision tower BF16). util 0.40
(~38GB) on the shared card (vLLM needs free>=util*total here). Vision
verified end-to-end through the gateway.
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