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.
glm-5.1 now disables GLM thinking by default via extra_body (LiteLLM strips
top-level thinking under drop_params but forwards extra_body verbatim to z.ai).
New glm-5.1-reasoning alias = same upstream with thinking enabled, so reasoning
is opt-in. Operator call 2026-06-11; primary driver is the pi coding harness.
Verified live: glm-5.1 reasoning_tokens=0, glm-5.1-reasoning reasoning_tokens>0.
LLM observability for the fleet — pretty trace UI over the gateway: prompts,
completions, reasoning, latency, token counts. The pretty layer LiteLLM's
spend_logs lacked.
- stacks/langfuse: v3 self-host stack (web/worker/postgres/clickhouse/redis/
minio) on ana-docker, adapted from upstream. UI on :3001 (gitea owns :3000).
Project + API keys auto-provisioned via LANGFUSE_INIT_*. HOSTNAME=0.0.0.0 on
langfuse-web so it's reachable via the published port while also on tnet.
- litellm: enabled success_callback/failure_callback: ["langfuse"] (the
passthrough env was already wired); keys + host go in the litellm .env.
Verified: stack healthy, project keys authenticate, and a real gateway call
landed a litellm-acompletion trace in Langfuse within ~6s. Secrets live only in
the server .env (never committed).
Granite 4.1 8B beat phi4-mini on precision in brokkr's R15 P03 eval, so it's
the new production summarizer/dreamer for nevermore.
- vllm-phi4 -> vllm-granite: official IBM FP8 (ibm-granite/granite-4.1-8b-fp8,
compressed-tensors), GPU 1, 50K ctx, FP8-KV, CUDA graphs. Same :8004 slot.
- GPU 1 retune: the embed/rerank/reward trio was over-provisioned (embed ran a
5.89x KV pool, reward 3.90x). Trimmed utils 0.20/0.20/0.30 -> 0.07/0.07/0.18,
freeing ~10 GB so granite runs with CUDA graphs (not --enforce-eager) and
keeps ~10 GB free as a hedge for future Granite text-LoRAs (--enable-lora).
- LiteLLM: phi4-mini model_list entry -> granite-4.1-8b (hosted_vllm @ :8004);
explicit entry shadows the '*' wildcard's llama-swap route.
- nevermore repointed (LLAMA_SWAP_MODEL=granite-4.1-8b via the gateway) live.
Verified end-to-end: vLLM :8004 generates, gateway routes (gateway-granite-ok),
KV 86,768 tokens/1.69x at 50K, 0 restarts, GPU 1 10.3 GB free.
Adds explicit gateway entries for the four z.ai GLM models (glm-5.1,
glm-5-turbo, glm-4.7, glm-4.5-air) routed to api.z.ai with Z_AI_API_KEY,
plus the compose env passthrough + .env.example doc. Explicit entries
win over the llama-swap wildcard (distinct IDs, no collision). Extends
the gateway's unified logging to cloud inference, not just local
vLLM/llama-swap.
Cost note: paid API — only gateway-keyed callers reach these, but calls
spend z.ai credits (documented in config + compose comments).
Adds a `model_name: "*"` entry routing any unmatched model to llama-swap
(ana-ml2:9292) so its whole swappable LLM zoo logs through the gateway
without per-model registration — add/swap models in llama-swap freely,
litellm logs them all. Exact entries (phi4-mini/qwen3-embedding/
qwen3-reranker → vLLM) still win; the wildcard only catches the rest.
litellm does no inference; llama-swap keeps loading + serving. Enables
routing worldtree-personal's generative chat through the gateway for
full req/resp logging while preserving llama-swap's on-demand swapping.
LiteLLM proxy fronting the vLLM services on ana-ml2 so every request +
response is captured and inspectable in a browser Logs UI — the
visibility vLLM itself lacks (Dozzle shows only connection metadata).
- compose: litellm (proxy + /ui Logs) + litellm-db (Postgres store)
- conf/config.yaml: routes phi4-mini (chat, :8004), qwen3-embedding
(:8001), qwen3-reranker (:8002); store_prompts_in_spend_logs persists
full prompt/completion text. reward classifier (:8003) stays direct
(no first-class LiteLLM route).
- Langfuse-ready: lean first cut intentionally skips Langfuse's heavy v3
stack; graduating is one env-var + callback step, no re-architecture.
- roadmap: mark the vLLM-observability item's first cut as shipped.
Lean first cut of docs/roadmap.md "Observability for the vLLM stack".
vh