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.
Native-allocator expandable segments to cut Qwen-Image-Edit fragmentation
OOMs on the A6000 (a ~2 GB alloc failing with 1.75 GB free while 45 GB sat
allocated + reserved-but-unallocated). Cache-preserving — packs better
without unloading the checkpoint, so no edit-latency hit. Paired with the
existing --disable-cuda-malloc (incompatible with cudaMallocAsync).
Deployed + recreated on irv-ml1; verified env present, PyTorch reads it,
container healthy. comfy-dev request 2026-06-25.
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.
Empirical follow-up to the streaming /tts smoke test on the 3090. OmniVoice
is diffusion: a ~fixed per-call overhead (~1.5s at 32 steps, ~0.7s at 16)
dominates regardless of chunk length, so the upstream-claimed 40x RTF does
NOT hold here (measured ~2.8x/32-step, ~5.6x/16-step) and the chatterbox-
tuned scheduler over-chunks and starves.
- Streaming /tts defaults to num_step=16 (TTFA ~1.5s -> ~0.7s); batch
/v1/audio/speech stays num_step=32 for quality. Per-request override intact.
- Scheduler prior raised to rtf_prior=20 (env OMNIVOICE_STREAM_RTF_PRIOR,
wired through compose + .env.example) so it packs whole-text-minus-first-
sentence into a few chunks: validated ~3 chunks, no starvation, total wall
~= one-shot, less per-chunk silence padding.
- Docs corrected: the "sub-second / 40x" claims were wrong; streaming has a
diffusion TTFA floor (~0.7s) and wins mainly on long replies. chatterbox-
fast (autoregressive, ~0.5s TTFA) stays the lowest-latency front-end;
OmniVoice is the multilingual / voice-design complement.
Add a live-consumer streaming path and text sanitation to the OmniVoice
wrapper, so it can front speech-to-speech chat engines (not just the
asset-engine's batch WAV use).
- POST /tts: chunked 24 kHz mono s16le PCM (or open-ended WAV), driven by
the adaptive buffer-ratchet scheduler. Emits the first sentence
immediately, then ratchets chunk size up on OmniVoice's ~40x realtime
headroom -> sub-second time-to-first-audio. Wire-compatible with
chatterbox-fast /tts (both 24 kHz mono PCM). Batch /v1/audio/speech is
unchanged for asset/file callers.
- scheduler.py: VENDORED byte-faithful copy of chatterbox-fast's pure-
Python (torch-free) scheduler, pinned to commit 7631462 (v0.1.0/v0.1.1).
Vendor-copy over a shared package (operator call 2026-06-19): the module
has no GPU deps, so reuse it without dragging chatterbox-fast's torch
tree into this image. Promote to a shared package only on a 3rd consumer
or real drift.
- sanitize.py: language-safe TTS sanitizer run on both endpoints. Strips
markdown, <think> blocks, HTML, and model control tokens; deliberately
SKIPS the fork's English-only number/phone normalization that would
corrupt OmniVoice's 600-language input. Preserves [laughter]-style tags.
- Refactor: shared GenParams base for SpeechRequest + TTSStreamRequest;
single GEN_LOCK serializes generation (single-stream interactive).
- Dockerfile/playbook: copy + upload the two new modules; build-time
`import app` smoke; correct stale "Gradio demo / no FastAPI" comments.
Stands up tools/gateway-chat.html as a permanent URL on ana-docker (http://10.250.50.70:8091)
via a tiny nginx:alpine static container (no GPU, no DB). conf/index.html is a deployed
mirror of tools/gateway-chat.html (re-sync one-liner in README). Homepage tile + tnet per
convention. The enhanced tool (auto-discovers /v1/models, system prompts, streaming +
reasoning, image upload for vision) is now always-on for smoking new gateway models.
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.
Replaces the bjk110 text-only qwen3.5-122b as the `gen` model on ana-ml2 GPU 0.
OpenYourMind/Qwopus3.5-122B-A10B-Kimi-K2.6-destilled-abliterated-NVFP4 — Kimi-
distilled, abliterated, NVFP4, and crucially VISION-INTACT (serves as plain
multimodal, no text-only patch). Served as qwen3.5-122-a10b so the litellm
gen / gen-reasoning / qwen-large records route here unchanged.
Tuned for full native context on the 96GB Blackwell:
- stable vLLM image + fp8 KV → 11GB pool = 870,014 tokens = 3.32x concurrency
at the full 262144 (256K) window. Nightly+turboquant-4bit was unnecessary.
- CUDA graphs ON (no --enforce-eager) → 92.7 tok/s warm single-stream.
- util 0.95 + PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True — 0.96 OOM'd by
0.1GB on the 3.09GB FusedMoE transient workspace (the hard floor; defrag
reclaims the 4.2GB fragmentation, 0.95 adds margin).
- max-num-seqs 16 (short reqs fan out ~16x32k; 256K reqs pool-limit to 3.32x).
- text + image + video all enabled; tool-calling via qwen3_coder (XML), verified.
gen/gen-reasoning tool-calling 400'd (operator + brokkr's capability battery both caught it):
the bjk110 serve command shipped --reasoning-parser qwen3 but no tool flags. Qwen3.5 emits XML
tool calls (<tool_call><function=NAME><parameter=K>V</parameter></function></tool_call>), NOT
Hermes JSON — so `hermes` mis-parsed to raw text; `qwen3_xml` is the correct parser. Reasoning +
tools coexist (gen-reasoning keeps its thinking split). Verified live: a get_weather request
returns tool_calls=[get_weather {"city":"Paris"}].
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.
Wrapper /v1/audio/speech now accepts OmniVoice's whole surface:
- voice (clone, now OPTIONAL) and/or instruct (voice DESIGN). instruct is a CONTROLLED
vocabulary (gender/age/pitch/accent/whisper tags, comma-separated), not free prose —
discoverable at the new /v1/audio/instruct-items endpoint (23 items).
- language (Auto + 647, new /v1/audio/languages endpoint), speed, duration.
- diffusion controls: num_step, guidance_scale, denoise, preprocess_prompt,
postprocess_output; plus a generation_overrides JSON passthrough for expert
GenerationConfig knobs (t_shift, layer_penalty_factor, position/class temperature,
audio_chunk_*).
- at least one of voice/instruct required (else 400).
Catalog (services.yaml): omnivoice v1 -> v2, 13 schema-valid fields; instruct as a
controlled-vocab text field sourced from the items endpoint.
Verified live on irv-ml1: clone, voice-design (instruct-only), and tuned-param synths
all -> 24 kHz PCM_16 WAV; 647 languages; 23 instruct items.
- app.py: thin FastAPI wrapper exposing OpenAI /v1/audio/speech (+ /v1/audio/voices,
/healthz) around OmniVoice's Python API; precomputes a voice-clone prompt per voice
at startup (loaded Whisper auto-transcribes each reference). Replaces the Gradio demo.
- Dockerfile/compose: run the uvicorn wrapper, /healthz healthcheck, project name pinned
to "omnivoice" so the asset-engine liveness probe matches.
- deploy-omnivoice.yaml: stage chatterbox /refs/*.wav as clone voices (skip _* artifacts)
+ verify the API surface.
- services.yaml: catalog entry (id omnivoice, :8199/v1/audio/speech, voice list sourced
live from /v1/audio/voices) + reproducibility_audit row.
Verified live on irv-ml1: /healthz ok, 33 voices loaded, test synth -> 24kHz PCM_16 WAV.
Zero-shot, massively-multilingual (600+ language) voice-cloning + voice-design
TTS (diffusion-LM, Apache-2.0). No official image, so a thin CUDA container
around the pip package running upstream's own Gradio demo (no FastAPI wrapper).
Pinned to GPU 0 (3090) — the A6000 is ComfyUI-exclusive — port 8199. Built +
verified live on irv-ml1 (Gradio 200, container healthy). Surface is the Gradio
UI + Gradio API, NOT OpenAI-compat /v1/audio/speech (wrap later if asset-engine
should consume it). deploy-omnivoice.yaml builds local + verifies.
ComfyUI 0.24.1 added native attention selection; the node-based
BlehGlobalSageAttention errors "does not support the new ComfyUI attention
changes". Add --use-sage-attention to COMFY_CMDLINE_EXTRA so the in-image
sageattention v2.2.0 sm_86 build (rebuilt vs pinned torch 2.12.1) binds via
the native path. OOM flags preserved. Deployed to irv-ml1 + recreated; log
confirms "Using sage attention", container healthy, serving 200.
(comfy-dev request, thread 01KVE89T2DKC)
Operator consolidation (2026-06-18): give ComfyUI the full 48 GB A6000 and move the
audio/TTS zoo to the 3090.
- comfyui: NVIDIA_VISIBLE_DEVICES all -> 1 (A6000 only), + DISABLE_UPGRADES=true to
pin torch at 2.12.1+cu129 so the mmartial boot script stops auto-upgrading it and
the compiled SageAttention kernels stay matched (comfy-dev torch-pin, approved).
- parakeet: NVIDIA_VISIBLE_DEVICES all -> 0 (3090).
Other GPU reassignments are deployment-side (not repo compose): chatterbox-fast via
its .env CBF_GPU_DEVICES=0; vibevoice device_ids ["1"]->["0"] (deployed from
/worktank/vibevoice/build); yt-voice-clipper worker via its override. dia2-2b,
ace-step, csm-expressiva downed (stale/unused).
Result: A6000 = ComfyUI alone (48.3 GB free); 3090 = chatterbox + parakeet + the
on-demand audio (vibevoice/ytvc/kokoro). SageAttention rebuilt against the pinned
torch; OOM cmdline (COMFY_CMDLINE_EXTRA) preserved; /object_info still lists the 9
acceleration nodes.
Serves the in-house abliterated Mistral Small 4 (heretic NVFP4, vision-intact)
under --served-model-name mistral-small-4 on ana-ml2 GPU0:8010 — a true drop-in
for the official mistral-small-4 backend. Both litellm entries (mistral-small-4 +
mistral-small-4-reasoning) route here with no litellm change.
GPU0 fits one mistral-class model, so this is a backend swap, not a co-tenant:
bring up after downing the official stack; revert by downing this and up-ing the
official. Verified live through the gateway: standard returns clean answers,
reasoning populates reasoning_content (the [THINK] split). Checkpoint built per
tools/mistral-small4-nvfp4/.
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).
/healthz catalog_version read the BAKED package version (importlib.metadata),
so a catalog/frontend-only webhook deploy (no image rebuild) left it stale —
v0.12.4 data went live but /healthz still reported 0.12.3. comfy-dev's v0.12.5
reads comfy_catalog.__version__ from the repo-root pyproject.toml; mounting it
on the same checkout mount (catalog/graphs/frontend) makes /healthz report the
MOUNTED version after a catalog-restart. Falls back cleanly if absent.
Pushed to irv-ml1's host compose + validated via `docker compose config`
(bind -> /app/pyproject.toml:ro resolves). Recreate deferred to comfy-dev's
imminent v0.12.5 rebuild (the mount is inert for v0.12.5 itself, which bakes
its own version — it only matters for subsequent catalog-only deploys — so no
separate prod blip). Requested by comfy-dev (engine owner), althing thread
01KV95R88A3Y.
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.
Put GPU1's idle ~11 GB buffer to work on the two KV-bound models that gained
live consumers from the worldtree migration (granite + the pooling models
under-use their util, so growing them is wasted):
- selene 0.13→0.17: KV 2.53→6.33 GiB, concurrency 1.27x→3.16x @32K (Domari judge)
- qwen36 0.32→0.34: KV 7.73→9.63 GiB, concurrency 2.92x→3.64x @131K (arbo judge +
worldtree actor/echo + gateway)
GPU1 free now ~5.6 GB (safe floor for single-service recreates).
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.
vh