Created private vh/zonos-gateway on gitea, imported the previously-unversioned
~/zonos-gateway working tree (source + dials-first spec + voices). Updated the
stack README, spec §8, and the sister-repos table to point at the repo. Remaining
follow-up: CI + deploy key to wire the irv-ml1 deploy tree to the repo.
Canonical direction: emotion set by twisting raw dials per-utterance, not preset
selection. Presets demoted to optional examples. Spec covers the dial vocabulary
+ ranges, the emotion_cfg_scale 'deaf by 1.5' rule (NO cap — documented ceiling,
explicit over implicit), measured RTF cost, starting-point dial-sets, an LLM
client system-prompt snippet, and clone reference guidance (~15-24s, no
transcript). Follow-ups flagged: align dials.py cfg help/metadata, trim in-code
PRESETS pending usage check, stand up vh/zonos-gateway for version control.
Captures the production Zonos TTS path that the repo was blind to: stock ZONOS2
:1920 engine (stacks/zonos-engine) fronted by zonos-gateway:0.2.0 :8890, reached
via the LiteLLM ext-tts alias. Documents the emotion-preset system (neutral/warm/
excited/sad/intense/whisper, the simple preset: caller path), the API, and the
measured real-time cost (calibrated steering free at RTF~0.52, cfg1.5 ~0.625 —
still realtime). Flags the gateway source (~/zonos-gateway on irv-ml1) as not yet
in gitea. Marks stacks/zonos (v0.1 Gradio) dead/superseded. Records the emotion-
lever finding (text-priming flat -> native steering works) in persistent memory.
The Zonos TTS engine that zonos-gateway fronts (irv-ml1 3090, feeds asset-engine +
gateway-chat) ran as a bare native process with its real invocation existing ONLY
in the running process argv — the committed harness/zonos_server.sh on irv-ml1 was
STALE (said A6000/:1919, no perf flags; live is 3090/:1920 with cuda-graph/num-pages/
max-running-requests/memory-ratio). Captured the corrected canonical invocation +
tunables + the containerization plan here so the config survives a process death.
Engine = stock Zyphra/Zonos2 @ 194c0a3 (no custom PFI server code); torch 2.9.1+cu128;
15 GB HF weights. Next: containerize in-place on the 3090 (operator: keep off the
A6000, it OOMs under ComfyUI). Not yet built — this commit is the config capture only.
granite (fleet fan-out summarizer/classifier) had no explicit --max-num-seqs,
so vLLM V1 resolved it to 128 — which caps concurrency BELOW granite's own KV
bound (~192 concurrent @ 1K-token calls, more for shorter classify calls).
Pinned it very high (1024) so the KV pool is the only bound; VRAM-neutral
(the KV pool is util-bound, unchanged). Added the flag to the granite command
+ GRANITE_MAX_NUM_SEQS to the env template. Live applied + verified
(resolved max_num_seqs=1024, seat healthy).
Operator-directed 2026-07-16. Moved the char-rp prose seat (Magidonia-24B,
llama-charrp) from GPU0 to GPU1 (CHARRP_GPU_ID 0->1; recreate llama-charrp
only -- the var is shared with the retired GGUF reasoning service), then
re-optimized every context-relevant seat on both cards to native/max context
with acceptable headroom:
GPU0 (both seats now 256K native, ~14 GB reserve):
- char-rp-reasoning 150K -> 256K (heretic2 stack, util 0.38->0.46, 1.56x)
- gen 256K, max-num-seqs 16 -> 32 (qwen36-27b-aeon, util 0.30->0.42, 5.43x)
GPU1 (~6.7 GB headroom):
- granite 64K -> 128K full-chapter (vllm stack, util 0.18->0.27, 1.50x)
- char-rp 128K native (4 slots), selene/reward/embed/rerank unchanged
All seats gateway-verified healthy. Live .env changes on ana-ml2 with per-stack
backups (*-20260716). Templates updated to match; the qwen36-27b-aeon template
carries a NOTE that its served-name/model still lag the 2026-07-08 gen model swap
(35B-A3B-heretic) -- separate reconciliation. persistent-memory records the full
layout + the util-floor / per-model-KV-cost lessons.
Note: the heretic2-charrp-reasoning stack (char-rp-reasoning's live config) is
still untracked in git -- standing open-loop, its .env change lives server-side only.
Operator-directed 2026-07-16: free ~10GB on ana-ml2 GPU1 to relocate a GPU0
model onto GPU1. granite-4.1-8b (fleet summarizer) was over-provisioned at
util 0.34 / max-model-len 131072 with a flat 0.0% KV usage.
Set GRANITE_GPU_MEM_UTIL 0.34 -> 0.18 and GRANITE_MAX_MODEL_LEN 131072 -> 65536
on the live /opt/docker/compose/vllm/.env (backup .env.bak-pre-granite-rightsize-
20260716), recreated vllm-granite ONLY (shared stack). Result: GPU1 62,641 ->
51,897 MiB used (~10.5GB freed, ~45GB free now); KV 6.45 GiB / 84,528 tok /
1.29x concurrency @ 65536; summarizer verified healthy.
The util drop required the max-len drop: on this shared card the effective KV
slope is ~950 MiB per 0.01 util, and vLLM refuses to start unless the KV pool
holds >= 1x max-model-len -- util 0.15 undershot (est max-len 47184 < 65536,
crash-loop, ~2-3 min summarizer outage) before 0.18 landed. 65536 is granite's
precedented summarizer ctx; a summarizer doesn't need 131072.
.env.example updated to the new util (max-len was already 65536 in the template;
live had drifted to 131072). persistent-memory.md updated (parked item closed).
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).
Move the ~22-service flat "AI Systems" group off the Main tab into a new
four-tab layout (Main / AI / Infrastructure / Toolchain). The AI tab sorts
the inference fleet by function into seven groups:
AI - Inference gen, char-rp, char-rp-reasoning, Granite summarizer
AI - Eval & Retrieval Selene, Skywork Reward, Qwen3 rerank/embed, image-bench
AI - Gateways & Chat LiteLLM, Asset Engine, Gateway Chat, Open WebUI, ...
AI - Speech (TTS) Chatterbox Fast, Kokoro, mOrpheus
AI - Audio Tools Parakeet ASR, YT Voice Clipper
AI - Image & Media ComfyUI, Arbo
AI - Dormant stopped rollback seats + retired auditions
Relabel each stack's homepage.group so canonical stacks/ matches the live
containers on ana-ml2, ana-docker, and irv-ml1. Dormant stacks were refreshed
with `docker compose up --no-start` so they carry the new label while staying
stopped (compose-start rollback preserved). settings.yaml drives tab/order/
columns; services.yaml and README updated to the new scheme.
R36 gate (2026-07-14) validated NEO-CODE ships on all axes: tool-calling 0.967
(attach_tool 1.00, 0 runaways — #355 eliminated), prose genre-artifact-fine
(less clichéd than gen), refusal uncensored-as-spec + CSAM-clean.
#355 root cause was MODEL-level, not the reasoning-budget-forcing bug: Deckard
emitted Qwen's native qwen3_coder XML tool format malformed -> llama.cpp leaked
the closing tags into the arg value -> Bifrost attach_tool schema error -> retry
-> reasoning runaway to max_tokens. NEO-CODE emits the same native format cleanly
on the same seat/parser -> no schema error -> no runaway. The fix was the model
swap; there was never a wrong parser (the XML is Qwen3.5/3.6-native).
- reasoning seat: Deckard-PKD (Qwen3.5) -> NEO-CODE=Heretic2-Thinking (Qwen3.6-27B) Q5
- samplers: card defaults (temp 1.0 / top_p 0.95 / top_k 20 / min_p 0.0), DRY dropped
- ctx: 256K max; custom llama.cpp kept (qwen3_coder parse + PR#25544 belt-and-suspenders)
- persistent-memory ACTIVE 1 marked resolved
The char-rp-reasoning (Deckard) seat now runs llamacpp-charrp:custom-latest via a
new LLAMA_REASONING_IMAGE var (Magidonia char-rp stays on stock — no reasoning bug).
Fixes Worldtree #355 at the source (budget multi-terminator handles Qwen3.5's
<tool_call> reasoning end-tag). Live 2026-07-13: Deckard loads on Blackwell, serves
coherent, reasoning bounds at the 400 budget. Rollback via .env LLAMA_REASONING_IMAGE.
Durable record of the custom llama.cpp the char-rp-reasoning seat will run to
fix Worldtree #355 (reasoning-budget forcing broken in stock b8840 — single
end-tag </think> can't match Qwen3.5's <tool_call> reasoning terminator, so the
budget never force-closes and reasoning runs away to max_tokens). PR #25544
adds multiple terminating sequences; unmerged upstream, so we build it.
- build.sh: reproducible recipe (clone master@6eddde0 + merge PR #25544 +
resolve the 1 server-common.cpp conflict + CUDA build for Blackwell sm_120)
- README.md: why + acceptance test + rollback + REMOVE-WHEN-MERGED tracking
Image llamacpp-charrp:6eddde0-pr25544 BUILT + smoke-tested on ana-ml2; seat
swap pending. See also auto-memory reference_charrp_custom_llamacpp_pr25544.
Drop-in system prompt for an agent whose quoted output is voiced by mOrpheus: speak in
double quotes (only quoted text is voiced), phrase each quoted line as one coherent
utterance (per-quote prosody), and the sparse/boundary/no-stack tag rules (measured
stability on the early checkpoint). Lists the honored tag set.
Per-sentence chunking generated each sentence cold, flattening intonation/prosody that
spans the whole quoted line. Chunk by QUOTED SECTION instead — each contiguous quote is
generated whole (max_tokens 2400) so its prosody stays intact; multiple quotes in a reply
still play serially on the shared clock. extractQuotes already returns exactly these spans;
dropped splitSentences.
Split the quoted dialogue into sentences and stream each as its own short /tts/stream
request (max_tokens 900), queued back-to-back on one shared AudioContext clock (speechHead)
so playback is gapless and in order. First sentence starts fast; each chunk is short so it
generates cleanly (no ramble/cap risk); the next sentence generates while the current plays.
A newer reply supersedes via the ttsGen counter; 🔊 replays.
Browsers suspend the Web Audio AudioContext until a user gesture; speakQuotes fires on
reply-complete (no active gesture), so a suspended context played silently. Prime/resume
the context on any click or keydown (capture phase) so it's running before playback.
Server side was fine throughout (/tts + /tts/stream both 200 with valid audio).
- max_tokens default 2400->3500 (~42s) in wrapper + gateway-chat client, with a _cap()
clamp so prompt+gen never exceeds MAX_CTX (4096) — a cloning ref block is ~1100 tokens,
so an unclamped 3500 would overflow context on the clone path.
- Staged clone voices: /voices dir of <name>.wav + <name>.txt, each encoded to its Orpheus
reference block at startup; voice="<name>" zero-shot clones it. Beatrice (a chatterbox
reference) staged as the first normal-voice clone. GET /voices lists baddy + clones.
- compose: mount voices dir + pass MORPHEUS_MAX_LEN to the wrapper (clamp must match engine).
vLLM concurrency (measured, --max-num-seqs 8, 250-tok reqs): near-linear batching — 8
concurrent finish in the same ~2.8s as 1 (707 tok/s, 8.1x single, flat per-req latency).
Chunked-sentence production can fan out for ~8x throughput; CPU SNAC decode is the scale
bottleneck, not generation.
Cut-offs were the max_tokens=1200 ceiling (~14.6s of audio), not memory (~1250 tokens
<< 4096 context). Diagnosis: the repetition penalty is load-bearing for clean stops —
rep 1.0 => the model never emits end-of-speech and rambles to the cap; rep 1.1 (the
wrapper default) => clean natural stop. So normal lines already complete; only genuinely
long dialogue (>~14.6s, ~25+ words) hit the cap. Raised default + client max_tokens to
2400 (~29s), still within the 4096 context (no memory cost). Verified: a 49-word line
now finishes at 16.73s (was clipped at 14.6s).
Wrapper gains POST /tts/stream: reads the vLLM token stream, decodes SNAC in WINDOWED
CHUNKS (every 6 frames, decode [2 ctx | 6 | 2 ctx] and emit only the middle 6 — context
both sides => seamless), and streams raw PCM16 (24kHz mono) as it generates. Windowed
(not per-frame) because per-frame CPU decode's per-call overhead x ~60 frames serialized
to ~7s (RTF 2.2); windowed keeps up (RTF ~0.97). Whole-clip /tts kept for non-browser use.
gateway-chat plays the stream via the Web Audio API (fetch reader -> int16->float32 ->
scheduled AudioBufferSourceNodes on a running clock; a new reply supersedes the prior
stream via a generation counter; 🔊 replays). Measured: TTFA 0.80s (was ~4.5s whole-clip),
RTF 0.97, full-duration match. CORS already covers the new route.
Deployed: tts rebuilt on irv-ml1, page pushed to ana-docker.
Gateway-chat now auto-plays quoted text from each assistant reply through the mOrpheus
TTS endpoint. Sidebar gains a 🔊 toggle + endpoint/voice fields (persist in localStorage,
prefilled to irv-ml1:8299 / baddy). On reply-complete, straight and typographic double
quotes are extracted, joined, POSTed to /tts, and the returned WAV plays (click 🔊 to
replay; a new reply interrupts the prior clip).
Requires CORS on the wrapper (page served from ana-docker:8091 fetches irv-ml1:8299
cross-origin) — added CORSMiddleware(allow_origins=[*]) to the mOrpheus tts app (internal-
only endpoint). Verified end-to-end: preflight + POST return ACAO=*, valid 24kHz WAV.
Deployed: tts container rebuilt/recreated on irv-ml1; page pushed to ana-docker conf
(bind-mounted, live on next request).
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.
vh