Benchmarked selene against gen on selene's own job: 24 designed judge items
with checkable ground truth, pairwise + absolute modes, 3 repeats, on BOTH a
neutral JSON prompt and Selene's native Atla template. 288 calls, all free
local.
neutral JSON selene 20/24 (83%) gen 23/24 (96%)
native Atla selene 21/24 (88%) gen 22/24 (92%)
gen won on both templates and selene's BEST sat below gen's WORST. Selene was
given its own fine-tuned template as a fairness check; it gained one point,
not the three it needed.
Decisive defect: selene cannot emit "tie" -- 0/2 on both templates, forcing a
winner on every equivalent pair. For eval work that is the case that matters
most. gen returned tie correctly on the JSON template. Selene also compressed
the 1-5 scale (clustered at 2s and 4s) where gen used it fully. Selene's only
win was ~3x latency, unexercised at ~60 calls/day with zero queueing.
TWO NAMES, TWO DIFFERENT TREATMENTS, deliberately:
- chat-judge -> repointed to gen. It is a ROLE alias and ADR-0012 says
consumers bind the capability, not a concrete model. Sampler profile copied
from image-judge (temp 0, top_p 1.0, top_k 1, thinking off) so the served
config matches the benchmarked condition.
- selene-1-mini-8b -> REMOVED. It 404s. It was NOT aliased to gen. A
served-name is a contract about what the model IS; answering it with a
different model hides a material change behind a stable string. Operator
ruling: "never repoint a named model at a different model's endpoint --
that is intentionally misleading." Verified: the gateway now returns
HTTP 400 "Invalid model name" for it.
Reclaimed 17.2 GiB on ana-ml2 GPU 1 (free 1,818 -> 19,450 MiB) on a card that
had under 2 GiB of headroom. gen already runs on GPU 0, so the judge role
moved onto an existing seat rather than allocating anything new.
Canonical litellm config synced from the host; ana-ml2 README and
recommended-model-settings updated. compose.yaml kept for reference, not
deployed.
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.
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