feat(selene+mistral): restore Selene judge (FP8, GPU1) + push Mistral to 256K

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.

stacks/mistral-small-4/.env.example

@@ -22,14 +22,17 @@ MISTRAL_PORT=8010
 # the embed/rerank/reward trio.
 MISTRAL_GPU_ID=0
 
-# util 0.93 (~89 GB budget) — 74.4 GB weights + ~5 GB CUDA/graph leaves ~10 GB
-# KV. MLA keeps KV compressed so 131072 ctx fits; raise toward native 256K only
-# after measuring real KV headroom. Dedicated card, so 0.93 is safe.
+# util 0.93 (~89 GB budget) — measured 66.1 GiB weights + 0.8 GiB graph + ~18.5 GiB
+# KV. MLA keeps KV compressed, so the FULL native 256K context fits (verified).
 MISTRAL_GPU_MEM_UTIL=0.93
-MISTRAL_MAX_MODEL_LEN=131072
-# Single-card KV is tighter than the official TP=2 setup → cap concurrency at 64
-# (official used 128 across two cards).
-MISTRAL_MAX_NUM_SEQS=64
+# 262144 = native 256K (for novel-length consistency-checking). The KV pool is
+# util-bound (~862K tokens) regardless of max-len, so 256K costs no extra VRAM —
+# it just lets one request use up to 256K (→ max concurrency 3.29x at full length).
+MISTRAL_MAX_MODEL_LEN=262144
+# 32 (halved from 64 when going to 256K): the warmup transient scales with
+# max-num-seqs × max-model-len, so halving seqs while doubling len keeps it flat
+# and fits the tight card. 32 is ample — this is a low-concurrency creative model.
+MISTRAL_MAX_NUM_SEQS=32
 
 # Optional — model is ungated (Apache-2.0), no token needed.
 HF_TOKEN=

stacks/mistral-small-4/compose.yaml

@@ -19,16 +19,17 @@
 # reference 80 GB cards can't fit 74.4 GB + context on one). The 96 GB Blackwell
 # flips that to single-card: 74.4 GB weights + ~5 GB overhead leaves ~17 GB for
 # KV. Mistral Small 4 uses MLA attention (TRITON_MLA) so KV is compressed/cheap —
-# big context stays affordable even on a constrained KV pool. max-model-len is
-# capped to 131072 on first bring-up (raise toward the native 256K once real KV
-# headroom is measured).
+# big context stays affordable even on a constrained KV pool. We serve the FULL
+# native 256K (max-model-len 262144) — the KV pool is util-bound (~862K tokens)
+# so 256K costs no extra VRAM, it just lets one request use up to 256K (max
+# concurrency 3.29x at full length). For novel-length consistency-checking.
 #
-# vLLM FLOOR: needs >= 0.20 (Mistral Small 4 day-0 support); validated on 0.23.0.
-# Do NOT reuse the qwen36-vl 0.19.1 image — it predates this model.
+# vLLM PIN: v0.22.0 (in .env) — the last release with WORKING Mistral vision
+# (#44911 fetch_images regression hit 0.22.1+/0.23.0). See the .env header.
 #
 # Serve flags mirror Mistral's official command (cited in README), adapted for
-# single-card: TP 2->1, util 0.8->0.93, max-len 262144->131072, max-num-seqs
-# 128->64. All tunables live in .env — edit that, not this file.
+# single-card: TP 2->1, util 0.8->0.93, max-num-seqs 128->32 (32 keeps the
+# 256K warmup transient flat). All tunables live in .env — edit that, not this file.
 
 name: mistral-small-4