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=