Files
vh 52612cbe96 feat(reward-seat): move Skywork reward seat from fv-ml1 to esh-ml1; audit finds nothing superseding it
- Audit: Skywork-Reward-V2-Llama-3.1-8B is still #1 of 188 on AllenAI's
  RewardBench 2 per-sample results; no Skywork V3; the -40M sibling is
  vendor-marked experimental. Our AWQ W4A16 quant: 0.847 vs published bf16
  0.860 on a 150-prompt sample (within +/-2.9 pt SE), 96.2% pairwise
  agreement. Double BOS from vLLM on pre-templated text costs a further
  ~2.7 pts; callers must send add_special_tokens=false.
- No working consumer: 0 requests since 2026-09-13; Worldtree Domari points
  at a dead IP with a non-vLLM schema (reported to worldtree-dev).
- Move: sha256-identical model copy; vLLM v0.24.0 on esh-ml1 :8003 at 0.55
  util (KV 1.30x of a 16k request). Parity vs fv-ml1: 149/150 verdicts,
  99.8% pairwise signs, raw |delta| median 0.049.
- Gateway /scalar-judge passthrough -> 10.0.50.80:8003; fv-ml1 vllm-reward
  removed (~10.2 GB freed on GPU 1). stacks/vllm now holds only vllm-coder.
2026-09-25 09:09:48 -07:00

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YAML

# vLLM — the coder FIM seat on fv-ml1 (the last utility seat left here).
#
# Originally created to replace the unmaintained Infinity stack (embed +
# rerank); generalized 2026-05-13 to host any vLLM-served model on fv-ml1,
# starting with the Skywork-Reward-V2-Llama-3.1-8B reward classifier
# (AWQ-quantized locally, served from /tank/aimodels/llm/).
#
# ⚠ Embedding + reranking LEFT this stack 2026-09-25 (Prime): they now run on TEI
# on esh-ml1 (stacks/embed-rerank), and TEI is the fleet's embed/rerank engine
# from now on. The reward classifier moved to esh-ml1 the same day
# (stacks/reward-seat). Do not re-add them here.
#
# All tunables live in .env — edit that, not this file.
#
# Skywork-Reward-V2-Llama-3.1-8B-AWQ is a locally-quantized model. The canonical
# copy still lives at /tank/aimodels/llm/Skywork-Reward-V2-Llama-3.1-8B-AWQ on
# fv-ml1; the serving copy is on esh-ml1. Not from HF Hub.
services:
# vllm-embed (Qwen3-Embedding-0.6B, :8001) and vllm-rerank-a3
# (bge-reranker-v2-m3, :8013) were RETIRED 2026-09-25 (Prime): embedding and
# reranking moved to TEI on esh-ml1 (stacks/embed-rerank), and TEI is now the
# fleet's embed/rerank engine. The gateway names `qwen3-embedding`, `reranker`
# and `reranker-a3-bge-v2-m3` did not change. Their definitions are in git
# history before this commit if a rollback is ever needed.
# vllm-reward (Skywork-Reward-V2-Llama-3.1-8B AWQ, :8003) — MOVED to esh-ml1
# 2026-09-25 (stacks/reward-seat); the gateway `/scalar-judge` passthrough
# follows it. The model files stay at /tank/aimodels/llm/ on fv-ml1 as the
# canonical copy of the local quant.
# vllm-granite (ibm-granite/granite-4.1-8b-fp8, :8004) — RETIRED 2026-08-12,
# service block removed 2026-08-20. It was the fleet summarizer until the
# `summarizer` and `classifier` aliases were repointed at the gen seat; the
# container was stopped then and sat Exited for 8 days while this block still
# claimed it as production.
#
# ⚠️ Retiring the SEAT did not retire its CONSUMERS, and nobody checked. The
# `granite-4.1-8b` gateway alias was deleted at the same time, but `nevermore`
# was still pinned to that alias by name — so its LLM summarization pass failed
# 67 consecutive times over 8 days, 0 tokens, entirely silently, because a
# failed gateway call still returns 200-shaped spend-log rows and nothing
# alerts on status=failure. Found 2026-08-20 only because someone asked an
# unrelated question about reranker VRAM.
#
# The rule this earns: RETIRING A MODEL IS A TWO-SIDED OPERATION. Grep every
# consumer's config for the alias BEFORE deleting it, and prefer that consumers
# pin stable ALIASES (`summarizer`) over model names (`granite-4.1-8b`) so the
# gateway can repoint them without anyone editing a downstream .env.
vllm-coder:
image: vllm/vllm-openai:${VLLM_VERSION}
container_name: vllm-coder
restart: unless-stopped
ipc: host
ports:
- "${CODER_PORT}:8000"
volumes:
- /tank/aimodels/huggingface:/hfcache
environment:
- HF_HOME=/hfcache
- HF_HUB_CACHE=/hfcache/hub
- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
- VLLM_API_KEY=${API_KEY:-}
command:
# Qwen2.5-Coder-1.5B (BASE) — FIM code-completion seat for Zed edit-predictions
# (deep-research pick 2026-07-27). Native fill-in-the-middle: <|fim_prefix|> /
# <|fim_suffix|> / <|fim_middle|> (IDs 151659/151660/151661); Zed sends the
# FIM-formatted prompt to /v1/completions and vLLM passes it through (the FIM
# special tokens live in the tokenizer). BASE not -Instruct (FIM is a
# pretraining objective; base completions are cleaner). Apache-2.0. Runner-up =
# Qwen2.5-Coder-3B (higher HumanEval-FIM but non-commercial Qwen-Research license).
- ${CODER_MODEL}
- --served-model-name
- ${CODER_SERVED_NAME}
- --host
- 0.0.0.0
- --port
- "8000"
- --gpu-memory-utilization
- ${CODER_GPU_MEM_UTIL}
- --max-model-len
- ${CODER_MAX_MODEL_LEN}
- --max-num-seqs
- ${CODER_MAX_NUM_SEQS}
- --dtype
- auto
- --kv-cache-dtype
- ${CODER_KV_CACHE_DTYPE}
- --enable-prefix-caching
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids:
- "${CODER_GPU_ID}"
capabilities:
- gpu
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 300s
networks:
- tnet
labels:
- homepage.group=AI - Inference
- homepage.name=vLLM Qwen2.5-Coder 1.5B (FIM)
- homepage.icon=mdi-code-braces
- homepage.description=Qwen2.5-Coder-1.5B FIM code-completion (fv-ml1, Zed edit-predictions)
- homepage.href=http://10.251.50.54:${CODER_PORT}/docs
# vllm-lfm25 (LiquidAI/LFM2.5-2.6B, :8021) — RETIRED PERMANENTLY 2026-08-20 by
# operator directive. It was an EVAL-ONLY bake-off seat against granite-4.1-8b
# (brokkr R-target, 2026-08-10) that never received the operator ruling it was
# pending. Its comparator is gone (granite retired from the roster 2026-08-15),
# it was deliberately never wired into any default/fallback routing chain, and
# LiteLLM spend logs showed 0 calls in the 4-day window ending 2026-08-21.
# Freed 8,772 MiB on fv-ml1 GPU1. The `lfm2.5-2.6b` gateway alias was removed
# in the same pass so the name 404s cleanly rather than erroring against a dead
# backend. Weights remain in the shared HF cache; nothing was deleted from disk.
networks:
tnet:
name: traefik-net
external: true