esh-pfi-infrastructure/stacks/vllm/compose.yaml

# vLLM — Qwen3 Embedding + Reranker + Skywork Reward-V2 classifier.
#
# Originally created to replace the unmaintained Infinity stack (embed +
# rerank); generalized 2026-05-13 to host any vLLM-served model on ana-ml2,
# starting with the Skywork-Reward-V2-Llama-3.1-8B reward classifier
# (AWQ-quantized locally, served from /tank/aimodels/llm/).
#
# vLLM runs one model per process, so this stack brings up three containers
# sharing a single GPU:
#
#   vllm-embed   — Qwen3-Embedding served as an OpenAI /v1/embeddings server
#   vllm-rerank  — Qwen3-Reranker served as a /rerank + /score server
#   vllm-reward  — Skywork-Reward-V2-Llama-3.1-8B-AWQ served as a /classify scorer
#
# The reranker is a causal-LM checkpoint; --hf-overrides re-maps it to
# Qwen3ForSequenceClassification so vLLM's reranking endpoints work and the
# model only emits two class logits (no/yes) instead of the full 151k vocab.
#
# All tunables live in .env — edit that, not this file.
#
# Pre-download models to avoid first-run delay:
#   scripts/elway ana-ml2 --playbook playbooks/pull-hf-repo.yaml \
#       --var hf_repo=Qwen/Qwen3-Embedding-0.6B
#   scripts/elway ana-ml2 --playbook playbooks/pull-hf-repo.yaml \
#       --var hf_repo=Qwen/Qwen3-Reranker-0.6B
#
# Skywork-Reward-V2-Llama-3.1-8B-AWQ is a locally-quantized model — lives at
# /tank/aimodels/llm/Skywork-Reward-V2-Llama-3.1-8B-AWQ on ana-ml2 and is
# bind-mounted into the reward service at /local-models. Not from HF Hub.

services:
  vllm-embed:
    image: vllm/vllm-openai:${VLLM_VERSION}
    container_name: vllm-embed
    restart: unless-stopped
    ipc: host
    ports:
      - "${EMBED_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:
      - ${EMBED_MODEL}
      - --served-model-name
      - ${EMBED_MODEL}
      - --runner
      - pooling
      - --host
      - 0.0.0.0
      - --port
      - "8000"
      - --gpu-memory-utilization
      - ${EMBED_GPU_MEM_UTIL}
      - --max-model-len
      - ${EMBED_MAX_MODEL_LEN}
      - --dtype
      - auto
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids:
                - "${GPU_ID}"
              capabilities:
                - gpu
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 180s
    networks:
      - tnet
    labels:
      - homepage.group=AI - Eval & Retrieval
      - homepage.name=vLLM Embed (Qwen3)
      - homepage.icon=mdi-vector-arrange-below
      - homepage.description=Qwen3 Embedding via vLLM (ana-ml2)
      - homepage.href=http://10.250.50.54:${EMBED_PORT}/docs

  vllm-rerank:
    image: vllm/vllm-openai:${VLLM_VERSION}
    container_name: vllm-rerank
    restart: unless-stopped
    ipc: host
    ports:
      - "${RERANK_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:
      - ${RERANK_MODEL}
      - --served-model-name
      - ${RERANK_MODEL}
      - --runner
      - pooling
      - --hf-overrides
      - '{"architectures":["Qwen3ForSequenceClassification"],"classifier_from_token":["no","yes"],"is_original_qwen3_reranker":true}'
      - --host
      - 0.0.0.0
      - --port
      - "8000"
      - --gpu-memory-utilization
      - ${RERANK_GPU_MEM_UTIL}
      - --max-model-len
      - ${RERANK_MAX_MODEL_LEN}
      - --dtype
      - auto
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids:
                - "${GPU_ID}"
              capabilities:
                - gpu
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 180s
    networks:
      - tnet
    labels:
      - homepage.group=AI - Eval & Retrieval
      - homepage.name=vLLM Rerank (Qwen3)
      - homepage.icon=mdi-sort-variant
      - homepage.description=Qwen3 Reranker via vLLM (ana-ml2)
      - homepage.href=http://10.250.50.54:${RERANK_PORT}/docs

  vllm-reward:
    image: vllm/vllm-openai:${VLLM_VERSION}
    container_name: vllm-reward
    restart: unless-stopped
    ipc: host
    ports:
      - "${REWARD_PORT}:8000"
    volumes:
      # AWQ output lives in the legacy llama-swap models tree, not the HF cache
      # — bind-mount the LLM models dir read-only so the reward service can
      # load it as a local-path HF-format model.
      - /tank/aimodels/llm:/local-models:ro
    environment:
      - VLLM_API_KEY=${API_KEY:-}
    command:
      - /local-models/Skywork-Reward-V2-Llama-3.1-8B-AWQ
      - --served-model-name
      - Skywork/Skywork-Reward-V2-Llama-3.1-8B-AWQ
      # vLLM 0.19.1 deprecated --task in favor of --runner. The model's
      # config.json declares `LlamaForSequenceClassification` so the
      # pooling runner uses it as a classifier (single-label reward score)
      # without needing an explicit task flag.
      - --runner
      - pooling
      - --host
      - 0.0.0.0
      - --port
      - "8000"
      - --gpu-memory-utilization
      - ${REWARD_GPU_MEM_UTIL}
      - --max-model-len
      - ${REWARD_MAX_MODEL_LEN}
      - --dtype
      - auto
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids:
                - "${GPU_ID}"
              capabilities:
                - gpu
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 240s
    networks:
      - tnet
    labels:
      - homepage.group=AI - Eval & Retrieval
      - homepage.name=vLLM Reward (Skywork)
      - homepage.icon=mdi-scale-balance
      - homepage.description=Skywork-Reward-V2 8B classifier via vLLM (ana-ml2)
      - homepage.href=http://10.250.50.54:${REWARD_PORT}/docs

  # Granite 4.1 8B (FP8) — production summarizer (replaced phi4-mini
  # 2026-06-05, which had superseded the llama-swap granite-4-small pin).
  # Generative chat model (OpenAI /v1/chat/completions), so NO --runner
  # pooling. FP8 on RTX PRO 6000 Blackwell (cc 12.0): near-lossless, ~1.2x, ~6 GB.
  vllm-granite:
    image: vllm/vllm-openai:${VLLM_VERSION}
    container_name: vllm-granite
    restart: unless-stopped
    ipc: host
    ports:
      - "${GRANITE_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:
      # Production summarizer (replaced phi4-mini 2026-06-05). Default = official
      # IBM pre-quantized FP8 (compressed-tensors), loaded directly; FP8 is native
      # on the RTX PRO 6000 Blackwell (cc 12.0). Fallback to vLLM-native dynamic FP8 from
      # BF16: GRANITE_MODEL=ibm-granite/granite-4.1-8b + GRANITE_QUANT=fp8.
      - ${GRANITE_MODEL}
      - --served-model-name
      - ${GRANITE_SERVED_NAME}
      - --quantization
      - ${GRANITE_QUANT}
      - --host
      - 0.0.0.0
      - --port
      - "8000"
      - --gpu-memory-utilization
      - ${GRANITE_GPU_MEM_UTIL}
      - --max-model-len
      - ${GRANITE_MAX_MODEL_LEN}
      # Very high so the KV pool (not the seq cap) is the only concurrency bound —
      # granite is the fleet fan-out summarizer/classifier (many concurrent SHORT
      # calls). vLLM's default resolves to 128, capping below the KV bound
      # (~192 @ 1K-tok); 1024 unblocks it (VRAM-neutral — KV pool is util-bound).
      - --max-num-seqs
      - ${GRANITE_MAX_NUM_SEQS}
      - --dtype
      - auto
      # CUDA graphs ENABLED (no --enforce-eager) for decode throughput. Made
      # room 2026-06-05 by right-sizing the embed/rerank/reward trio's KV pools
      # (they were over-provisioned at 5.9x/2.0x/3.9x concurrency); GPU 1 now has
      # ~17 GB free after granite, so graph-capture buffers fit. If the trio
      # ever grows back, granite may need --enforce-eager again on this card.
      # FP8 KV cache — halves KV memory; near-lossless on Blackwell (cc 12.0).
      - --kv-cache-dtype
      - ${GRANITE_KV_CACHE_DTYPE}
      # Prefix caching pinned EXPLICIT (vLLM v1 defaults it on, but pin so a
      # version flip can't silently disable it). Benched 2026-06-13: ~6.5x faster
      # TTFT (45ms vs 292ms) on a shared ~4.5k-token summarizer template; soft/
      # evictable KV, neutral when prefixes don't repeat — pure win for granite.
      - --enable-prefix-caching
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids:
                - "${GRANITE_GPU_ID}"
              capabilities:
                - gpu
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 180s
    networks:
      - tnet
    labels:
      - homepage.group=AI - Inference
      - homepage.name=vLLM Granite 4.1 8B (summarizer)
      - homepage.icon=mdi-text-box-outline
      - homepage.description=Granite 4.1 8B FP8 via vLLM (ana-ml2)
      - homepage.href=http://10.250.50.54:${GRANITE_PORT}/docs

  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 (ana-ml2, Zed edit-predictions)
      - homepage.href=http://10.250.50.54:${CODER_PORT}/docs

  vllm-lfm25:
    image: vllm/vllm-openai:${VLLM_VERSION}
    container_name: vllm-lfm25
    restart: unless-stopped
    ipc: host
    ports:
      - "${LFM25_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:
      # LiquidAI/LFM2.5-2.6B (BF16, Lfm2ForCausalLM). NON-PRODUCTION bake-off alias
      # vs granite-4.1-8b on structured extraction / classification / tool-call
      # formatting (brokkr R-target, 2026-08-10). LFM Open License v1.0 (<USD 10M-rev
      # commercial, not OSI) — EVAL-ONLY pending an operator ruling on production; NOT
      # wired into any default/fallback routing chain. Reasoning model: the chat
      # template opens <think>; deliberately NO --reasoning-parser, so the full
      # generation (thinking + answer) lands in `content` non-empty — brokkr's explicit
      # requirement (an empty content with the answer stranded in reasoning_content
      # reads as a degenerate model). Vendor sampling (temp 0.1 / top_k 50 / rep_pen 1.1)
      # is the LiteLLM alias default, not a launch arg.
      - ${LFM25_MODEL}
      - --served-model-name
      - ${LFM25_SERVED_NAME}
      - --host
      - 0.0.0.0
      - --port
      - "8000"
      - --gpu-memory-utilization
      - ${LFM25_GPU_MEM_UTIL}
      - --max-model-len
      - ${LFM25_MAX_MODEL_LEN}
      - --max-num-seqs
      - ${LFM25_MAX_NUM_SEQS}
      - --dtype
      - auto
      - --kv-cache-dtype
      - ${LFM25_KV_CACHE_DTYPE}
      - --enable-prefix-caching
      # Split the <think> reasoning (delimited by </think>; the opening tag is
      # injected by the chat template into the prompt) into reasoning_content, so
      # `content` is the clean post-</think> answer — scoreable JSON for brokkr's
      # structured-extraction bake-off (raw-served, reasoning prepended, is not).
      - --reasoning-parser
      - deepseek_r1
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids:
                - "${LFM25_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 LFM2.5-2.6B (bake-off)
      - homepage.icon=mdi-flask-outline
      - homepage.description=LiquidAI LFM2.5-2.6B non-prod bake-off vs granite (ana-ml2)
      - homepage.href=http://10.250.50.54:${LFM25_PORT}/docs

networks:
  tnet:
    name: traefik-net
    external: true