#!/bin/bash # Launch vLLM server for google/gemma-4-31B-it. # # Hardware: 2× L40S 48GB (TP=2). Note: Gemma shares the same GPU pair (0-1 by # default here) as Magistral in the paper's triadic-sweep node layout. They # cannot run simultaneously; stop one before starting the other. # Default port: 8009. Override with PORT env var. # # Key settings: # --tool-call-parser gemma4 — requires the custom jinja chat template # --chat-template — path to tool_chat_template_gemma4.jinja # tool_choice=auto — Gemma 4 returns empty tool_calls with 'required' # --max-num-seqs 64 — tuned to avoid KV-cache saturation under # concurrent multi-agent load # --no-scheduler-reserve-full-isl — avoids a vLLM 0.19.0 scheduler deadlock # # Note on max_tokens: Gemma 4 output is capped at 512 tokens in the runner # (compared to 1536 for other models) to limit KV-cache pressure. set -euo pipefail HF_HUB_CACHE="${HF_HUB_CACHE:-/data/hf_cache/hub}" export HF_HUB_CACHE VENV="${SAFEMASS_VENV:-$(dirname "$0")/../.venv}" PORT="${PORT:-8009}" LOGS="${LOGS_DIR:-/tmp/vllm_logs}" SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" TEMPLATE="${GEMMA_CHAT_TEMPLATE:-${SCRIPT_DIR}/../oasis/tool_chat_template_gemma4.jinja}" mkdir -p "$LOGS" if [[ ! -f "$TEMPLATE" ]]; then echo "ERROR: Gemma chat template not found at: $TEMPLATE" >&2 echo "Set GEMMA_CHAT_TEMPLATE env var or ensure oasis/tool_chat_template_gemma4.jinja exists." >&2 exit 1 fi CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1}" \ "${VENV}/bin/python3" -m vllm.entrypoints.openai.api_server \ --model google/gemma-4-31B-it \ --served-model-name google/gemma-4-31B-it \ --tensor-parallel-size 2 \ --max-model-len 16384 \ --gpu-memory-utilization 0.90 \ --port "$PORT" \ --host 127.0.0.1 \ --trust-remote-code \ --enable-auto-tool-choice \ --tool-call-parser gemma4 \ --chat-template "$TEMPLATE" \ --max-num-seqs 64 \ --no-scheduler-reserve-full-isl \ >> "${LOGS}/vllm_gemma_${PORT}.log" 2>&1