#!/usr/bin/env bash
# NVFP4A16 quant of the ERP run-6 merged model on ana-ml2 GPU1 (co-resident with the GPU1 seats;
# CPU-resident load, per-layer onload). Detached container; watch with `docker logs -f erp-v6-quant`.
# NOTE: no PYTORCH_CUDA_ALLOC_CONF=expandable_segments (playbook §3.10).
set -euo pipefail
WORK=/tank/aimodels/erp-tune-v6-quant-work
SRC="${1:-/tank/aimodels/erp-tune-v6-bf16}"
OUT="${2:-/tank/aimodels/erp-tune-v6-nvfp4a16}"
MODE="${3:-full}"          # full | dry-run
EXTRA=""; [ "$MODE" = "dry-run" ] && EXTRA="--dry-run"
NAME=erp-v6-quant; [ "$MODE" = "dry-run" ] && NAME=erp-v6-quant-dry
docker rm -f "$NAME" 2>/dev/null || true
docker run -d --name "$NAME" --gpus '"device=1"' --ipc host \
  -v /tank/aimodels:/tank/aimodels \
  --entrypoint python3 vllm-llmcompressor:latest \
  "$WORK/quant_nvfp4a16_gemma4_moe.py" --model "$SRC" --out "$OUT" \
  --num-samples "${NUM_SAMPLES:-256}" --seqlen "${SEQLEN:-8192}" $EXTRA
echo "launched $NAME: $(docker ps --filter name=$NAME --format '{{.Status}}')"