diff --git a/scripts/erp-tune-serve/merge_lora.py b/scripts/erp-tune-serve/merge_lora.py
index 7c77a99..ea68c6b 100755
--- a/scripts/erp-tune-serve/merge_lora.py
+++ b/scripts/erp-tune-serve/merge_lora.py
@@ -97,6 +97,50 @@ def main() -> int:
     n_lines = len(src.read_text().splitlines())
     print(f"[merge] chat_template.jinja <- {src} ({n_lines} lines)", flush=True)
 
+    # ⚠ CARRY THE PROCESSOR FILES. This is a multimodal checkpoint, so vLLM
+    # builds a feature extractor at startup and dies without them:
+    #     OSError: Can't load feature extractor for '<model>'
+    # `save_pretrained` on the merged model writes tokenizer files only, so
+    # anything else the base ships as auxiliary config must be copied across.
+    # Verified against the served nvfp4a16 artifact, which carries exactly this.
+    for aux in ("processor_config.json", "preprocessor_config.json",
+                "video_preprocessor_config.json", "special_tokens_map.json"):
+        src_aux = Path(a.base) / aux
+        if src_aux.exists() and not (out / aux).exists():
+            shutil.copy2(src_aux, out / aux)
+            print(f"[merge] carried {aux}", flush=True)
+
+    # ⚠⚠ CONFIG SCHEMA DOWNGRADE. transformers 5.15 MIGRATES Gemma-4's
+    # heterogeneous-attention config on save: it drops `global_head_dim` /
+    # `num_global_key_value_heads` and writes a `per_layer_config` dict instead.
+    # Older transformers (5.10, which is what the llmcompressor venv pins) does
+    # not understand the new key and resolves `config.num_key_value_heads` to
+    # None, dying with:
+    #     TypeError: unsupported operand type(s) for //: 'int' and 'NoneType'
+    # Every WORKING artifact on this box - the bf16 base, the served nvfp4 prod
+    # seat, and the nvfp4a16 build - uses the OLD schema. Merging a LoRA changes
+    # weights, not architecture, so the base's expression of the architecture is
+    # the correct one to ship.
+    cfg_path = out / "config.json"
+    cfg = json.loads(cfg_path.read_text())
+    base_cfg = json.loads((Path(a.base) / "config.json").read_text())
+    ct, bt = cfg.get("text_config", cfg), base_cfg.get("text_config", base_cfg)
+    if "per_layer_config" in ct:
+        ct.pop("per_layer_config")
+        for k in ("global_head_dim", "num_global_key_value_heads"):
+            if k in bt:
+                ct[k] = bt[k]
+        cfg_path.write_text(json.dumps(cfg, indent=2) + "\n")
+        print("[merge] config schema downgraded to match the base "
+              "(per_layer_config -> global_head_dim/num_global_key_value_heads)",
+              flush=True)
+    ct2 = json.loads(cfg_path.read_text()).get("text_config", {})
+    for k in ("global_head_dim", "num_global_key_value_heads"):
+        if bt.get(k) is not None and ct2.get(k) != bt.get(k):
+            print(f"REFUSING: {k} is {ct2.get(k)}, base says {bt.get(k)}",
+                  file=sys.stderr)
+            return 6
+
     tj = out / "tokenizer.json"
     if tj.exists() and json.loads(tj.read_text()).get("truncation"):
         print("REFUSING: shipped tokenizer carries a truncation cap", file=sys.stderr)
diff --git a/scripts/erp-tune-serve/quant_nvfp4a16.py b/scripts/erp-tune-serve/quant_nvfp4a16.py
index ff56014..64bb361 100755
--- a/scripts/erp-tune-serve/quant_nvfp4a16.py
+++ b/scripts/erp-tune-serve/quant_nvfp4a16.py
@@ -146,8 +146,14 @@ def main() -> int:
     )
 
     print("[oneshot] starting", flush=True)
+    # ⚠ `processor` must be passed EXPLICITLY. This is a multimodal
+    # (Gemma4ForConditionalGeneration) checkpoint, and llmcompressor's
+    # auto-init fails on it with "An error occurred when attempting to
+    # initialize model processor, which is required when a dataset is
+    # provided." Calibration here is text-only - the records come from the
+    # training encode cache - so the tokenizer is the correct processor.
     oneshot(
-        model=model, dataset=ds, recipe=recipe,
+        model=model, dataset=ds, recipe=recipe, processor=tok,
         max_seq_length=a.seqlen, num_calibration_samples=len(ds),
         output_dir=str(out),
     )
@@ -155,10 +161,14 @@ def main() -> int:
 
     # ⚠ playbook 3.14 - NEVER ship the calibration tokenizer. Re-read pristine.
     AutoTokenizer.from_pretrained(a.model, trust_remote_code=True).save_pretrained(out)
-    src_tpl = Path(a.model) / "chat_template.jinja"
-    if src_tpl.exists():
-        (out / "chat_template.jinja").write_text(src_tpl.read_text())
-        print("[post] chat_template.jinja carried over", flush=True)
+    import shutil as _sh
+    for aux in ("chat_template.jinja", "processor_config.json",
+                "preprocessor_config.json", "video_preprocessor_config.json",
+                "special_tokens_map.json", "generation_config.json"):
+        src_aux = Path(a.model) / aux
+        if src_aux.exists():
+            _sh.copy2(src_aux, out / aux)
+            print(f"[post] carried {aux}", flush=True)
 
     tj = out / "tokenizer.json"
     if tj.exists() and json.loads(tj.read_text()).get("truncation"):
diff --git a/scripts/erp-tune-serve/quant_with_gen_down.sh b/scripts/erp-tune-serve/quant_with_gen_down.sh
new file mode 100644
index 0000000..4707cfa
--- /dev/null
+++ b/scripts/erp-tune-serve/quant_with_gen_down.sh
@@ -0,0 +1,53 @@
+#!/usr/bin/env bash
+# Run the NVFP4A16 quant with `vllm-gen` temporarily stopped to free GPU1,
+# and ALWAYS bring gen back - crash, OOM, kill, or success.
+#
+# Operator authorised downing GPU1 residents overnight (2026-08-25) on the
+# condition they are restored. The restore therefore must NOT depend on the
+# calling session surviving, so it lives in a trap rather than in the caller.
+#
+# `docker start` (not `compose up`) is deliberate: it restarts the EXISTING
+# container with its exact original config, so there is no chance of compose
+# recreating the seat with drifted settings or a different image tag.
+#
+# gen serves `qwen3.8-27b-uncensored` + `-thinking`, and is the backing seat
+# for the fleet-wide `summarizer` / `classifier` aliases. Keep the window short.
+set -uo pipefail
+
+GEN=vllm-gen
+LOG=/tank/erp-tune/serve/quant.log
+OUT=/tank/erp-tune/serve/nvfp4a16-test
+
+restore() {
+  echo "[restore] starting $GEN ..." | tee -a "$LOG"
+  docker start "$GEN" >/dev/null 2>&1
+  for i in $(seq 1 60); do
+    st=$(docker inspect -f '{{.State.Health.Status}}' "$GEN" 2>/dev/null || echo unknown)
+    run=$(docker inspect -f '{{.State.Running}}' "$GEN" 2>/dev/null || echo false)
+    if [ "$st" = "healthy" ]; then echo "[restore] $GEN healthy" | tee -a "$LOG"; return 0; fi
+    if [ "$run" != "true" ] && [ "$i" -gt 3 ]; then
+      echo "[restore] ⚠ $GEN NOT RUNNING - MANUAL ACTION NEEDED" | tee -a "$LOG"; return 1
+    fi
+    sleep 10
+  done
+  echo "[restore] ⚠ $GEN started but not healthy after 600s - CHECK IT" | tee -a "$LOG"
+  return 1
+}
+trap restore EXIT INT TERM
+
+echo "[gen] stopping $GEN to free GPU1" | tee -a "$LOG"
+docker stop "$GEN" >/dev/null 2>&1
+sleep 8
+nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader | tee -a "$LOG"
+
+rm -rf "$OUT"
+cd /tank/erp-tune/serve
+CUDA_VISIBLE_DEVICES=1 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
+  /tank/aimodels/quant-work/.venv/bin/python -u quant_nvfp4a16.py \
+  --model /tank/erp-tune/serve/merged-test \
+  --out "$OUT" \
+  --calib-cache /tank/erp-tune/run-01/encode-cache/encoded-a4b0796de1260930.jsonl \
+  --num-calib "${NUM_CALIB:-16}" --seqlen "${SEQLEN:-4096}" >> "$LOG" 2>&1
+rc=$?
+echo "[quant] exit rc=$rc" | tee -a "$LOG"
+exit $rc