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diff --git a/scripts/smoke_llms.py b/scripts/smoke_llms.py
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+++ b/scripts/smoke_llms.py
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+from __future__ import annotations
+
+from pathlib import Path
+import sys
+import json
+import os
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "src"))
+
+from personalization.config.settings import load_local_models_config
+from personalization.models.llm.qwen_instruct import QwenInstruct
+from personalization.models.preference_extractor.rule_extractor import QwenRuleExtractor
+
+
+def ensure_models_present() -> bool:
+    cfg = load_local_models_config()
+    ok = True
+    for spec in (cfg.llm, cfg.preference_extractor):
+        path = ROOT / spec.local_path
+        if not path.exists() or not any(path.iterdir()):
+            print(f"[missing] {path} is empty. Run: python scripts/pull_models.py --target all")
+            ok = False
+    return ok
+
+
+def main() -> None:
+    if not ensure_models_present():
+        return
+
+    cfg = load_local_models_config()
+    os.environ["PREF_DEBUG"] = "1"
+    llm = QwenInstruct.from_config(cfg)
+    extractor = QwenRuleExtractor.from_config(cfg)
+
+    print("[llm] generating...")
+    out = llm.generate("Say hello in one short sentence.", max_new_tokens=32, temperature=0.2)
+    print(out)
+
+    print("[extractor] extracting...")
+    scenarios = [
+        (
+            "math_latex",
+            "Consider the sequence defined by a_1 = 1 and a_{n+1} = a_n + 1/n for n >= 1. "
+            "(1) Prove that a_n diverges. (2) Derive an asymptotic expression for a_n in terms of the harmonic numbers H_n. "
+            "(3) Compute the limit of (a_n - ln n) as n -> infinity. Please use LaTeX for the output.",
+        ),
+        (
+            "code_python311",
+            "I have a performance bottleneck in my Python code that processes large CSV files. It reads rows, aggregates stats, and writes summaries. "
+            "Explain how to optimize I/O and memory, discuss multiprocessing vs async. When you show code, please use Python 3.11 syntax and include type hints in the snippets.",
+        ),
+        (
+            "data_json_only",
+            "Given a dataset of user events with timestamps, device types, and regions, outline steps to compute DAU, WAU, and retention. "
+            "List pitfalls and how to handle missing data. Return your final answer as JSON only.",
+        ),
+        (
+            "writing_concise_no_emoji",
+            "Explain the difference between supervised and reinforcement learning with practical examples and cautions. "
+            "Keep answers concise and avoid emojis.",
+        ),
+    ]
+    for name, query in scenarios:
+        print(f"\n[scenario] {name}")
+        prefs = extractor.extract_preferences(query)
+        print(json.dumps(prefs, indent=2, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()
+
+