Upload kaggle/run_exp1b_kv200.py with huggingface_hub

kaggle/run_exp1b_kv200.py

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+#!/usr/bin/env python3
+"""
+================================================================================
+LITM v4 — Experiment 1B: KV Retrieval (200 keys)
+Standalone Kaggle-ready script.
+================================================================================
+"""
+import argparse
+import logging
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from experiments.kv_retrieval import run_kv_retrieval
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    level=logging.INFO,
+    stream=sys.stdout,
+)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    p = argparse.ArgumentParser(description="LITM v4 - Exp 1B: KV Retrieval (200 keys)")
+    p.add_argument("--model", default="Qwen/Qwen2.5-1.5B-Instruct")
+    p.add_argument("--n-examples", type=int, default=50)
+    p.add_argument("--n-keys", type=int, default=200)
+    p.add_argument("--output", default="/kaggle/working/litm_results")
+    return p.parse_args()
+
+
+def main():
+    args = parse_args()
+    out_dir = os.path.join(args.output, "exp1b_kv200")
+    os.makedirs(out_dir, exist_ok=True)
+
+    logger.info("=" * 60)
+    logger.info("LITM v4 - Experiment 1B: KV Retrieval (200 keys)")
+    logger.info(f"Model: {args.model}")
+    logger.info(f"Examples per position: {args.n_examples}")
+    logger.info(f"Keys: {args.n_keys}")
+    logger.info(f"Output: {out_dir}")
+    logger.info("=" * 60)
+
+    result = run_kv_retrieval(
+        model_name=args.model,
+        num_keys=args.n_keys,
+        num_examples=args.n_examples,
+        out_dir=out_dir,
+        prefix="kv200",
+    )
+
+    logger.info("\n" + "=" * 60)
+    logger.info("EXPERIMENT 1B COMPLETE")
+    logger.info(f"PBI: {result.get('pbi', 'N/A')}")
+    logger.info(f"Results saved to: {out_dir}")
+    logger.info("=" * 60)
+
+
+if __name__ == "__main__":
+    main()