初始化项目，由ModelHub XC社区提供模型

Model: rpant/iolai26-solve
Source: Original Platform

script.py

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+"""Entrypoint: read /tmp/data/test.csv, write submission.csv (id, pred, explanation)."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+# ===== CHANGE HERE — your model (must fit the T4's ~15 GB) =====
+# "." for the real submission (this repo ships Qwen2.5-14B-Instruct-AWQ at the
+# root); a Hub name (e.g. "Qwen/Qwen2.5-14B-Instruct-AWQ") while testing on
+# Colab.
+MODEL_ID = "."
+# None => the pipeline picks per test size (deep mode 2048 for a small test set,
+# coverage mode 1024 for a large one). Set an int to force it.
+MAX_NEW_TOKENS = None
+LLM_BATCH = 6          # puzzles gathered per checkpoint cycle (the client then
+                       # sub-batches by token budget to fit the T4)
+
+# Skip the LLM and emit the symbolic-only baseline. Diagnostic; leave False.
+SYMBOLIC_ONLY = False
+
+# LLM pass uses a minimal prompt: no scaffold injection, no chain-of-thought.
+# Set IOL_LEAN=0 for the scaffolded path.
+LEAN_MODE = os.environ.get("IOL_LEAN", "1") == "1"
+
+# Answer match_letters via the free-form LLM pass. Set IOL_MATCH_ASSIGN=1 to use
+# the assignment solver instead.
+MATCH_ASSIGNMENT = os.environ.get("IOL_MATCH_ASSIGN", "0") == "1"
+
+# Generation batch size. 1 = one prompt at a time, no padding. Larger batches
+# are faster but pad to the longest prompt. Set IOL_GEN_BATCH to change.
+GEN_BATCH_SIZE = int(os.environ.get("IOL_GEN_BATCH", "1"))
+
+# Light greedy-anchored self-consistency: N sampled passes that can only
+# displace the greedy answer on genuine agreement. Budget-gated. 0 disables.
+VOTE_SAMPLES = int(os.environ.get("IOL_VOTE_SAMPLES", "2"))
+VOTE_TEMP = float(os.environ.get("IOL_VOTE_TEMP", "0.5"))
+
+# Optional segmentation hint in the prompt. Off by default. Set IOL_HINT=1.
+HINT = os.environ.get("IOL_HINT", "0") == "1"
+
+# The eval sandbox has no internet; only go offline when loading local
+# weights so Colab testing with a Hub MODEL_ID still downloads normally.
+if MODEL_ID == "." or Path(MODEL_ID).exists():
+    os.environ.setdefault("HF_HUB_OFFLINE", "1")
+    os.environ.setdefault("TRANSFORMERS_OFFLINE", "1")
+# reduce CUDA fragmentation on the T4 (must be set before torch initializes)
+os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
+
+import csv
+import json
+import sys
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from solver.budget import Budget
+from solver.llm import load_client
+from solver.pipeline import run_pipeline
+
+TEST_CSV = "/tmp/data/test.csv"
+OUT_CSV = "submission.csv"
+
+csv.field_size_limit(min(sys.maxsize, 2 ** 31 - 1))
+
+
+def read_rows(path: str):
+    with open(path, newline="", encoding="utf-8") as f:
+        return [{k: (v or "") for k, v in row.items()} for row in csv.DictReader(f)]
+
+
+def write_submission(results, out_path: str) -> None:
+    """Atomic write (tmp + rename) so a crash mid-write never leaves a
+    truncated submission.csv."""
+    tmp = out_path + ".tmp"
+    with open(tmp, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=["id", "pred", "explanation"])
+        writer.writeheader()
+        for r in results:
+            writer.writerow({
+                "id": r.row_id,
+                "pred": json.dumps([str(a).strip() or "?" for a in r.answers],
+                                   ensure_ascii=False),
+                "explanation": r.explanation,
+            })
+    os.replace(tmp, out_path)
+
+
+def main(test_path: str = TEST_CSV, out_path: str = OUT_CSV) -> None:
+    budget = Budget()
+    rows = read_rows(test_path)
+    try:
+        if SYMBOLIC_ONLY:
+            from solver.llm import NullClient
+            client = NullClient()
+            print("SYMBOLIC_ONLY: skipping the LLM; submitting the symbolic "
+                  "baseline", flush=True)
+        else:
+            client = load_client(MODEL_ID)
+            if hasattr(client, "batch_size"):
+                client.batch_size = GEN_BATCH_SIZE
+        # checkpoint after the symbolic pass and every LLM batch: a crash at
+        # any later point still leaves a complete submission on disk
+        results = run_pipeline(rows, client, budget,
+                               llm_batch=LLM_BATCH, max_new_tokens=MAX_NEW_TOKENS,
+                               checkpoint=lambda rs: write_submission(rs, out_path),
+                               lean=LEAN_MODE,
+                               use_match_assignment=MATCH_ASSIGNMENT,
+                               vote_samples=VOTE_SAMPLES, vote_temp=VOTE_TEMP,
+                               hint=HINT)
+        write_submission(results, out_path)
+        print(f"wrote {out_path}: {len(results)} rows in {budget.elapsed():.1f}s",
+              flush=True)
+    except BaseException as e:
+        # last resort: if the pipeline itself died before the first
+        # checkpoint, emit query echoes — an empty pred is a zero row
+        if not Path(out_path).exists():
+            from solver.pipeline import PuzzleResult
+            fallback = [PuzzleResult(str(r.get("id", i)),
+                                     [str(r.get("query", "?")).strip() or "?"],
+                                     "- fallback")
+                        for i, r in enumerate(rows)]
+            write_submission(fallback, out_path)
+            print(f"pipeline failed ({type(e).__name__}); wrote fallback "
+                  f"{out_path}", flush=True)
+        raise
+
+
+if __name__ == "__main__":
+    args = sys.argv[1:]
+    main(args[0] if args else TEST_CSV, args[1] if len(args) > 1 else OUT_CSV)