初始化项目，由ModelHub XC社区提供模型

Model: rockerritesh/r1-distill-qwen7b-offline
Source: Original Platform

script.py

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+"""IOL-AI 2026 submission entrypoint.
+
+Runs on the platform's T4 (16 GB), offline, within a 30-minute limit. Reads the
+hidden test set from /tmp/data/test.csv and writes submission.csv (id,pred) to
+the working directory. Model weights ship inside this repo and load from ".".
+
+Baseline model: Qwen/Qwen2.5-1.5B-Instruct. The score lever here is the harness
+(task-aware prompts + robust parsing) in iol_harness.py, not the model size.
+"""
+
+import os
+import time
+
+# Must be set BEFORE importing transformers: no network at run time.
+os.environ["HF_HUB_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+
+import json
+
+import pandas as pd
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+import iol_harness as H
+
+MODEL_ID = "."  # weights are shipped in this repo (offline)
+TEST_CSV = "/tmp/data/test.csv"
+OUT_CSV = "submission.csv"
+
+TIME_LIMIT_S = 30 * 60
+SAFETY_S = 26 * 60  # stop generating past this; still write what we have
+_START = time.monotonic()
+
+# Optional per-repo harness config (reasoning-model repos ship one to turn on CoT).
+CFG = {}
+if os.path.exists("harness_cfg.json"):
+    try:
+        CFG = json.load(open("harness_cfg.json"))
+    except Exception:
+        CFG = {}
+COT = bool(CFG.get("cot", False))
+MAX_CAP = int(CFG.get("max_new_cap", 1024))
+
+
+def elapsed():
+    return time.monotonic() - _START
+
+
+def load_model():
+    tok = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.float16,  # T4 has no bfloat16
+        device_map="auto",
+        trust_remote_code=True,
+    ).eval()
+    if tok.pad_token_id is None:
+        tok.pad_token = tok.eos_token
+    return tok, model
+
+
+def generate(tok, model, messages, max_new_tokens):
+    inputs = tok.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+    with torch.no_grad():
+        out = model.generate(
+            inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=False,  # greedy => reproducible
+            num_beams=1,
+            pad_token_id=tok.pad_token_id,
+        )
+    return tok.decode(out[0][inputs.shape[-1] :], skip_special_tokens=True).strip()
+
+
+def main():
+    df = pd.read_csv(TEST_CSV, dtype=str).fillna("")
+    total = len(df)
+    print(f"loaded {total} problems from {TEST_CSV}", flush=True)
+
+    tok, model = load_model()
+    print(f"model loaded at {elapsed():.0f}s", flush=True)
+
+    records = []
+    for i, row in df.iterrows():
+        rid = row["id"]
+        context = row.get("context", "")
+        query = row.get("query", "")
+        task_type = row.get("task_type", "")
+        n = H.count_items(context, query, task_type)
+
+        preds = [""] * n  # safe default so every item has a slot
+        if elapsed() < SAFETY_S:
+            try:
+                messages = H.build_messages(context, query, task_type, n, cot=COT)
+                budget = H.max_new_tokens_for(task_type, n, cot=COT, cap=MAX_CAP)
+                text = generate(tok, model, messages, budget)
+                preds = H.parse_answers(text, n)
+            except Exception as exc:  # never let one row sink the whole run
+                print(f"[warn] id={rid} failed: {exc!r}", flush=True)
+        else:
+            print(f"[warn] time budget hit; blanking id={rid}", flush=True)
+
+        records.append({"id": rid, "pred": json.dumps(preds, ensure_ascii=False)})
+        if (i + 1) % 5 == 0 or (i + 1) == total:
+            print(f"{i + 1}/{total} done  ({elapsed():.0f}s)", flush=True)
+
+    pd.DataFrame(records, columns=["id", "pred"]).to_csv(OUT_CSV, index=False)
+    print(f"wrote {OUT_CSV} ({len(records)} rows) at {elapsed():.0f}s", flush=True)
+
+
+if __name__ == "__main__":
+    main()