初始化项目，由ModelHub XC社区提供模型

Model: rockerritesh/qwen25-14b-awq-offline
Source: Original Platform

script.py

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+"""IOL-AI 2026 submission entrypoint — PLAIN baseline harness.
+
+Faithful replica of the official How_to_submit.md baseline (the config the
+organizers posted as "Baseline-Qwen2.5-14B-AWQ", public score 0.1227): a minimal
+system prompt, the raw context+query, greedy decoding, 512 new tokens, and a
+naive newline split of the output with NO forced item count. The only additions
+are timeout-safe incremental writes (an all-blank submission.csv up front + a
+periodic flush), which never change the output of a run that finishes in time —
+they only guarantee a valid partial file if we were ever killed.
+
+Runs on the platform's T4 (16 GB), offline, within the 30-minute limit. Reads
+/tmp/data/test.csv, writes submission.csv (id,pred) to the working directory.
+Model weights (Qwen2.5-14B-Instruct-AWQ) ship in this repo and load from ".".
+"""
+
+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
+
+MODEL_ID = "."  # weights are shipped in this repo (offline)
+TEST_CSV = "/tmp/data/test.csv"
+OUT_CSV = "submission.csv"
+
+MAX_NEW_TOKENS = 512          # same as the official baseline
+SAFETY_S = 25 * 60            # stop generating past this; buffer before the 30-min cap
+WRITE_EVERY = 3               # flush submission.csv every N problems (timeout safety)
+_START = time.monotonic()
+
+# The official baseline's exact system prompt.
+SYSTEM = (
+    "You solve International Linguistics Olympiad problems. Answer every numbered "
+    "item. Put each answer on its own line, in order, with no numbering and no "
+    "extra text."
+)
+
+
+def elapsed():
+    return time.monotonic() - _START
+
+
+def load_model():
+    tok = AutoTokenizer.from_pretrained(MODEL_ID)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.float16,  # T4 has no bfloat16
+        device_map="auto",
+    ).eval()
+    if tok.pad_token_id is None:
+        tok.pad_token = tok.eos_token
+    return tok, model
+
+
+def main():
+    df = pd.read_csv(TEST_CSV, dtype=str).fillna("")
+    total = len(df)
+    print(f"loaded {total} problems from {TEST_CSV}", flush=True)
+
+    # Write a COMPLETE all-blank submission.csv up front so a valid file with
+    # every id always exists, even if we are killed during load or generation.
+    records = [
+        {"id": df.iloc[i]["id"], "pred": json.dumps([], ensure_ascii=False)}
+        for i in range(total)
+    ]
+
+    def flush():
+        pd.DataFrame(records, columns=["id", "pred"]).to_csv(OUT_CSV, index=False)
+
+    flush()
+    print(f"wrote blank {OUT_CSV} ({total} rows) at {elapsed():.0f}s", flush=True)
+
+    tok, model = load_model()
+    print(f"model loaded at {elapsed():.0f}s", flush=True)
+
+    for i in range(total):
+        if elapsed() > SAFETY_S:
+            print(f"[warn] time budget hit at problem {i}; leaving the rest blank", flush=True)
+            break
+        r = df.iloc[i]
+        messages = [
+            {"role": "system", "content": SYSTEM},
+            {"role": "user", "content": f"{r['context'].strip()}\n\n{r['query'].strip()}"},
+        ]
+        try:
+            ids = tok.apply_chat_template(
+                messages, add_generation_prompt=True, return_tensors="pt",
+            ).to(model.device)
+            with torch.no_grad():
+                out = model.generate(
+                    ids, max_new_tokens=MAX_NEW_TOKENS,
+                    do_sample=False, pad_token_id=tok.pad_token_id,
+                )
+            text = tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True).strip()
+            answers = [ln.strip() for ln in text.splitlines() if ln.strip()]
+        except Exception as exc:
+            print(f"[warn] id={r['id']} failed: {exc!r}", flush=True)
+            answers = []
+        records[i]["pred"] = json.dumps(answers, ensure_ascii=False)
+        if (i + 1) % WRITE_EVERY == 0:
+            flush()
+        print(f"{i + 1}/{total} done at {elapsed():.0f}s", flush=True)
+
+    flush()
+    print(f"final {OUT_CSV} ({total} rows) at {elapsed():.0f}s", flush=True)
+
+
+if __name__ == "__main__":
+    main()