初始化项目，由ModelHub XC社区提供模型

Model: HamnaKaleem/IOL-AI-2026 Source: Original Platform
2026-07-27 04:17:12 +08:00
commit 400283a697
12 changed files with 456307 additions and 0 deletions
--- a/script.py
+++ b/script.py
@@ -0,0 +1,79 @@
+import os
+# The repo is the working directory at run time, and there is no network.
+os.environ["HF_HUB_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+MODEL_ID = "."
+
+import re
+import json
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tok = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, torch_dtype=torch.float16, device_map="auto"
+).eval()
+
+MAX_NEW_TOKENS = 1536
+
+SYSTEM = (
+    "You solve International Linguistics Olympiad problems by reasoning from the "
+    "data you are given. You may meet a task type you have never seen: read the "
+    "instruction and the examples, and answer in the same form they use. "
+    "Common task types and what to give -- "
+    "translation: the translated form only, in the language the task asks for. "
+    "Apply every suffix, prefix, or ending shown in the examples (plurals, cases, "
+    "tense, etc.) -- do not give the bare stem if the pattern requires an ending; "
+    "fill_blanks: only the missing form for each blank; "
+    "match_letters: the option letter ALONE -- for example 'C', never 'word: C' or "
+    "any other text around it; "
+    "text_to_num: the number in digits; "
+    "num_to_text: the number written out in words, in the language asked; "
+    "any other type: give exactly what the instruction asks, nothing else. "
+    "Reason step by step first. Before you finalize, re-check each answer is in the "
+    "minimal bare form the task requires, with no echoed input, no labels, no extra "
+    "words attached. Then write a line that says exactly FINAL ANSWERS: "
+    "and, below it, one answer per line in the order the items are asked -- the "
+    "bare answer only, no numbering, no quotes, no extra text."
+)
+
+
+def parse_answers(text, task_type=None):
+    """Keep only the lines after the last 'FINAL ANSWERS:' marker, one answer per line."""
+    marker = list(re.finditer(r"(?im)^\s*final answers?\s*:?\s*$", text))
+    if marker:
+        text = text[marker[-1].end():]
+    answers = []
+    for line in text.splitlines():
+        line = re.sub(r"^\s*\d+[.)]\s*", "", line).strip()
+        if line:
+            if task_type == "match_letters":
+                m = re.search(r"([A-Za-z])\s*$", line)
+                if m and (":" in line or len(line) > 3):
+                    line = m.group(1)
+            answers.append(line)
+    return answers
+
+
+df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
+
+rows = []
+for _, r in df.iterrows():
+    messages = [
+        {"role": "system", "content": SYSTEM},
+        {"role": "user", "content": f"{r['context'].strip()}\n\n{r['query'].strip()}"},
+    ]
+    enc = tok.apply_chat_template(
+        messages, add_generation_prompt=True, return_tensors="pt", return_dict=True,
+    ).to(model.device)
+    with torch.no_grad():
+        out = model.generate(**enc, max_new_tokens=MAX_NEW_TOKENS, do_sample=False)
+    text = tok.decode(out[0][enc["input_ids"].shape[-1]:], skip_special_tokens=True).strip()
+    answers = parse_answers(text, task_type=r["task_type"])
+    rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
+    print(f"{len(rows)}/{len(df)} done", flush=True)
+
+os.makedirs("/tmp/model", exist_ok=True)
+pd.DataFrame(rows).to_csv("/tmp/model/submission.csv", index=False)
+print("wrote submission.csv", flush=True)