初始化项目，由ModelHub XC社区提供模型

Model: divaspoudel/iol-7162026
Source: Original Platform

script.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+import os
+import sys
+import json
+import re
+import csv
+import subprocess
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ------------------------------------------------------------------
+# 1. Install auto-gptq (PyPI is reachable at runtime)
+# ------------------------------------------------------------------
+try:
+    import auto_gptq
+except ImportError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "auto-gptq"])
+    import auto_gptq
+
+# ------------------------------------------------------------------
+# 2. Offline mode - no internet allowed during evaluation
+# ------------------------------------------------------------------
+os.environ["HF_HUB_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+
+# ------------------------------------------------------------------
+# 3. Load model from local directory (the repo root)
+# ------------------------------------------------------------------
+MODEL_DIR = "."
+print("Loading tokenizer and model...", flush=True)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_DIR,
+    device_map="auto",
+    trust_remote_code=True,
+    torch_dtype=torch.float16,
+)
+model.eval()
+print("Model loaded.", flush=True)
+
+# ------------------------------------------------------------------
+# 4. Helper: count numbered items in query
+# ------------------------------------------------------------------
+def count_items(query: str) -> int:
+    numbers = re.findall(r'(?:^|\n)\s*(?:\d+)\.', query)
+    if not numbers:
+        lines = query.strip().splitlines()
+        count = 0
+        for ln in lines:
+            if re.match(r'^\s*\d+[\.\)]', ln):
+                count += 1
+        return count
+    return len(numbers)
+
+# ------------------------------------------------------------------
+# 5. Parse model output to a JSON list
+# ------------------------------------------------------------------
+def extract_answers(text: str, expected_count: int) -> list:
+    json_match = re.search(r'\[\s*".*?"\s*(?:,\s*".*?"\s*)*\]', text, re.DOTALL)
+    if json_match:
+        try:
+            ans = json.loads(json_match.group())
+            if isinstance(ans, list) and all(isinstance(x, str) for x in ans):
+                return ans
+        except:
+            pass
+
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    clean = []
+    for ln in lines:
+        m = re.match(r'^\s*\d+[\.\)]\s*(.*)', ln)
+        if m:
+            clean.append(m.group(1).strip())
+        else:
+            clean.append(ln)
+    if len(clean) > expected_count:
+        clean = clean[:expected_count]
+    elif len(clean) < expected_count:
+        clean += [""] * (expected_count - len(clean))
+    return clean
+
+# ------------------------------------------------------------------
+# 6. Optional explanation generation (set to True to opt in)
+# ------------------------------------------------------------------
+EXPLAIN = False   # change to True if you want to include explanations
+
+def generate_explanation(context: str, query: str, answer_list: list) -> str:
+    prompt = (
+        f"{context}\n\n{query}\n\n"
+        "My answers are: " + ", ".join(answer_list) + "\n\n"
+        "Now, provide a concise explanation (max 3 bullet points) of the reasoning behind these answers. "
+        "Do not repeat the answers, only the reasoning."
+    )
+    messages = [
+        {"role": "system", "content": "You are an expert linguist. Provide a short, human-readable explanation of your reasoning for the given linguistic problem."},
+        {"role": "user", "content": prompt},
+    ]
+    ids = tokenizer.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=300, do_sample=False)
+    expl = tokenizer.decode(out[0][ids.shape[-1]:], skip_special_tokens=True).strip()
+    return expl
+
+# ------------------------------------------------------------------
+# 7. Main loop over test.csv
+# ------------------------------------------------------------------
+TEST_CSV = "/tmp/data/test.csv"
+df = pd.read_csv(TEST_CSV, dtype=str).fillna("")
+
+rows = []
+for idx, r in df.iterrows():
+    context = r["context"].strip()
+    query = r["query"].strip()
+    num_items = count_items(query)
+
+    system_msg = (
+        "You solve International Linguistics Olympiad problems. "
+        "Answer every numbered item in the query. "
+        "Output the answers as a JSON list of strings, e.g., [\"answer1\", "answer2\"]. "
+        "Do not include anything else."
+    )
+    user_msg = f"{context}\n\n{query}"
+
+    messages = [
+        {"role": "system", "content": system_msg},
+        {"role": "user", "content": user_msg},
+    ]
+    ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(ids, max_new_tokens=512, do_sample=False)
+    generated = tokenizer.decode(outputs[0][ids.shape[-1]:], skip_special_tokens=True)
+
+    answers = extract_answers(generated, num_items)
+    print(f"Problem {r['id']}: generated {len(answers)} answers for {num_items} items", flush=True)
+
+    explanation = ""
+    if EXPLAIN:
+        explanation = generate_explanation(context, query, answers)
+
+    rows.append({
+        "id": r["id"],
+        "pred": json.dumps(answers, ensure_ascii=False),
+        "explanation": explanation,
+    })
+
+pd.DataFrame(rows).to_csv("submission.csv", index=False)
+print("wrote submission.csv", flush=True)