初始化项目，由ModelHub XC社区提供模型
Model: divaspoudel/iol-div Source: Original Platform
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ModelHub XC
167
script.py
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167
script.py
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import os
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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# --- runtime bootstrap: PyPI is reachable in the eval sandbox even though the
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# --- HF Hub is not, so make sure 4-bit deps are present (needed to fit the T4).
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import subprocess, sys
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def _pip(*pkgs):
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try:
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subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=False)
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except Exception as e:
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print("pip bootstrap skipped:", e, flush=True)
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_pip("bitsandbytes>=0.43.0", "accelerate>=0.30.0", "sentencepiece", "tiktoken")
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import re, json, time
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import pandas as pd
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import torch
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START = time.time()
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TIME_BUDGET = 27 * 60 # stop generating with margin before the 30-min hard limit
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# On the platform the repo IS the working dir, so "." holds the weights.
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# IOL_MODEL_DIR lets a local dry-run point at a downloaded snapshot instead.
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MODEL_ID = os.environ.get("IOL_MODEL_DIR", ".")
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MAX_NEW_TOKENS = 512
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# ---------------------------------------------------------------------------
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# Load the model in 4-bit (nf4) so 7B weights fit in 16 GB of T4 VRAM.
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# Use float16 compute: the T4 is a Turing GPU with no native bfloat16.
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# ---------------------------------------------------------------------------
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from transformers import AutoTokenizer, AutoModelForCausalLM
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try:
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from transformers import BitsAndBytesConfig
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_bnb = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.float16,
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)
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load_kwargs = dict(quantization_config=_bnb)
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except Exception as e:
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print("bitsandbytes unavailable, falling back to fp16:", e, flush=True)
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load_kwargs = dict(torch_dtype=torch.float16)
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try:
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tok = AutoTokenizer.from_pretrained(MODEL_ID)
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except Exception as e:
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print("fast tokenizer failed, retrying slow:", e, flush=True)
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tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, device_map="auto", **load_kwargs
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).eval()
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if tok.pad_token_id is None:
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tok.pad_token_id = tok.eos_token_id
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df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def expected_n(query, context):
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# How many numbered items must this row's answer list contain?
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for pat in (r"(?m)^\s*(\d+)\s*[\.\)]", r"\((\d+)\)"):
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m = re.findall(pat, query)
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if m:
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return len(m)
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# matching tasks number their items in the context, not the query
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m = re.findall(r"(?m)^\s*(\d+)\s*[\.\)]", context)
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return len(m) if m else 1
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def strip_num(s):
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# Remove a leading list marker ("1.", "2)", "(3)") but NEVER a bare number,
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# so numeric answers like "111" survive. Punctuation after the digit is required.
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return re.sub(r"^\s*(?:\(\d+\)|\d+\s*[\.\):])\s*", "", s).strip()
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# A line that begins with a list marker of the same forms.
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NUMLINE = r"^\s*(?:\(\d+\)|\d+\s*[\.\):-])"
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def parse_output(text, n, task_type):
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ans_part, expl = text, ""
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m = re.search(r"(?is)\bEXPLANATION\b\s*:?", text)
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if m:
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ans_part = text[:m.start()]
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expl = text[m.end():].strip()
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m2 = re.search(r"(?is)\bANSWERS?\b\s*:?", ans_part)
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if m2:
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ans_part = ans_part[m2.end():]
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lines = [ln.strip() for ln in ans_part.splitlines() if ln.strip()]
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numbered = [ln for ln in lines if re.match(NUMLINE, ln)]
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use = numbered if numbered else lines
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answers = [strip_num(ln) for ln in use]
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# Fallback: model crammed items onto one comma-separated line (common for
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# matching/number tasks). Split it back out, but not for free-text tasks
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# where commas can legitimately appear inside an answer.
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if len(answers) < n and task_type in ("match_letters", "text_to_num", "num_to_text"):
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flat = []
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for ln in use:
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flat += re.split(r"\s*[,;]\s*", strip_num(ln))
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flat = [x for x in flat if x != ""]
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if len(flat) > len(answers):
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answers = flat
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if len(answers) < n:
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answers += [""] * (n - len(answers))
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expl = re.sub(r"\s+", " ", expl).strip()[:800] # keep explanation single-line for CSV
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return answers[:n], expl
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TASK_HINTS = {
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"translation": "Translate each item. Answer in the language the query asks for.",
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"fill_blanks": "Work out the rule from the paired forms, then give the missing form for each blank.",
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"match_letters": "For each numbered item, output ONLY the letter label of its correct match.",
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"text_to_num": "Convert each written number into digits.",
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"num_to_text": "Write each number out in words in the task language.",
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}
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def build_messages(r):
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hint = TASK_HINTS.get(r["task_type"].strip().lower(), "Answer every numbered item.")
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sys_prompt = (
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"You are an expert solver of International Linguistics Olympiad problems. "
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"Each problem is fully self-contained: reason only from the data shown, with no outside "
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"knowledge of the language. Infer the grammar, vocabulary, or number system from the "
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"given examples, then answer every numbered item.\n"
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"OUTPUT FORMAT (follow exactly):\n"
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"ANSWERS:\n"
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"1. <answer to item 1>\n"
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"2. <answer to item 2>\n"
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"(one line per item, numbered, in the query's order, no commentary between them)\n"
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"EXPLANATION:\n"
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"<2-4 short sentences, human-readable, describing the rule you found>"
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)
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user_prompt = r["context"].strip() + "\n\n" + r["query"].strip() + "\n\n" + hint
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return [
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{"role": "system", "content": sys_prompt},
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{"role": "user", "content": user_prompt},
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]
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# ---------------------------------------------------------------------------
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# Run
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# ---------------------------------------------------------------------------
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out_rows = []
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for _, r in df.iterrows():
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n = expected_n(r["query"], r["context"])
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if time.time() - START > TIME_BUDGET:
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# Out of time: still emit a valid, correctly-sized (empty) row.
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out_rows.append({"id": r["id"],
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"pred": json.dumps([""] * n, ensure_ascii=False),
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"explanation": ""})
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continue
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enc = tok.apply_chat_template(
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build_messages(r), add_generation_prompt=True,
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return_tensors="pt", return_dict=True, # BatchEncoding incl. attention_mask
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).to(model.device)
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input_len = enc["input_ids"].shape[-1]
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with torch.no_grad():
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gen = model.generate(
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**enc, max_new_tokens=MAX_NEW_TOKENS,
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do_sample=False, pad_token_id=tok.pad_token_id,
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)
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text = tok.decode(gen[0][input_len:], skip_special_tokens=True).strip()
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answers, expl = parse_output(text, n, r["task_type"].strip().lower())
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out_rows.append({"id": r["id"],
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"pred": json.dumps(answers, ensure_ascii=False),
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"explanation": expl})
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print(str(len(out_rows)) + "/" + str(len(df)) + " done", flush=True)
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pd.DataFrame(out_rows, columns=["id", "pred", "explanation"]).to_csv(
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"submission.csv", index=False)
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print("wrote submission.csv", flush=True)
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