初始化项目，由ModelHub XC社区提供模型
Model: JuliaKreutzerCohere/tiny-aya-water-prompt Source: Original Platform
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ModelHub XC
259
script.py
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259
script.py
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import os
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import subprocess
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import sys
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def _install_bundled_deps() -> None:
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"""Install transformers from bundled wheels (eval sandbox has no PyPI access)."""
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wheels_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wheels")
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if not os.path.isdir(wheels_dir):
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return
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subprocess.run(
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[
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sys.executable,
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"-m",
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"pip",
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"install",
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"-q",
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"--no-index",
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f"--find-links={wheels_dir}",
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"transformers==4.56.2",
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],
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check=True,
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)
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_install_bundled_deps()
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import re
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import csv
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import json
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import shutil
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import tempfile
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# The repo is the working directory at run time, and there is no network.
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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MODEL_ID = "."
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MAX_NEW_TOKENS = 2000
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TEMPERATURE = 0.8
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TOP_P = 0.95
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MAX_ATTEMPTS = 5
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def load_tokenizer(model_id: str = "."):
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"""Load tokenizer, converting tokenizer.json for older tokenizers if needed."""
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tokenizer_path = os.path.join(model_id, "tokenizer.json")
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with open(tokenizer_path, encoding="utf-8") as handle:
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data = json.load(handle)
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merges = data.get("model", {}).get("merges", [])
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if not merges or not isinstance(merges[0], list):
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return AutoTokenizer.from_pretrained(model_id)
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# Older tokenizers expect merge pairs as "a b" strings, not ["a", "b"] lists.
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data["model"]["merges"] = [" ".join(piece) for piece in merges]
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tmpdir = tempfile.mkdtemp()
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for name in ("tokenizer_config.json", "special_tokens_map.json"):
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src = os.path.join(model_id, name)
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if os.path.isfile(src):
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shutil.copy(src, tmpdir)
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with open(os.path.join(tmpdir, "tokenizer.json"), "w", encoding="utf-8") as handle:
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json.dump(data, handle)
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return AutoTokenizer.from_pretrained(tmpdir)
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SYSTEM = (
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"You solve International Linguistics Olympiad problems by reasoning from the "
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"data in CONTEXT you are given to solve the problems in QUERY. \n"
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"There are common TASK TYPES that we specify below, but "
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"you may meet a TASK TYPE you have never seen: read the "
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"instruction and the examples, and answer the QUERY in the same form they use.\n\n"
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"Common TASK TYPES and what to return: \n"
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"`translation`: return the translated form only, in the language the task asks for; \n"
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"`fill_blanks`: return only the missing form for each indicated blank "
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"(beware: this could be many different things: a word, a part of a word or a phonetic transcription---pay close attention to what part of the CONTEXT is missing in QUERY); \n"
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"`match_letters`: return only the option letter (for example A, B, C); \n"
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"`text_to_num`: return the number in digits; \n"
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"`num_to_text`: return the number written out in words, in the language asked; \n"
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"any other type: return exactly what the instruction asks for, nothing else. \n\n"
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"As the first part of your answer, reason step by step about (1) the linguistic "
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"rules that can be deduced from the given examples in CONTEXT, and (2) "
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"how to apply them to the given problems in QUERY, and (3) in what format answers need to be returned (words, numbers, phonetic transcriptions, ...). \n"
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"Then write a draft of the final answer. "
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"Subsequently, compare it with the format requirements again, "
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"and verify it's compliant with the deduced rules, and it is complete, i.e. has an answer for each element in QUERY. "
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"If necessary, correct and refine."
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"Finally, write a line that says exactly `FINAL ANSWERS:` "
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"and, below it, write the answers to the items requested in QUERY (not those in CONTEXT),"
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"one answer per line (separated by \n) in the order the items are asked for in the QUERY -- the "
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"bare answer only, no numbering, no quotes, no extra text, according to the given TASK TYPE."
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)
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tok = load_tokenizer(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, torch_dtype=torch.float16, device_map="auto"
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).eval()
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with open("/tmp/data/test.csv", encoding="utf-8", newline="") as f:
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test_rows = list(csv.DictReader(f))
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outputs_queries_types = []
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for r in test_rows:
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# Create the prompt.
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messages = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content":
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f"CONTEXT:{r['context'].strip()}\nTASK TYPE:`{r['task_type']}`\n\nQUERY:{r['query'].strip()}"},
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]
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ids = tok.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt",
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).to(model.device)
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# Generate the answer.
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done = False
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attempts = 0
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while not done and attempts < MAX_ATTEMPTS:
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with torch.no_grad():
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out = model.generate(
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ids,
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max_new_tokens=MAX_NEW_TOKENS,
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do_sample=True,
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temperature=TEMPERATURE,
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top_p=TOP_P,)
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text = tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True).strip()
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# IF NO FINAL ANSWER keyword is used, try again.
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attempts += 1
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if "final answer" not in text.lower() or text.lower().split('final answer')[1].split('\n')==0:
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print(f'TRYING AGAIN...attempts #{attempts+1}/{MAX_ATTEMPTS}')
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else:
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done = True
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outputs_queries_types.append((text, r['id'], r['query'], r['task_type']))
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print(f"{len(outputs_queries_types)}/{len(test_rows)} done", flush=True)
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# Postprocess and store the answers.
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def expected_answer_count(query: str, task_type: str) -> int:
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if task_type == "match_letters":
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numbered = re.findall(r"^\s*\d+\.", query, re.MULTILINE)
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return len(numbered) or 1
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if "blanks" in query.lower():
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range_match = re.search(r"\((\d+)-(\d+)\)", query)
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if range_match:
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return int(range_match.group(2)) - int(range_match.group(1)) + 1
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return len(re.findall(r"\(\d+\)", query)) or 1
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numbered = re.findall(r"^\s*\d+[.)]", query, re.MULTILINE)
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return len(numbered) or 1
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def split_single_line_answer(text: str, expected: int, task_type: str) -> list[str]:
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text = text.strip()
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if expected <= 1:
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return [text]
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def try_split(pattern: str) -> list[str] | None:
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parts = [part.strip() for part in re.split(pattern, text) if part.strip()]
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return parts if len(parts) == expected else None
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if task_type == "match_letters":
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for pattern in (r"\s+", r",\s*", r";\s*"):
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if result := try_split(pattern):
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return result
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letters = re.findall(r"[A-Za-z]", text)
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if len(letters) == expected:
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return [letter.upper() for letter in letters]
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return [text]
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if task_type in ("text_to_num", "num_to_text"):
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for pattern in (r",\s*", r";\s*", r"\s+"):
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if result := try_split(pattern):
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return result
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return [text]
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for pattern in (r";\s*", r",\s*"):
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if result := try_split(pattern):
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return result
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return [text]
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def postprocess_answer(text, query, task_type):
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"""Keep only the lines after the last 'FINAL ANSWERS:' marker, one answer per line,
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stopping at the first empty line. If eval_type is multiple and only one line as answer, split at whitespace."""
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# Updated regex to be more flexible with surrounding characters
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marker_match = list(re.finditer(r"(?im)^[^\w\n]*final answers?[^\w\n]*:?\s*$", text))
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if marker_match:
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text_after_marker = text[marker_match[-1].end():]
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#print('FOUND FINAL ANSWER', text_after_marker)
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else:
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#print("No 'FINAL ANSWERS:' marker found")
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return []
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answers = []
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answer_lines = text_after_marker.splitlines()
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for i, line in enumerate(answer_lines):
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stripped_line = line.strip('`').strip()
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# Stop processing if an empty line is encountered (not as first line)
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if stripped_line=='':
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continue
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# Use a more precise regex to only remove numbering if it's a prefix to other text
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# This ensures that lines which are just numbers (e.g., '1') are not stripped.
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match_numbered_prefix = re.match(r"^\s*\d+[.)]\s+(.*)", stripped_line)
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if match_numbered_prefix:
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cleaned_line = match_numbered_prefix.group(1).strip()
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else:
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cleaned_line = stripped_line
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# Remove any bold markdown '**'
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cleaned_line = re.sub(r"\*\*", "", cleaned_line).strip()
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# Specific handling for 'match_letters' task type to strip extra words
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if task_type == 'match_letters':
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parts = [
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part.strip("().[]")
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for part in re.split(r"[\s,;]+", cleaned_line)
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if part.strip()
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]
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if not (len(parts) > 1 and all(re.fullmatch(r"[A-Za-z]", part) for part in parts)):
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match_letter_word = re.match(
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r"^\s*(?:\(([A-Za-z])\)|\[([A-Za-z])\]|([A-Za-z]))\.?:?\s*(.*)$",
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cleaned_line,
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)
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if match_letter_word:
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letter = (
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match_letter_word.group(1)
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or match_letter_word.group(2)
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or match_letter_word.group(3)
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)
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cleaned_line = letter.upper()
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# Append the cleaned, non-empty line
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if cleaned_line:
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answers.append(cleaned_line)
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#print('PARSED ANSWERS', answers)
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# Compare against QUERY length: sometimes model forgets newlines
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#print('QUERY', query)
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expected = expected_answer_count(query, task_type)
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query_len = len(query.splitlines()) - 2
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#print(query_len)
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if len(answers) == 1 and expected > 1:
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answers = split_single_line_answer(answers[0], expected, task_type)
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return answers
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rows = []
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for answer, row_id, query, task_type in outputs_queries_types:
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answers = postprocess_answer(answer, query, task_type)
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rows.append({"id": row_id, "pred": json.dumps(answers, ensure_ascii=False)})
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with open("submission.csv", "w", encoding="utf-8", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=["id", "pred"])
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writer.writeheader()
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writer.writerows(rows)
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print("wrote submission.csv", flush=True)
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