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| import os |
| import re |
| from datasets import load_dataset |
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| os.environ.setdefault("HF_HUB_TOKEN", os.getenv("HF_TOKEN", "")) |
| MODEL = os.getenv("MODEL", "Qwen/Qwen3.5-4B") |
| LIMIT = int(os.getenv("LIMIT", "250")) |
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| print(f"Loading GSM8K dataset (limit={LIMIT})...") |
| ds = load_dataset("openai/gsm8k", "main", split="test").select(range(min(LIMIT, 1319))) |
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| print(f"Loading model {MODEL}...") |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
|
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| tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True) |
| m = AutoModelForCausalLM.from_pretrained( |
| MODEL, trust_remote_code=True, device_map="auto", torch_dtype=torch.float16 |
| ) |
|
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| sys_msg = "You are a helpful assistant that solves math problems step by step." |
| correct = 0 |
| total = len(ds) |
|
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| for i, ex in enumerate(ds): |
| msgs = [ |
| {"role": "system", "content": sys_msg}, |
| {"role": "user", "content": f"Solve this math problem step by step. End your answer with 'ANSWER: <number>'\n\n{ex['question']}"}, |
| ] |
| inp = tok.apply_chat_template(msgs, tokenize=True, return_tensors="pt", add_generation_prompt=True) |
| inp = {k: v.to(m.device) for k, v in inp.items()} |
| with torch.no_grad(): |
| out = m.generate(**inp, max_new_tokens=512, do_sample=False) |
| gen = tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True) |
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| gt_match = re.search(r"####\s*(-?\d[\d,]*\.?\d*)", ex["answer"]) |
| gt = gt_match.group(1).replace(",", "") if gt_match else ex["answer"].strip().split()[-1] |
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| pred_match = re.search(r"ANSWER:\s*(-?\d[\d,]*\.?\d*)", gen) |
| if pred_match: |
| pred = pred_match.group(1).replace(",", "") |
| else: |
| nums = re.findall(r"-?\d[\d,]*\.?\d*", gen) |
| pred = nums[-1].replace(",", "") if nums else "" |
|
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| if pred == gt: |
| correct += 1 |
| if i < 5 or i % 50 == 0: |
| print(f"Q{i+1}: GT={gt} Pred={pred} OK={pred==gt}") |
|
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| accuracy = correct / total * 100 |
| print(f"\nGSM8K: {correct}/{total} = {accuracy:.2f}%") |
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