Upload eval_gsm8k_vllm.py with huggingface_hub

eval_gsm8k_vllm.py

@@ -11,56 +11,60 @@
 
 import os
 import re
-from datasets import load_dataset
 
-os.environ.setdefault("HF_HUB_TOKEN", os.getenv("HF_TOKEN", ""))
-MODEL = os.getenv("MODEL", "Qwen/Qwen3.5-4B")
-LIMIT = int(os.getenv("LIMIT", "250"))
+def main():
+    from datasets import load_dataset
+    from vllm import LLM, SamplingParams
+    from transformers import AutoTokenizer
 
-print(f"Loading GSM8K dataset (limit={LIMIT})...")
-ds = load_dataset("openai/gsm8k", "main", split="test").select(range(min(LIMIT, 1319)))
+    os.environ.setdefault("HF_HUB_TOKEN", os.getenv("HF_TOKEN", ""))
+    MODEL = os.getenv("MODEL", "Qwen/Qwen3.5-4B")
+    LIMIT = int(os.getenv("LIMIT", "250"))
 
-print(f"Loading model {MODEL} with vLLM...")
-from vllm import LLM, SamplingParams
-from transformers import AutoTokenizer
+    print(f"Loading GSM8K dataset (limit={LIMIT})...")
+    ds = load_dataset("openai/gsm8k", "main", split="test").select(range(min(LIMIT, 1319)))
 
-tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
-llm = LLM(model=MODEL, trust_remote_code=True, dtype="half", gpu_memory_utilization=0.9)
+    print(f"Loading model {MODEL} with vLLM...")
+    tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
+    llm = LLM(model=MODEL, trust_remote_code=True, dtype="half", gpu_memory_utilization=0.9)
 
-sys_msg = "You are a helpful assistant that solves math problems step by step."
+    sys_msg = "You are a helpful assistant that solves math problems step by step."
 
-problems = []
-answers = []
-for ex in 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']}"},
-    ]
-    formatted = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
-    problems.append(formatted)
-    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]
-    answers.append(gt)
+    problems = []
+    answers = []
+    for ex in 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']}"},
+        ]
+        formatted = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+        problems.append(formatted)
+        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]
+        answers.append(gt)
 
-print(f"Generating answers for {len(problems)} problems...")
-sampling_params = SamplingParams(temperature=0.0, max_tokens=512, stop=["<|im_end|>", "<|endoftext|>"])
-outputs = llm.generate(problems, sampling_params)
+    print(f"Generating answers for {len(problems)} problems...")
+    sampling_params = SamplingParams(temperature=0.0, max_tokens=512, stop=["<|im_end|>", "<|endoftext|>"])
+    outputs = llm.generate(problems, sampling_params)
 
-correct = 0
-total = len(outputs)
-for i, output in enumerate(outputs):
-    gen = output.outputs[0].text.strip()
-    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 ""
+    correct = 0
+    total = len(outputs)
+    for i, output in enumerate(outputs):
+        gen = output.outputs[0].text.strip()
+        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 ""
 
-    if pred == answers[i]:
-        correct += 1
-    if i < 5 or i % 50 == 0:
-        print(f"Q{i+1}: GT={answers[i]} Pred={pred} OK={pred==answers[i]}")
+        if pred == answers[i]:
+            correct += 1
+        if i < 5 or i % 50 == 0:
+            print(f"Q{i+1}: GT={answers[i]} Pred={pred} OK={pred==answers[i]}")
 
-accuracy = correct / total * 100
-print(f"\nGSM8K: {correct}/{total} = {accuracy:.2f}%")
+    accuracy = correct / total * 100
+    print(f"\nGSM8K: {correct}/{total} = {accuracy:.2f}%")
+
+if __name__ == "__main__":
+    main()