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5e79b62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | #!/usr/bin/env python3
# coding=utf-8
# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Minimal vLLM MMLU-Pro single-sample inference example.
Example:
# Use embedded MMLU-Pro example sample (no dataset file needed)
python run_text_vllm_example.py --model-path $(pwd)/../../checkpoint_folder_textonly
# Or use a real MMLU-Pro json file
python run_text_vllm_example.py \
--model-path /path/to/model \
--mmlupro-json /path/to/mmlu_pro/test.json \
--sample-idx 0
"""
import argparse
import json
import re
from pathlib import Path
from typing import Any
from vllm import LLM, SamplingParams
SYSTEM_PROMPT = (
"<|im_start|>system\n"
"You are a helpful and harmless assistant.\n\n"
"You are not allowed to use any tools."
"<|im_end|>\n"
)
CHOICES = list("ABCDEFGHIJKLMNOP")
STOP_MARKERS = ("<|im_end|>", "<|end_of_text|>", "<|eot_id|>")
EXAMPLE_MMLUPRO_SAMPLE = {
"question": "Which organelle is primarily responsible for ATP production in eukaryotic cells?",
"options": [
"Golgi apparatus",
"Mitochondrion",
"Lysosome",
"Endoplasmic reticulum",
],
"answer": "B",
}
def build_mmlupro_user_prompt(sample: dict[str, Any]) -> str:
"""Build the MMLU-Pro user prompt with boxed-answer instruction."""
options = [opt for opt in sample["options"] if opt != "N/A"]
prompt = "Question:\n" + sample["question"] + "\n\nAnswer Choices:"
for i, opt in enumerate(options):
prompt += f"\n({CHOICES[i]}) {opt}"
prompt += (
"\n\nConclude your response with the sentence "
"`The answer is \\boxed{{X}}.`, in which X is the correct capital letter "
"of your choice."
)
return prompt.strip() + "\n"
def build_chatml_prompt(user_prompt: str, think: bool = True) -> str:
assistant_prefix = "<think>\n" if think else ""
return (
SYSTEM_PROMPT
+ "<|im_start|>user\n"
+ user_prompt
+ "<|im_end|>\n"
+ "<|im_start|>assistant\n"
+ assistant_prefix
)
def clean_generation(text: str) -> str:
"""Trim common end markers used in the eval scripts."""
cleaned = text
for marker in STOP_MARKERS:
idx = cleaned.find(marker)
if idx != -1:
cleaned = cleaned[:idx]
return cleaned.strip()
def extract_boxed_answer(text: str) -> str | None:
"""Extract answer letter from `The answer is \\boxed{X}.`"""
match = re.search(r"The answer is\s*\\boxed\{([A-P])\}\.?", text)
if match:
return match.group(1)
match = re.search(r"\\boxed\{([A-P])\}", text)
return match.group(1) if match else None
def load_mmlupro_sample(path: Path, sample_idx: int) -> dict[str, Any]:
with path.open("r", encoding="utf-8") as f:
data = json.load(f)
if not (0 <= sample_idx < len(data)):
raise IndexError(f"sample_idx={sample_idx} out of range [0, {len(data) - 1}]")
return data[sample_idx]
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Minimal vLLM MMLU-Pro inference with reasoning template."
)
parser.add_argument("--model-path", type=str, required=True, help="Model path for vLLM.")
parser.add_argument(
"--mmlupro-json",
type=str,
default=None,
help="Optional path to MMLU-Pro test.json (list of {question, options, ...}).",
)
parser.add_argument("--sample-idx", type=int, default=0, help="MMLU-Pro sample index.")
parser.add_argument("--tensor-parallel-size", type=int, default=1)
parser.add_argument("--max-tokens", type=int, default=131072)
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--top-p", type=float, default=0.95)
parser.add_argument("--seed", type=int, default=100)
parser.add_argument("--disable-thinking", action="store_true")
parser.add_argument("--fp16", action="store_true", help="Use float16 instead of bfloat16.")
parser.add_argument("--print-prompt", action="store_true", help="Print full prompt.")
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.mmlupro_json:
sample = load_mmlupro_sample(Path(args.mmlupro_json), args.sample_idx)
print(f"Loaded sample {args.sample_idx} from: {args.mmlupro_json}")
else:
sample = EXAMPLE_MMLUPRO_SAMPLE
print("Using embedded MMLU-Pro example sample.")
user_prompt = build_mmlupro_user_prompt(sample)
prompt = build_chatml_prompt(user_prompt, think=not args.disable_thinking)
if args.print_prompt:
print("=== PROMPT ===")
print(prompt)
print("==============")
dtype = "float16" if args.fp16 else "bfloat16"
model = LLM(
args.model_path,
dtype=dtype,
tensor_parallel_size=args.tensor_parallel_size,
trust_remote_code=True,
enable_prefix_caching=True,
enforce_eager=False,
)
sampling_params = SamplingParams(
temperature=args.temperature,
top_p=args.top_p,
max_tokens=args.max_tokens,
seed=args.seed,
)
output = model.generate([prompt], sampling_params)[0].outputs[0].text
output = clean_generation(output)
pred = extract_boxed_answer(output)
print("\n=== QUESTION ===")
print(sample.get("question", ""))
print("\n=== MODEL OUTPUT ===")
print(output)
print("\n=== PARSED PREDICTION ===")
print(pred if pred is not None else "No boxed answer found")
if "answer" in sample:
print("\n=== REFERENCE ANSWER ===")
print(sample["answer"])
if __name__ == "__main__":
main()
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