vllm-semantic-router-bench 1.0.0

Comprehensive benchmark suite for semantic router vs direct vLLM evaluation across multiple reasoning datasets

vLLM Semantic Router Benchmark Suite

A comprehensive benchmark suite for evaluating semantic router performance against direct vLLM across multiple reasoning datasets. Perfect for researchers and developers working on LLM routing, evaluation, and performance optimization.

🎯 Key Features

• 6 Major Reasoning Datasets: MMLU-Pro, ARC, GPQA, TruthfulQA, CommonsenseQA, HellaSwag
• Router vs vLLM Comparison: Side-by-side performance evaluation
• Multiple Evaluation Modes: NR (neutral), XC (explicit CoT), NR_REASONING (auto-reasoning)
• Research-Ready Output: CSV files and publication-quality plots
• Dataset-Agnostic Architecture: Easy to extend with new datasets
• CLI Tools: Simple command-line interface for common operations

🚀 Quick Start

Installation

pip install vllm-semantic-router-bench

Basic Usage

# Quick test on MMLU dataset
vllm-semantic-router-bench test --dataset mmlu --samples 5

# Full comparison between router and vLLM
vllm-semantic-router-bench compare --dataset arc --samples 10

# List available datasets
vllm-semantic-router-bench list-datasets

# Run comprehensive multi-dataset benchmark
vllm-semantic-router-bench comprehensive

Python API

from vllm_semantic_router_bench import DatasetFactory, list_available_datasets

# Load a dataset
factory = DatasetFactory()
dataset = factory.create_dataset("mmlu")
questions, info = dataset.load_dataset(samples_per_category=10)

print(f"Loaded {len(questions)} questions from {info.name}")
print(f"Categories: {info.categories}")

📊 Supported Datasets

Dataset	Domain	Categories	Difficulty	CoT Support
MMLU-Pro	Academic Knowledge	57 subjects	Undergraduate	✅
ARC	Scientific Reasoning	Science	Grade School	❌
GPQA	Graduate Q&A	Graduate-level	Graduate	❌
TruthfulQA	Truthfulness	Truthfulness	Hard	❌
CommonsenseQA	Common Sense	Common Sense	Hard	❌
HellaSwag	Commonsense NLI	~50 activities	Moderate	❌

🔧 Advanced Usage

Custom Evaluation Script

import subprocess
import sys

# Run detailed benchmark with custom parameters
cmd = [
    "router-bench",  # Main benchmark script
    "--dataset", "mmlu",
    "--samples-per-category", "20", 
    "--run-router", "--router-models", "auto",
    "--run-vllm", "--vllm-models", "openai/gpt-oss-20b",
    "--vllm-exec-modes", "NR", "NR_REASONING",
    "--output-dir", "results/custom_test"
]

subprocess.run(cmd)

Plotting Results

# Generate plots from benchmark results
bench-plot --router-dir results/router_mmlu \
           --vllm-dir results/vllm_mmlu \
           --output-dir results/plots \
           --dataset-name "MMLU-Pro"

📈 Research Output

The benchmark generates research-ready outputs:

• CSV Files: Detailed per-question results and aggregated metrics
• Master CSV: Combined results across all test runs
• Plots: Accuracy and token usage comparisons
• Summary Reports: Markdown reports with key findings

Example Output Structure

results/
├── research_results_master.csv          # Main research data
├── comparison_20250115_143022/
│   ├── router_mmlu/
│   │   └── detailed_results.csv
│   ├── vllm_mmlu/  
│   │   └── detailed_results.csv
│   ├── plots/
│   │   ├── accuracy_comparison.png
│   │   └── token_usage_comparison.png
│   └── RESEARCH_SUMMARY.md

🛠️ Development

Local Installation

git clone https://github.com/vllm-project/semantic-router
cd semantic-router/bench
pip install -e ".[dev]"

Adding New Datasets

1. Create a new dataset implementation in dataset_implementations/
2. Inherit from DatasetInterface
3. Register in dataset_factory.py
4. Add tests and documentation

from vllm_semantic_router_bench import DatasetInterface, Question, DatasetInfo

class MyDataset(DatasetInterface):
    def load_dataset(self, **kwargs):
        # Implementation here
        pass
    
    def format_prompt(self, question, style="plain"):
        # Implementation here  
        pass

📋 Requirements

• Python 3.8+
• OpenAI API access (for model evaluation)
• Hugging Face account (for dataset access)
• 4GB+ RAM (for larger datasets)

Dependencies

• openai>=1.0.0 - OpenAI API client
• datasets>=2.14.0 - Hugging Face datasets
• pandas>=1.5.0 - Data manipulation
• matplotlib>=3.5.0 - Plotting
• seaborn>=0.11.0 - Advanced plotting
• tqdm>=4.64.0 - Progress bars

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Common Contributions

• Adding new datasets
• Improving evaluation metrics
• Enhancing visualization
• Performance optimizations
• Documentation improvements

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🔗 Links

• Documentation: https://vllm-semantic-router.com
• GitHub: https://github.com/vllm-project/semantic-router
• Issues: https://github.com/vllm-project/semantic-router/issues
• PyPI: https://pypi.org/project/vllm-semantic-router-bench/

📞 Support

• GitHub Issues: Bug reports and feature requests
• Documentation: Comprehensive guides and API reference
• Community: Join our discussions and get help from other users

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Made with ❤️ by the vLLM Semantic Router Team