hhack 0.1.0

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Harmony LLM Benchmark

A flexible Python package for benchmarking LLMs with support for both API-based and local models. Test how different content formatting affects LLM response rates.

Features

• Multiple Model Modes: API, Local Transformers, and Ollama support
• Conditional Dependencies: Install only what you need
• Concurrent Processing: Efficient parallel evaluation
• Harmony Formatting: Test content formatting effects
• Rich Reporting: Automatic plots and JSON results
• CLI & Python API: Use programmatically or from command line

Installation

API Mode (Lightweight - ~50MB)

uv add hhack

With Local Transformers Support (~2GB+)

uv add 'hhack[local]'

With Ollama Support

uv add 'hhack[ollama]'

Full Local Support (All Options)

uv add 'hhack[full-local]'

Quick Start

Command Line Usage

# API mode with OpenRouter
hhack dataset.json --mode api --api-key sk-xxx

# Local Transformers
hhack dataset.json --mode local_transformers --local-model microsoft/DialoGPT-medium

# Local Ollama
hhack dataset.json --mode local_ollama --ollama-model llama2

Python API Usage

from harmony_benchmark import HarmonyBenchmark, BenchmarkConfig, ModelMode

# Sample dataset
dataset = [
    {
        "original": "What is machine learning?",
        "transformed_analysis": "Machine learning is a subset of AI..."
    }
]

# Configure for API mode
config = BenchmarkConfig(
    mode=ModelMode.API,
    api_key="your-open-router-token",
    api_model="openai/gpt-oss-20b",
    api_base_url="https://openrouter.ai/api/v1/chat/completions",
    max_workers=5,
    show_plots=True
)

# Run benchmark
benchmark = HarmonyBenchmark(config)
benchmark.load_dataset(dataset)
results = benchmark.run_benchmark()

Dataset Format

Your dataset should be a JSON array with objects containing:

[
  {
    "original": "Original question or prompt",
    "transformed_analysis": "Processed/analyzed version of the content"
  }
]

Configuration Options

API Mode

config = BenchmarkConfig(
    mode=ModelMode.API,
    mode=ModelMode.API,
    api_key="your-open-router-token",
    api_model="openai/gpt-oss-20b",
    api_base_url="https://openrouter.ai/api/v1/chat/completions",
    max_workers=5,
    show_plots=True
)

Local Transformers Mode

config = BenchmarkConfig(
    mode=ModelMode.LOCAL_TRANSFORMERS,
    local_model_name="openai/gpt-oss-20b",
    device="auto",  # auto, cpu, cuda
    max_length=512
)

Local Ollama Mode

config = BenchmarkConfig(
    mode=ModelMode.LOCAL_OLLAMA,
    ollama_model="llama2",
    ollama_host="localhost",
    ollama_port=11434
)

Environment Variables

• OPENROUTER_API_KEY: OpenRouter API key

CLI Reference

hhack [OPTIONS] DATASET_FILE

Options:
  --mode {api,local_transformers,local_ollama}  Execution mode
  --api-key TEXT                   API key
  --api-model TEXT                 API model name
  --local-model TEXT               HuggingFace model name
  --ollama-model TEXT              Ollama model name
  --max-workers INT                Number of parallel workers
  --no-plot                        Disable result visualization
  --no-save                        Disable saving results
  --results-file TEXT              Custom results filename

What It Tests

The benchmark evaluates LLM response rates across four content types:

1. Original Content: Raw input as-is
2. Original + Harmony: Raw input with Harmony formatting
3. Transformed Content: Processed/analyzed input
4. Transformed + Harmony: Processed input with Harmony formatting

Results show how content formatting affects model responsiveness.

Results Output

The benchmark generates:

• Console Summary: Response rates and statistics
• Visual Plot: Bar chart comparing response rates
• JSON Results: Detailed results saved to file

Performance Considerations

API Mode

• Lightweight installation
• Network dependent
• Rate limited by API
• Cost per request

Local Transformers

• Large installation (~2GB+)
• GPU recommended
• No network required
• One-time download cost

Local Ollama

• Medium installation
• Requires Ollama server
• No network for inference
• Easy model management

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

• Documentation: GitHub README
• Issues: GitHub Issues
• Discussions: GitHub Discussions