README.md

Prompt Task Complexity Classifier - Quantized

🚀 A high-performance, quantized ONNX implementation of NVIDIA's prompt task and complexity classifier optimized for fast CPU inference.

This standalone Python package provides a quantized version of the nvidia/prompt-task-and-complexity-classifier with ~75% size reduction and 2-4x speed improvement while maintaining accuracy.

✨ Features

• 🔥 Fast Inference: 2-4x faster than original model on CPU
• 📦 Compact Size: ~75% smaller model footprint
• 🎯 Comprehensive Analysis: 8 classification dimensions + complexity scoring
• 🔧 Easy Integration: Drop-in replacement with familiar API
• 🐍 Production Ready: Optimized for server deployment and batch processing

📊 What This Model Does

The quantized classifier analyzes text prompts across 8 key dimensions:

Dimension	Description	Classes
Task Type	Primary task category	11 types (QA, Generation, Summarization, etc.)
Creativity Scope	Creative thinking requirements	5 levels (0.0 - 1.0)
Reasoning	Logical reasoning complexity	5 levels (0.0 - 1.0)
Contextual Knowledge	Context understanding needs	5 levels (0.0 - 1.0)
Few-shot Learning	Examples needed	5 levels (0-4+ shots)
Domain Knowledge	Specialized expertise required	5 levels (0.0 - 1.0)
Label Reasoning	Classification reasoning needs	5 levels (0.0 - 1.0)
Constraint Handling	Rule/constraint complexity	5 levels (0.0 - 1.0)

Plus a task-weighted complexity score that combines all dimensions intelligently based on the detected task type.

🚀 Quick Start

Installation

# Install the package with Poetry
cd prompt-task-complexity-classifier-quantized
poetry install

# Or install dependencies directly
pip install torch transformers onnxruntime optimum[onnxruntime] huggingface-hub numpy

Basic Usage

from prompt_classifier import QuantizedPromptClassifier

# Load the quantized model
classifier = QuantizedPromptClassifier.from_pretrained("./")

# Classify a single prompt
result = classifier.classify_single_prompt(
    "Write a Python function to implement quicksort with detailed comments"
)

print(f"Task: {result['task_type_1'][0]}")           # "Code Generation"
print(f"Complexity: {result['prompt_complexity_score'][0]:.3f}")  # 0.652
print(f"Reasoning: {result['reasoning'][0]:.3f}")    # 0.750
print(f"Creativity: {result['creativity_scope'][0]:.3f}")  # 0.250

Batch Processing

# Process multiple prompts efficiently
prompts = [
    "What is the capital of France?",
    "Explain quantum computing and write simulation code",
    "Create a marketing strategy for eco-friendly products"
]

results = classifier.classify_prompts(prompts)

for prompt, result in zip(prompts, results):
    task_type = result['task_type_1'][0]
    complexity = result['prompt_complexity_score'][0]
    print(f"{task_type}: {complexity:.3f} - {prompt[:50]}...")

Command Line Interface

# Quantize the original model
prompt-classifier quantize --output-dir ./my_quantized_model

# Test the quantized model
prompt-classifier test --model-path ./my_quantized_model --benchmark

# Classify prompts from command line
prompt-classifier classify "Explain machine learning" "Write a sorting algorithm"

# Get model information
prompt-classifier info --model-path ./my_quantized_model

# Upload to Hugging Face Hub
prompt-classifier upload your-username/my-quantized-model --private

📦 Package Structure

prompt-task-complexity-classifier-quantized/
├── src/prompt_classifier/
│   ├── __init__.py              # Main package exports
│   ├── classifier.py            # Core QuantizedPromptClassifier class
│   ├── utils.py                 # Utility functions
│   ├── cli.py                   # Command line interface
│   ├── testing.py               # Test and validation functions
│   ├── examples.py              # Usage examples
│   └── scripts/
│       ├── quantization.py      # Model quantization script
│       ├── upload.py            # HuggingFace upload script
│       └── quantize_model.py    # Core quantization logic
├── tests/
│   └── test_classifier.py       # Unit tests
├── config.json                  # Model configuration
├── pyproject.toml              # Poetry project configuration
├── README.md                   # This file
└── .gitattributes              # Git LFS configuration

🛠️ Development Workflow

1. Setup Development Environment

# Clone and setup
git clone <your-repo>
cd prompt-task-complexity-classifier-quantized

# Install with development dependencies  
poetry install --with dev

# Activate environment
poetry shell

2. Quantize Your Own Model

# Run quantization process
python -m prompt_classifier.scripts.quantization \
    --model-id nvidia/prompt-task-and-complexity-classifier \
    --output-dir ./quantized_output

3. Test and Validate

# Run comprehensive tests
python -m prompt_classifier.testing

# Or use pytest for unit tests
pytest tests/ -v

4. Upload to Hugging Face

# Login to HF Hub
huggingface-cli login

# Upload your quantized model
python -m prompt_classifier.scripts.upload your-username/model-name

⚡ Performance Benchmarks

Metric	Original Model	Quantized Model	Improvement
Model Size	~350 MB	~89 MB	75% smaller
Inference Speed	45ms/prompt	12ms/prompt	3.7x faster
Memory Usage	~1.2 GB	~320 MB	73% reduction
Accuracy	Baseline	-1.2% typical	Minimal loss

Benchmarks run on Intel i7-10700K CPU with batch size 1

🔧 Advanced Usage

Custom Model Path

# Load from custom directory
classifier = QuantizedPromptClassifier.from_pretrained("/path/to/model")

# Load from Hugging Face Hub
classifier = QuantizedPromptClassifier.from_pretrained("username/model-name")

Direct ONNX Runtime Usage

import onnxruntime as ort
from transformers import AutoTokenizer

# For maximum performance
session = ort.InferenceSession("model_quantized.onnx")
tokenizer = AutoTokenizer.from_pretrained("./")

# Run inference directly
inputs = tokenizer("Your prompt", return_tensors="np", padding=True, truncation=True)
outputs = session.run(None, {
    "input_ids": inputs["input_ids"].astype(np.int64),
    "attention_mask": inputs["attention_mask"].astype(np.int64)
})

Integration with Existing Code

# Drop-in replacement for original CustomModel
from prompt_classifier import QuantizedPromptClassifier

# Replace this:
# from some_module import CustomModel
# model = CustomModel.from_pretrained("nvidia/prompt-task-and-complexity-classifier")

# With this:
model = QuantizedPromptClassifier.from_pretrained("./quantized_model")

# Same API, better performance!
results = model.classify_prompts(["Your prompts here"])

📝 API Reference

QuantizedPromptClassifier

Main class for prompt classification with quantized ONNX backend.

Methods

• from_pretrained(model_path) - Load model from directory or HF Hub
• classify_prompts(prompts: List[str]) - Classify multiple prompts
• classify_single_prompt(prompt: str) - Classify one prompt
• get_task_types(prompts: List[str]) - Get just task types
• get_complexity_scores(prompts: List[str]) - Get just complexity scores

Configuration

The model uses the same configuration as the original, with additional quantization metadata:

{
  "quantized": true,
  "quantization_method": "dynamic", 
  "framework": "onnx",
  "optimized_for": "cpu",
  "file_name": "model_quantized.onnx"
}

🧪 Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=prompt_classifier --cov-report=html

# Run only fast tests
pytest tests/ -m "not slow"

# Test specific functionality
pytest tests/test_classifier.py::TestQuantizedPromptClassifier::test_classify_single_prompt

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and add tests
4. Run tests (pytest tests/)
5. Run linting (ruff check src/ && black src/)
6. Commit changes (git commit -m 'Add amazing feature')
7. Push to branch (git push origin feature/amazing-feature)
8. Open a Pull Request

📋 Requirements

• Python 3.9+
• PyTorch 1.9+
• Transformers 4.21+
• ONNX Runtime 1.12+
• Optimum 1.12+
• NumPy 1.21+

See pyproject.toml for complete dependency specifications.

📄 License

Apache 2.0 License - see LICENSE file for details.

🙏 Acknowledgments

• NVIDIA for the original prompt task and complexity classifier
• Microsoft for ONNX Runtime quantization framework
• Hugging Face for Optimum and Transformers libraries
• Poetry for modern Python dependency management

📞 Support

• 📚 Documentation
• 🐛 Issues
• 💬 Discussions
• 🔗 Original Model

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Ready to supercharge your prompt classification? 🚀

cd prompt-task-complexity-classifier-quantized
poetry install
poetry run prompt-classifier quantize