superllm 0.1.0

Enhanced LLM capabilities through test-time compute and search algorithms

SuperLLM

A powerful Python library for enhancing LLM capabilities through advanced test-time compute and search algorithms, focusing on expert human-AI co-construction. This project is under consturction. Don't come with any expectations!

🌟 Features

• Test-Time Compute Algorithms

  • Tree of Thoughts implementation
  • Self-consistency sampling
  • Dynamic prompt optimization
  • Multi-perspective reasoning

• Search Algorithms

  • Beam search with adaptive width
  • Best-first symbolic search
  • Guided exploration with expert feedback
  • Knowledge graph-based reasoning

• Expert Human-AI Co-Construction

  • Interactive reasoning frameworks
  • Expert feedback integration
  • Knowledge distillation from expert interactions
  • Adaptive learning from human guidance

🚀 Installation

pip install superllm

🔧 Quick Start

from superllm import ThoughtTree, ExpertFeedback
from superllm.search import AdaptiveBeamSearch

# Initialize a thought tree for complex reasoning
thought_tree = ThoughtTree()

# Create an expert feedback system
expert_system = ExpertFeedback()

# Configure adaptive beam search
search = AdaptiveBeamSearch(
    beam_width=5,
    max_steps=10
)

# Run enhanced inference
result = thought_tree.solve(
    prompt="Your complex question here",
    search_algorithm=search,
    expert_system=expert_system
)

📚 Documentation

For detailed documentation, visit docs.superllm.ai

🛠️ Core Components

1. Thought Trees: Implement tree-based reasoning strategies for complex problem-solving
2. Search Algorithms: Advanced search techniques for exploring solution spaces
3. Expert Systems: Tools for integrating human expert knowledge and feedback
4. Evaluation Metrics: Comprehensive metrics for assessing solution quality

🤝 Contributing

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

📄 License

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

🔗 Citation

If you use SuperLLM in your research, please cite:

@software{superllm2024,
  title = {SuperLLM: Enhanced LLM Capabilities through Test-Time Compute},
  author = {SuperLLM Contributors},
  year = {2024},
  url = {https://github.com/to314as/superllm}
}

🌟 Star History