https://github.com/chihayayuka/open-o3
Run o3-pro on your computer. 🌌
https://github.com/chihayayuka/open-o3
ai deep-learning langchain machine-learning o3 openai openai-o3
Last synced: 8 months ago
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Run o3-pro on your computer. 🌌
- Host: GitHub
- URL: https://github.com/chihayayuka/open-o3
- Owner: ChihayaYuka
- License: other
- Created: 2025-02-21T15:40:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-01T10:25:43.000Z (over 1 year ago)
- Last Synced: 2025-03-01T11:25:00.631Z (over 1 year ago)
- Topics: ai, deep-learning, langchain, machine-learning, o3, openai, openai-o3
- Language: Python
- Homepage: https://lumenlab.cc/
- Size: 54.7 KB
- Stars: 16
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
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README
# Open-o3
[Blog](https://yuka.living/2025/02/26/open-o3-a-framework-for-exponentially-improving-llm-accuracy-through-systematic-resampling/) | [Website](https://lumenlab.cc) | [Feedback](https://github.com/ChihayaYuka/Open-o3/issues)
> [!TIP]
> This is a framework of reasoning, not a model. You can use the framework on any LLM.
Open-o3 is the open-source version of OpenAI's o3. This project aims to provide a powerful, open, and accessible language model for researchers and developers, advancing the field of artificial intelligence. Open-o3 is committed to offering flexible and customizable interfaces to support various reasoning tasks such as numerical calculations, programming, logic reasoning, etc. Through this open-source project, we hope to foster innovation and application of AI technology.
## Project Description
Open-o3 is an efficient and scalable language model, which is an open-source reproduction of OpenAI's o3 model. Our goal is to provide developers and researchers with a reliable and easy-to-use tool to help them implement and explore smarter and more complex natural language processing applications.
## Features
- **Systematic Resampling**: Implements OpenAI's o3 approach to exponentially improve accuracy through iterative reasoning.
- **Model-Agnostic**: Compatible with any LLM, allowing you to leverage your preferred model.
- **Result Tracking**: Save and analyze reasoning paths to improve performance.
- **Customizable Prompts**: Tailor the system prompt to your specific use cases and domains.
- **Extensible Architecture**: Easily build upon the core framework for specialized applications.
## Quick Start
### Basic Usage
```python
from open_o3 import o3
system_prompt = "You are a large reasoning model Open-o3 developed by Lumen Lab."
reasoner = o3(system_prompt=system_prompt, enable_tda=True, save_results=True)
reasoner.run_example()
```
### Advanced Configuration
```python
from open_o3 import o3
# Custom configuration
reasoner = o3(
system_prompt="You are specialized in solving mathematical problems.",
model="deepseek-r1",
max_iterations=5,
temperature=0.7,
enable_tda=True,
save_results=True,
result_path="./reasoning_logs/"
)
# Solve a specific problem
result = reasoner.solve("What is the integral of x²?")
print(result)
```
## Parameters
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `system_prompt` | str | Required | Initial instructions for the model |
| `model` | str | "deepseek-r1" | The LLM to use |
| `enable_tda` | bool | False | Whether to use TDA |
| `max_iterations` | int | 3 | Maximum reasoning iterations |
| `temperature` | float | 0.8 | Sampling temperature for the model |
| `save_results` | bool | False | Whether to save reasoning paths |
| `result_path` | str | ". /results/" | Path to save reasoning logs |
## Examples
### Mathematical Reasoning
```python
result = reasoner.solve("Solve for x: 2x² + 5x - 3 = 0")
```
### Code Generation
```python
code = reasoner.solve("Write a function to check if a string is a palindrome in Python")
```
### Logical Reasoning
```python
analysis = reasoner.solve("If all A are B, and some B are C, what can we conclude about A and C?")
```
## Usage Example
```python
system_prompt = "You are a large reasoning model Open-o3 developed by Lumen Lab."
reasoner = o3(system_prompt=system_prompt, enable_tda=True, save_results=True)
reasoner.run_example()
```
## Development and Contribution
We welcome contributions in any form, whether it's reporting bugs or submitting new features. To contribute, you can:
1. Create a new issue to report a problem or suggest a new feature.
2. Fork the repository and develop new features or fix bugs on your own branch.
3. Submit a pull request.
## Roadmap
- [ ] Multi-model ensemble reasoning
- [ ] Fine-tuning support for specific domains
- [ ] Performance benchmarks against other reasoning frameworks
- [ ] Community templates for common reasoning tasks
## Citation
If you use Open-o3 in your research, please cite:
```bibtex
@software{Open-o3,
author = {Lumen Lab},
title = {Open-o3: A Framework for Exponentially Improving LLM Accuracy},
url = {https://github.com/ChihayaYuka/Open-o3},
year = {2025},
}
```
## License
This project is licensed under the MIT License, allowing you to freely use, modify, and distribute the code. For more details, please refer to the [LICENSE](LICENSE) file.
## Contact Information
If you have any questions, suggestions, or feedback, feel free to contact us via the following methods:
- Email: [yuka@lumenlab.cc](mailto:yuka@lumenlab.cc)
- GitHub Issues: [https://github.com/ChihayaYuka/Open-o3](https://github.com/ChihayaYuka/Open-o3)