DatarusAI/Datarus-R1-14B-preview

Datarus-R1-14B-preview

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🚀 Overview

Datarus-R1-14B-Preview is a 14B-parameter open-weights language model fine-tuned from Qwen2.5-14B-Instruct, designed to act as a virtual data analyst and graduate-level problem solver. Unlike traditional models trained on isolated Q&A pairs, Datarus learns from complete analytical trajectories—including reasoning steps, code execution, error traces, self-corrections, and final conclusions—all captured in a ReAct-style notebook format.

Key Highlights

• —🎯 State-of-the-art efficiency: Surpasses similar-sized models and competes with 32B+ models while using 18-49% fewer tokens
• —🔄 Dual reasoning interfaces: Supports both Agentic (ReAct) mode for interactive analysis and Reflection (CoT) mode for concise documentation
• —📊 Superior performance: Achieves up to 30% higher accuracy on AIME 2024/2025 and LiveCodeBench
• —💡 "AHA-moment" pattern: Exhibits efficient hypothesis refinement in 1-2 iterations, avoiding circular reasoning loops

🔗 Quick Links

• —🌐 Website: https://datarus.ai
• —💬 Try the Demo: https://chat.datarus.ai
• —🛠️ Jupyter Agent: GitHub Repository
• —📄 Paper: Datarus-R1: An Adaptive Multi-Step Reasoning LLM

📊 Performance

Benchmark Results

*Reported values from official papers

Token Efficiency and Performance

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🎯 Model Card

Model Details

• —Model Type: Language Model for Reasoning and Data Analysis
• —Parameters: 14.8B
• —Training Data: 144,000 synthetic analytical trajectories across finance, medicine, numerical analysis, and other quantitative domains + A curated collection of reasoning datasets.
• —Language: English
• —License: Apache 2.0

Intended Use

Primary Use Cases

• —Data Analysis: Automated data exploration, statistical analysis, and visualization
• —Mathematical Problem Solving: Graduate-level mathematics including AIME-level problems
• —Code Generation: Creating analytical scripts and solving programming challenges
• —Scientific Reasoning: Complex problem-solving in physics, chemistry, and other sciences
• —Interactive Notebooks: Building complete analysis notebooks with iterative refinement

Dual Mode Usage

Agentic Mode (for interactive analysis)

• —Use <step>, <thought>, <action>, <action_input>, <observation> tags
• —Enables iterative code execution and refinement
• —Best for data analysis, simulations, and exploratory tasks

Reflection Mode (for documentation)

• —Use <think> and <answer> tags
• —Produces compact, self-contained reasoning chains
• —Best for mathematical proofs, explanations, and reports

📚 Citation

@article{benchaliah2025datarus,
  title={Datarus-R1: An Adaptive Multi-Step Reasoning LLM for Automated Data Analysis},
  author={Ben Chaliah, Ayoub and Dellagi, Hela},
  journal={arXiv preprint arXiv:2508.13382},
  year={2025}
}

🤝 Contributing

We welcome contributions! Please see our GitHub repository for:

• —Bug reports and feature requests
• —Pull requests
• —Discussion forums

📄 License

This model is released under the Apache 2.0 License.

🙏 Acknowledgments

We thank the Qwen team for the excellent base model and the open-source community for their valuable contributions.

📧 Contact

• —Email: ayoub1benchaliah@gmail.com, hela.dellagi@outlook.com
• —Website: https://datarus.ai
• —Demo: https://chat.datarus.ai

<div align="center"> <strong>Experience the future of AI-powered data analysis with Datarus-R1</strong>

Try Demo | View Code | Read Paper </div>

⭐ Support

If you find this model and Agent pipeline useful, please consider _Like/Star_! Your support helps us continue improving the project.

Found a bug or have a feature request? Please open an issue on GitHub.

<p align="center">Made with ❤️ by the Datarus Team from Paris</p>