Datarus-R1-14B-preview
🚀 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
| Benchmark | Datarus-R1-14B-Preview | QwQ-32B | Phi-4-reasoning | DeepSeek-R1-Distill-14B |
|---|---|---|---|---|
| LiveCodeBench v6 | 57.7 | 56.6 | 52.6 | 48.6 |
| AIME 2024 | 70.1 | 76.2 | 74.6* | - |
| AIME 2025 | 66.2 | 66.2 | 63.1* | - |
| GPQA Diamond | 62.1 | 60.1 | 55.0 | 58.6 |
*Reported values from official papers
Token Efficiency and Performance
🎯 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
Try Demo | View Code | Read Paper
⭐ 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.