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--- |
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license: apache-2.0 |
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library_name: transformers |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-14B |
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pipeline_tag: text-generation |
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--- |
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# Datarus-R1-14B-preview |
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<div align="center"> |
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<img src="https://i.postimg.cc/7hsStNgm/logo-icon-2-1.png" alt="Datarus Logo" width="150"/> |
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[](https://huggingface.co/DatarusAI/Datarus-R1-14B-preview) |
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[](LICENSE) |
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[](https://datarus.ai) |
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[](https://chat.datarus.ai) |
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[](https://arxiv.org/abs/2508.13382) |
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</div> |
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## 🚀 Overview |
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**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. |
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### Key Highlights |
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- **🎯 State-of-the-art efficiency**: Surpasses similar-sized models and competes with 32B+ models while using 18-49% fewer tokens |
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- **🔄 Dual reasoning interfaces**: Supports both Agentic (ReAct) mode for interactive analysis and Reflection (CoT) mode for concise documentation |
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- **📊 Superior performance**: Achieves up to 30% higher accuracy on AIME 2024/2025 and LiveCodeBench |
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- **💡 "AHA-moment" pattern**: Exhibits efficient hypothesis refinement in 1-2 iterations, avoiding circular reasoning loops |
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## 🔗 Quick Links |
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- 🌐 **Website**: [https://datarus.ai](https://datarus.ai) |
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- 💬 **Try the Demo**: [https://chat.datarus.ai](https://chat.datarus.ai) |
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- 🛠️ **Jupyter Agent**: [GitHub Repository](https://github.com/DatarusAI/Datarus-JupyterAgent) |
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- 📄 **Paper**: [Datarus-R1: An Adaptive Multi-Step Reasoning LLM](https://arxiv.org/abs/2508.13382) |
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## 📊 Performance |
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### Benchmark Results |
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| Benchmark | Datarus-R1-14B-Preview | QwQ-32B | Phi-4-reasoning | DeepSeek-R1-Distill-14B | |
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|-----------|----------------|---------|-----------------|-------------------------| |
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| **LiveCodeBench v6** | 57.7 | 56.6 | 52.6 | 48.6 | |
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| **AIME 2024** | 70.1 | 76.2 | 74.6* | - | |
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| **AIME 2025** | 66.2 | 66.2 | 63.1* | - | |
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| **GPQA Diamond** | 62.1 | 60.1 | 55.0 | 58.6 | |
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*Reported values from official papers |
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### Token Efficiency and Performance |
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<div align="center"> |
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<img src="https://i.postimg.cc/NMSppNM4/perf-efficiency.png" alt="LCB-Efficiency" width="600"/> |
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<img src="https://i.postimg.cc/nV341Ssf/efficiency.png" alt="Efficiency" width="600" /> |
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</div> |
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## 🎯 Model Card |
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### Model Details |
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- **Model Type**: Language Model for Reasoning and Data Analysis |
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- **Parameters**: 14.8B |
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- **Training Data**: 144,000 synthetic analytical trajectories across finance, medicine, numerical analysis, and other quantitative domains + A curated collection of reasoning datasets. |
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- **Language**: English |
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- **License**: Apache 2.0 |
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### Intended Use |
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#### Primary Use Cases |
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- **Data Analysis**: Automated data exploration, statistical analysis, and visualization |
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- **Mathematical Problem Solving**: Graduate-level mathematics including AIME-level problems |
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- **Code Generation**: Creating analytical scripts and solving programming challenges |
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- **Scientific Reasoning**: Complex problem-solving in physics, chemistry, and other sciences |
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- **Interactive Notebooks**: Building complete analysis notebooks with iterative refinement |
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### Dual Mode Usage |
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#### Agentic Mode (for interactive analysis) |
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- Use `<step>`, `<thought>`, `<action>`, `<action_input>`, `<observation>` tags |
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- Enables iterative code execution and refinement |
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- Best for data analysis, simulations, and exploratory tasks |
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#### Reflection Mode (for documentation) |
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- Use `<think>` and `<answer>` tags |
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- Produces compact, self-contained reasoning chains |
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- Best for mathematical proofs, explanations, and reports |
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## 📚 Citation |
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```bibtex |
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@article{benchaliah2025datarus, |
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title={Datarus-R1: An Adaptive Multi-Step Reasoning LLM for Automated Data Analysis}, |
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author={Ben Chaliah, Ayoub and Dellagi, Hela}, |
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journal={arXiv preprint arXiv:2508.13382}, |
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year={2025} |
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} |
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``` |
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## 🤝 Contributing |
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We welcome contributions! Please see our [GitHub repository](https://github.com/DatarusAI/Datarus-JupyterAgent) for: |
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- Bug reports and feature requests |
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- Pull requests |
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- Discussion forums |
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## 📄 License |
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This model is released under the Apache 2.0 License. |
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## 🙏 Acknowledgments |
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We thank the Qwen team for the excellent base model and the open-source community for their valuable contributions. |
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## 📧 Contact |
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- **Email**: [email protected], [email protected] |
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- **Website**: [https://datarus.ai](https://datarus.ai) |
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- **Demo**: [https://chat.datarus.ai](https://chat.datarus.ai) |
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--- |
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<div align="center"> |
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<strong>Experience the future of AI-powered data analysis with Datarus-R1</strong> |
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[Try Demo](https://chat.datarus.ai) | [View Code](https://github.com/DatarusAI/Datarus-JupyterAgent) | [Read Paper](https://arxiv.org/abs/2508.13382) |
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</div> |
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## ⭐ Support |
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If you find this model and Agent pipeline useful, please consider __Like/Star__! Your support helps us continue improving the project. |
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Found a bug or have a feature request? Please open an issue on GitHub. |
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--- |
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<p align="center">Made with ❤️ by the Datarus Team from Paris</p> |
