初始化项目，由ModelHub XC社区提供模型
Model: DatarusAI/Datarus-R1-14B-preview Source: Original Platform
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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**: ayoub1benchaliah@gmail.com, hela.dellagi@outlook.com
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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>
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