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README.md

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+
+---
+
+license: apache-2.0
+library_name: transformers
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-14B
+pipeline_tag: text-generation
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Datarus-R1-14B-preview-GGUF
+This is quantized version of [DatarusAI/Datarus-R1-14B-preview](https://huggingface.co/DatarusAI/Datarus-R1-14B-preview) created using llama.cpp
+
+# Original Model Card
+
+
+# Datarus-R1-14B-preview
+
+<div align="center">
+  <img src="https://i.postimg.cc/7hsStNgm/logo-icon-2-1.png" alt="Datarus Logo" width="150"/>
+  
+  [![Model](https://img.shields.io/badge/Model-Datarus--R1--14B-blue)](https://huggingface.co/DatarusAI/Datarus-R1-14B-preview)
+  [![License](https://img.shields.io/badge/License-Apache%202.0-green)](LICENSE)
+  [![Website](https://img.shields.io/badge/Website-datarus.ai-orange)](https://datarus.ai)
+  [![Demo](https://img.shields.io/badge/Demo-Try%20Now-purple)](https://chat.datarus.ai)
+  [![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2508.13382)
+</div>
+
+## 🚀 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](https://datarus.ai)
+- 💬 **Try the Demo**: [https://chat.datarus.ai](https://chat.datarus.ai)
+- 🛠️ **Jupyter Agent**: [GitHub Repository](https://github.com/DatarusAI/Datarus-JupyterAgent)
+- 📄 **Paper**: [Datarus-R1: An Adaptive Multi-Step Reasoning LLM](https://arxiv.org/abs/2508.13382)
+
+## 📊 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
+
+<div align="center">
+    <img src="https://i.postimg.cc/NMSppNM4/perf-efficiency.png" alt="LCB-Efficiency" width="600"/>
+    <img src="https://i.postimg.cc/nV341Ssf/efficiency.png" alt="Efficiency" width="600" />
+</div>
+
+## 🎯 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
+
+```bibtex
+@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](https://github.com/DatarusAI/Datarus-JupyterAgent) 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**: [email protected], [email protected]
+- **Website**: [https://datarus.ai](https://datarus.ai)
+- **Demo**: [https://chat.datarus.ai](https://chat.datarus.ai)
+
+---
+
+<div align="center">
+  <strong>Experience the future of AI-powered data analysis with Datarus-R1</strong>
+  
+  [Try Demo](https://chat.datarus.ai) | [View Code](https://github.com/DatarusAI/Datarus-JupyterAgent) | [Read Paper](https://arxiv.org/abs/2508.13382)
+</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>