🧠 Recurv-Medical-Deepseek-R1 Model

Overview

The Recurv-Medical-Deepseek-R1 model is an enhanced version of Deepseek’s R1, designed to offer accurate and context-specific support for healthcare professionals and researchers. This model is particularly effective in answering medical questions, aiding in patient history gathering, and generating comprehensive explanations tailored to medical situations, utilizing advanced instruction tuning techniques.

(Knowledge cut-off date: 22th January, 2025)

🎯 Key Features

• Optimized for medical-specific queries across various specialties.
• Fine-tuned for clinical and research-oriented workflows.
• Lightweight parameter-efficient fine-tuning with safetensors format.
• Multi-turn conversation support for context-rich interactions.
• Generates comprehensive answers and evidence-based suggestions.

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🚀 Model Card

Parameter	Details
Base Model	DeepSeek R1 Distill Llama 8B
Fine-Tuning Framework	safetensors
Dataset Size	67,299 high-quality Q&A pairs
Context Length	4,096 tokens
Training Steps	100,000
Model Size	8 billion parameters

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📊 Model Architecture

Dataset Sources

The dataset comprises high-quality Q&A pairs curated from medical textbooks, research papers, and clinical guidelines.

Source	Description
PubMed	Extracted insights from open-access medical research.
Clinical Guidelines	Data sourced from WHO, CDC, and specialty-specific guidelines.
EHR-Simulated Data	Synthetic datasets modeled on real-world patient records for anamnesis workflows.

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🌟 Try The Model

🚀 Recurv-Medical-Deepseek-R1 on Our Website

🙌 Contributing

We welcome contributions to enhance Recurv-Medical-Deepseek-R1. You can:

• Share feedback or suggestions on the Hugging Face Model Hub
• Submit pull requests or issues for model improvement.

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📜 License

This model is licensed under the MIT License.

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📞 Community

For questions or support, connect with us via:

• Twitter: RecurvAI
• Email: [email protected]

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🤝 Acknowledgments

Special thanks to the medical community and researchers for their valuable insights and support in building this model. Together, we’re advancing AI in healthcare.