🧠 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.
🚀 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 |
📊 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. |
🌟 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.
📜 License
This model is licensed under the MIT License.
📞 Community
For questions or support, connect with us via:
- Twitter: RecurvAI
- Email: support@recurvai.com
🤝 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.