Model Card for Sum (3B) Small
Model Description
Sum Small is a powerful language model specifically designed to generate SOAP summaries from medical dialogues. It is a fine-tuned version of the Microsoft/Phi-3-mini-4k-instruct using the Omi Health/medical-dialogue-to-soap-summary dataset. This model demonstrates superior performance compared to larger models like GPT-4.
Intended Use
This model is intended for research and development in AI-powered medical documentation. It is not ready for direct clinical use without further validation and should be integrated with additional safety guardrails before deployment in a medical setting.
Training Data
The model was trained on the Omi Health's synthetic medical-dialogue-to-soap-summary dataset, which consists of 10,000 synthetically generated dialogues and corresponding SOAP summaries.
Training Procedure
Training was conducted on NVIDIA A100 GPUs, ensuring efficient processing and model optimization.
Evaluation
The performance of Summ Small has been evaluated using several Rouge metrics as follows:
| Model | ROUGE-1 |
|---|---|
| Omi-Sum 3B Small | 70 |
| GPT4Turbo | 69 |
| LLama3 8B Instruct | 59 |
| GPT3.5 | 54 |
| Phi-3 3B mini 4k instruct | 55 |
| Phi2 basic | 41 |
These results showcase Sum Small's capabilities in generating accurate SOAP summaries compared to other leading models.
Limitations
While Sum Small demonstrates promising results, the training data is completely synthetic and not derived from actual clinical interactions. Care must be taken when considering this model for practical applications, as it requires significant testing and adaptation to meet clinical safety standards.
Licensing
The Sum Small model is released under the MIT License, which permits broad use with fewer restrictions, making it accessible for both commercial and non-commercial use.
Ethical Considerations
Users are urged to consider the ethical implications of AI in healthcare and ensure that any deployment of such models prioritizes patient safety and data privacy.
Contact
For more information or to request access to Sum Small API, please contact [email protected].
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