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UltraMedical

Professional generic model for biomedical fields

#Dataset
#biomedicine
#Supervise fine-tuning
#Preference optimization
#Professional general model
UltraMedical

Product Details

The UltraMedical project aims to develop specialized general models in the biomedical field that are designed to answer questions relevant to examinations, clinical scenarios, and research questions, while maintaining a broad general knowledge base to effectively address cross-domain problems. By using advanced alignment techniques, including supervised fine-tuning (SFT), direct preference optimization (DPO), and odds ratio preference optimization (ORPO), large language models are trained on the UltraMedical dataset to create powerful and versatile models that effectively serve the needs of the biomedical community.

Main Features

1
Construct UltraMedical, a large-scale, high-quality biomedical instruction data set.
2
Enhance data diversity and complexity using a mix of synthetic and artificial data and preferred annotations.
3
Adopt advanced alignment techniques such as supervised fine-tuning (SFT), direct preference optimization (DPO), and odds ratio preference optimization (ORPO).
4
Provides language models of different sizes, including 7B-level and 70B-level models.
5
Achieved excellent average results in multiple medical benchmark tests.
6
Limitations of the model, such as hallucination issues and potential biases, are planned to be addressed in future studies.

How to Use

1
Visit UltraMedical’s GitHub page for project information and resources.
2
Read the project documentation to understand the model's architecture and functionality.
3
Download or access the model training dataset UltraMedical.
4
Choose the appropriate language model scale for experiments or applications according to your needs.
5
Test model performance and accuracy on biomedical problems.
6
Adjust how the model is used or its parameters based on feedback and results.

Target Users

The UltraMedical model is suitable for use by researchers, doctors and students in the biomedical field because it can provide professional answers relevant to examinations, clinical scenarios and research questions, while having a broad general knowledge base to help them deal more effectively with cross-domain medical problems.

Examples

Answers to questions in medical exams.

Professional consultation in clinical scenarios.

Professional analysis of biomedical research problems.

Quick Access

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Categories

🎓 educate
› AI medical health
› AI academic research

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