Model Card for Model ID
The CiPE GenAI project is a revolutionary tool designed to improve medication management and safety by providing alerts for potential drug interactions and side effects using Generative AI technology.
Model Details
Model Description
- Developed by: Shubhankar Tripathy, Sid Vijay, Jiyeon Song, Aditi Killedar
- Funded by [optional]: [More Information Needed]
- Shared by [optional]:
- Model type: Fine-Tuned RAG Model
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: Neural-Chat-7B
Model Card for My Fine-Tuned Model
Model Description
- Purpose: [Describe the purpose of your model. What task does it perform?]
- Model architecture: [Specify the architecture, e.g., BERT, GPT-2, etc.]
- Training data: [Briefly describe the dataset used for training. Include any data cleaning or preprocessing steps.]
Intended Use
- Intended users: [Who are the intended users of the model?]
- Use cases: [Describe potential use cases for the model.]
Limitations
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Hardware
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Software Optimizations
- Known Optimizations: [Describe details about any optimizations used during training.]
Ethical Considerations
- Ethical concerns: [Discuss any ethical concerns related to the use of your model.]
More Information
- [Include any additional information, links, or references.]
- Repository: https://github.com/lonexreb/CiPE
- Demo [optional]: [More Information Needed]
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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