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README.md
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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## Model Details
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### Model Description
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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### Training Data
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:**
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:**
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- **Hours used:**
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- **Cloud Provider:**
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications
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### Model Architecture and Objective
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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tags:
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- skin
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- medical
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- dermatology
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datasets:
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- brucewayne0459/Skin_diseases_and_care
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---
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## Model Details
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### Model Description
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This model is designed for skin-related medical applications, particularly for use in a dermatology chatbot. It provides clear, accurate, and helpful information about various skin diseases, skincare routines, treatments, and related dermatological advice.
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- **Developed by:** Bruce_Wayne (The Batman)
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- **Funded by:** Wayne Industries
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- **Model type:** Text Generation
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- **Language(s) (NLP):** English
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- **Finetuned from model [optional]:** OpenBioLLM (llama-3) by aaditya/Llama3-OpenBioLLM-8B
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## Uses
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### Direct Use
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This model is fine-tuned on skin diseases and dermatology data and is used for a dermatology chatbot to provide clear, accurate, and helpful information about various skin diseases, skincare routines, treatments, and related dermatological advice.
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### Downstream Use
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The model can be integrated into healthcare applications, mobile apps for skin health monitoring, or systems providing personalized skincare advice.
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### Out-of-Scope Use
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The model should not be used for non-medical image analysis, general object detection, or without proper medical oversight. It is not designed to replace professional medical diagnosis.
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## Bias, Risks, and Limitations
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This model is trained on dermatology data, which might contain inherent biases. It is important to note that the model's responses should not be considered a substitute for professional medical advice. There may be limitations in understanding rare skin conditions or those not well-represented in the training data. The model still needs to be fine-tuned further to get accurate answers.
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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### Training Data
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The model is fine-tuned on a dataset containing information about various skin diseases and dermatology care. brucewayne0459/Skin_diseases_and_care
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#### Training Hyperparameters
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- **Training regime:** The model was trained using the following hyperparameters:
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Per device train batch size: 2
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Gradient accumulation steps: 4
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Warmup steps: 5
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Max steps: 120
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Learning rate: 2e-4
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Optimizer: AdamW (8-bit)
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Weight decay: 0.01
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LR scheduler type: Linear
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** Tesla t4
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- **Hours used:** 3hr
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- **Cloud Provider:** Google Colab
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## Technical Specifications
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### Model Architecture and Objective
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This model is based on the LLaMA (Large Language Model Meta AI) architecture and fine-tuned to provide dermatological advice.
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#### Hardware
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The training was performed on Tesla T4 GPU with 4-bit quantization and gradient checkpointing to optimize memory usage.
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## Feel free to provide any missing details or correct any assumptions, and I'll update the model card accordingly.
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