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--- |
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license: mit |
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language: |
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- en |
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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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pipeline_tag: text-generation |
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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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``` python |
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from llama_cpp import Llama |
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model_name = "brucewayne0459/OpenBioLLm-Derm-gguf" |
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model_file = "unsloth.Q8_0.gguf" |
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``` |
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## Training Details |
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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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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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. |
