license: mit
language:
- en
tags:
- skin
- medical
- dermatology
datasets:
- brucewayne0459/Skin_diseases_and_care
pipeline_tag: text-generation
Model Details
Model Description
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.
- Developed by: Bruce_Wayne (The Batman)
- Funded by: Wayne Industries
- Model type: Text Generation
- Language(s) (NLP): English
- Finetuned from model [optional]: OpenBioLLM (llama-3) by aaditya/Llama3-OpenBioLLM-8B
Uses
Direct Use
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.
Downstream Use
The model can be integrated into healthcare applications, mobile apps for skin health monitoring, or systems providing personalized skincare advice.
Out-of-Scope Use
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.
Bias, Risks, and Limitations
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.
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
from llama_cpp import Llama
model_name = "brucewayne0459/OpenBioLLm-Derm-gguf"
model_file = "unsloth.Q8_0.gguf"
Training Details
Training Data
The model is fine-tuned on a dataset containing information about various skin diseases and dermatology care. brucewayne0459/Skin_diseases_and_care
Training Hyperparameters
- Training regime: The model was trained using the following hyperparameters: Per device train batch size: 2 Gradient accumulation steps: 4 Warmup steps: 5 Max steps: 120 Learning rate: 2e-4 Optimizer: AdamW (8-bit) Weight decay: 0.01 LR scheduler type: Linear
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: Tesla t4
- Hours used: 3hr
- Cloud Provider: Google Colab
Technical Specifications
Model Architecture and Objective
This model is based on the LLaMA (Large Language Model Meta AI) architecture and fine-tuned to provide dermatological advice.
Hardware
The training was performed on Tesla T4 GPU with 4-bit quantization and gradient checkpointing to optimize memory usage.