OpenBioLLm-Derm-gguf

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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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.

Feel free to provide any missing details or correct any assumptions, and I'll update the model card accordingly.