---
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

<!-- 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

``` python
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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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.