---
datasets:
- brucewayne0459/Skin_diseases_and_care
language:
- en
license: mit
tags:
- medical
- dermatology
- skin_disease
- skin_care
- unsloth
- trl
- sft
---

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Bruce_Wayne(The Batman)
- **Model type:** Text Generation
- **Finetuned from model [optional]:** OpenBioLLM(llama-3)(aaditya/Llama3-OpenBioLLM-8B)

## You can find the gguf versions here --> https://huggingface.co/brucewayne0459/OpenBioLLm-Derm-gguf
### please let me know how the model works -->https://forms.gle/N14zZTkLpUr6Hf4BA
### Thank you!
## 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, skin care routines, treatments, and related dermatological advice.


## 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 need 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

Use the code below to get started with the model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "brucewayne0459/OpenBioLLm-Derm"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```

## 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 Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
You are a highly knowledgeable and empathetic dermatologist. Provide clear, accurate, and helpful information about various skin diseases, skin care routines, treatments, and related dermatological advice.

### Input:
{}

### Response:
{}
"""
EOS_TOKEN = tokenizer.eos_token  # Must add EOS_TOKEN

def formatting_prompts_func(examples):
    inputs = examples["Topic"]
    outputs = examples["Information"]
    texts = []

Prompt passed while fine tuning the model
#### 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:** Tesls T4 gpu
- **Hours used:** 1hr
- **Cloud Provider:** Google Colab


## Technical Specifications [optional]

### 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 the assumptions made, and I'll update the model card accordingly.

