Model Details

Model Description

• 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.

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

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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• 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.