skkjodhpur/Gemma-Code-Instruct-Finetune-by-skk

Model

Fine-Tuning GEMMA-2B for Doctor-Patient Interaction: Efficient Model Adaptation Using LoRA and 4-bit Quantization

Model Description

This model is a fine-tuned version of GEMMA-2B, adapted specifically for doctor-patient interaction tasks. Fine-tuning was performed using Low-Rank Adaptation (LoRA) and 4-bit quantization for efficient model adaptation. The model aims to facilitate improved, context-aware, and relevant interactions in medical consultations, enhancing communication and understanding between healthcare providers and patients.

Developed by: Shailesh Kumar Khanchandani Shared by: Shailesh Kumar Khanchandani Model type: Causal Language Model Language(s) (NLP): English Finetuned from model: google/gemma-2b

Model Sources

Repository: Fine-Tuned GEMMA-2B

Uses

Direct Use

The model is intended for direct use in generating contextually appropriate responses for doctor-patient interactions. It can be used in virtual assistants, chatbots, and other medical consultation platforms to assist healthcare providers in communicating with patients.

Downstream Use

The model can be further fine-tuned for specific medical specialties or customized to fit the unique requirements of different healthcare systems or languages.

Out-of-Scope Use

The model is not intended for use in generating medical advice or diagnoses without human oversight. It should not be used in any scenario where critical health decisions are made solely based on its outputs.

Bias, Risks, and Limitations

The model inherits biases present in the training data. Users should be cautious of potential biases and inaccuracies, particularly in sensitive medical contexts.

Recommendations

Users should ensure that outputs are reviewed by qualified healthcare professionals before being utilized in a clinical setting. Continuous monitoring and updating of the model with diverse and representative data can help mitigate biases.

How to Get Started with the Model

Use the code below to get started with the model:

Python Code

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

def generate_text(prompt):
    # Model and tokenizer initialization
    model_name = "skkjodhpur/Gemma-Code-Instruct-Finetune-by-skk"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)

    # Move model to GPU if available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = model.to(device)

    # Tokenize input
    input_ids = tokenizer.encode(f"<s>[INST] {prompt} [/INST]", return_tensors="pt").to(device)

    # Generate text
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_length=200,
            num_return_sequences=1,
            do_sample=True,
            temperature=0.7,
        )

    # Decode and return the generated text
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

    # Example usage
    prompt = "I fell on uneven sidewalk on July 3rd and hurt my knee: I had no problem walking and I didn t have much pain just bruising on my knee with tenderness. Now as the bruising is entering the purple/green phase I am starting to get a lot of swelling in my calf and ankle- what should I do ??"
    response = generate_text(prompt)
    print("Generated response:")
    print(response)

Output:

Generated response: [INST] I fell on uneven sidewalk on July 3rd and hurt my knee: I had no problem walking and I didn t have much pain just bruising on my knee with tenderness. Now as the bruising is entering the purple/green phase I am starting to get a lot of swelling in my calf and ankle- what should I do ?? [/INST]

Output: Take care Chat Doctor - Avoid walking and running to protect your knee . - Take pain relievers to help reduce pain and swelling . - Take cold compresses to help reduce pain and swelling . - Use crutches to assist with walking for a few days to protect your knee.

Training Details

Training Data

The training data includes dialogues from the "Doctor-Chat-Dataset," which consists of anonymized conversations between doctors and patients. The dataset was processed to ensure privacy and relevancy.

Training Procedure

Preprocessing

The input texts were tokenized using AutoTokenizer from the transformers library. The dataset was mapped to the tokenized format and formatted appropriately for causal language modeling.

Training Hyperparameters

Training regime: Mixed precision (fp16) Batch size: 1 Gradient accumulation steps: 4 Warmup steps: 0.03 Max steps: 100 Learning rate: 2e-4 Optimizer: PagedAdamW (8-bit) Save strategy: Per epoch

Technical Specifications

Model Architecture and Objective The model follows the architecture of a causal language model, finetuned for specific tasks using parameter-efficient fine-tuning techniques (LoRA) and quantized to 4-bit precision for efficient deployment.

Software

Framework: PyTorch Libraries: transformers, peft, trl, accelerate, datasets

@article{khanchandani2024fine-tunedgemma, title={Fine-Tuning GEMMA-2B for Doctor-Patient Interaction: Efficient Model Adaptation Using LoRA and 4-bit Quantization}, author={Shailesh Kumar Khanchandani}, year={2024}, url={Coming Soon} }

APA:

Khanchandani, S. K. (2024). Fine-Tuning GEMMA-2B for Doctor-Patient Interaction: Efficient Model Adaptation Using LoRA and 4-bit Quantization.