---
license: mit
language:
- en
base_model: ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1
pipeline_tag: text-generation
tags:
- biology
- medical
- fine-tuning
library_name: transformers
---

# Model Card for Fine-Tuned Bio-Medical-Llama-3-8B

This model is a fine-tuned version of **Bio-Medical-Llama-3-8B-V1**, designed to enhance its performance for specialized biomedical and healthcare-related tasks. It provides responses to medical questions, explanations of health conditions, and insights into biology topics.

---

## Model Details

### Model Description

- **Developed by:** ContactDoctor Research Lab
- **Fine-Tuned by:** Gokul Prasath M
- **Model type:** Text Generation (Causal Language Modeling)
- **Language(s):** English
- **License:** MIT
- **Fine-Tuned from Model:** Bio-Medical-Llama-3-8B-V1

This fine-tuned model aims to improve accuracy and relevancy in generating biomedical-related responses, helping healthcare professionals and researchers with faster, more informed guidance.

---

## Uses

### Direct Use

- Biomedical question answering
- Patient education and healthcare guidance
- Biology and medical research support

### Downstream Use

- Can be further fine-tuned for specific domains within healthcare, such as oncology or pharmacology.
- Integrates into larger medical chatbots or virtual assistants for clinical settings.

### Out-of-Scope Use

The model is not a substitute for professional medical advice, diagnosis, or treatment. It should not be used for emergency or diagnostic purposes.

---

## Fine-Tuning Details

### Fine-Tuning Dataset

The model was fine-tuned on a domain-specific dataset consisting of medical articles, clinical notes, and health information databases.

### Fine-Tuning Procedure

- **Precision:** Mixed-precision training using bf16 for optimal performance and memory efficiency.
- **Quantization:** 4-bit LoRA for lightweight deployment.
- **Hyperparameters**:
  - **Learning Rate**: 2e-5
  - **Batch Size**: 4
  - **Epochs**: 3

### Training Metrics

During fine-tuning, the model achieved the following results:
- **Training Loss:** 0.5396 at 1000 steps

---

## Evaluation

### Evaluation Data

The model was evaluated on a sample of medical and biological queries to assess its accuracy, relevance, and generalizability across health-related topics.

### Metrics

- **Accuracy:** Evaluated by response relevance to medical queries.
- **Loss:** Final training loss of 0.5396

---

## Example Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# Load the fine-tuned model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("path/to/your-finetuned-model/tokenizer")
model = AutoModelForCausalLM.from_pretrained("path/to/your-finetuned-model")

# Initialize the pipeline
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Generate a response
response = generator("What are the symptoms of hypertension?", max_length=100)
print(response[0]["generated_text"])
```

## Limitations and Recommendations
The model may not cover the latest medical research or all conditions. It is recommended for general guidance rather than direct clinical application.

## Bias, Risks, and Limitations
Potential biases may exist due to dataset limitations. Responses should be verified by professionals for critical decisions.