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

# Model Card for Bio-Medical-Llama-3-8B-V1

This model is a fine-tuned version of **Bio-Medical-Llama-3-8B** for generating text related to biomedical knowledge. It is designed to assist in answering health and medical queries, serving as a robust tool for both healthcare professionals and general users.

---

## Model Details

### Model Description

- **Developed by:** ContactDoctor
- **Funded by:** ContactDoctor Research Lab
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** Bio-Medical-MultiModal-Llama-3-8B

This model was created to address the need for accurate, conversational assistance in healthcare, biology, and medical science.

---

## Uses

### Direct Use

Users can employ the model to generate responses to biomedical questions, explanations of medical concepts, and general healthcare advice.

### Downstream Use

This model can be further fine-tuned for specific tasks, such as diagnosis support, clinical decision-making, and patient education.

### Out-of-Scope Use

The model should not be used as a substitute for professional medical advice, emergency assistance, or detailed medical diagnoses.

---

## Bias, Risks, and Limitations

While the model is trained on extensive biomedical data, it might not cover every condition or the latest advancements. Users are advised to treat responses as informational rather than authoritative.

### Recommendations

- Use this model for general guidance, not as a substitute for professional advice.
- Regularly review updates and improvements for the latest accuracy enhancements.

---

## How to Get Started with the Model

You can use the model through the Hugging Face API or locally as shown in the example below.

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

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ContactDoctor/Bio-Medical-Llama-3-8B-V1")
model = AutoModelForCausalLM.from_pretrained("ContactDoctor/Bio-Medical-Llama-3-8B-V1")

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

# Generate a response
response = generator("What is hypertension?", max_length=100)
print(response[0]["generated_text"])
---
license: mit
language:
- en
base_model: ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1
pipeline_tag: text-generation
tags:
- biology
- medical
- fine-tuning
---

# 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.