gguf llama medical en dataset:keivalya/MedQuad-MedicalQnADataset dataset:turquise/Comprehensive_Medical_QA_Dataset base_model:meta-llama/Llama-3.1-8B-Instruct base_model:quantized:meta-llama/Llama-3.1-8B-Instruct endpoints_compatible region:us
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
datasets:
- keivalya/MedQuad-MedicalQnADataset
- turquise/Comprehensive_Medical_QA_Dataset
language:
- en
tags:
- medical
---
# Fine-Tuning Llama-3.1 with Comprehensive Medical Q&A Dataset

This project fine-tunes the **Llama-3.1 8B Model** using the **Comprehensive Medical Q&A Dataset** to build a specialized model capable of answering medical questions. 

---

## 🚀 Features

- Fine-tuned on a diverse dataset of over **43,000 medical Q&A pairs**.
- Supports **31 distinct types of medical queries**, including treatments, chronic diseases, and protocols.
- Provides answers sourced from doctors, nurses, and pharmacists.

---

## 📂 Dataset Overview

### **Comprehensive Medical Q&A Dataset**

- **Source:** [Huggingface Hub](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset)
- **License:** CC0 1.0 Universal (Public Domain Dedication)

#### **Key Details**
- **Total Questions:** 43,000+  
- **Categories:** 31 medical question types (`qtype`)  
- **Columns:**  
  - `qtype`: Type of medical question (e.g., Treatment, Symptoms).  
  - `Question`: Patient's medical question.  
  - `Answer`: Expert response (from doctors, nurses, and pharmacists).  

### **How the Dataset is Used**
- **Filtering:** Questions are filtered by `qtype` for domain-specific fine-tuning.  
- **Analysis:** Queries are analyzed to understand patterns, such as correlations between treatments and chronic conditions.  
- **Applications:** Insights can be applied to build medical educational tools, predictive models, and virtual assistants.

For more details, check the [dataset documentation](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset).

---

## 💻 How to Use This Model

The fine-tuned model is available on Hugging Face under the repository: [`turquise/MedQA_q4`](https://huggingface.co/turquise/MedQA_q4). Below are several ways to use the model:

### **Using llama-cpp-python Library**
```python
from llama_cpp import Llama

# Load the model
llm = Llama.from_pretrained(
    repo_id="turquise/MedQA_q4",
    filename="MedQA.Q4_K_M.gguf",
)

# Query the model
output = llm(
    "What is Medullary Sponge Kidney?",
    max_tokens=512,
    echo=True
)
print(output)
```

### **Using llama.cpp**
#### **Install via Homebrew**
```bash
brew install llama.cpp

llama-cli \
  --hf-repo "turquise/MedQA_q4" \
  --hf-file MedQA.Q4_K_M.gguf \
  -p "What is Medullary Sponge Kidney?"
```
#### **Use Pre-Built Binary**
```bash
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases

./llama-cli \
  --hf-repo "turquise/MedQA_q4" \
  --hf-file MedQA.Q4_K_M.gguf \
  -p "What is Medullary Sponge Kidney?"
```
#### **Build from Source Code**
```bash
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DLLAMA_CURL=ON
cmake --build build -j --target llama-cli

./build/bin/llama-cli \
  --hf-repo "turquise/MedQA_q4" \
  --hf-file MedQA.Q4_K_M.gguf \
  -p "What is Medullary Sponge Kidney?"
```

---

## 🤖 Example Usages
This model can assist with the following tasks:

- Answering medical questions:
```python
question = "What are the symptoms of diabetes?"
output = llm(question, max_tokens=512)
print(output)
```
- Providing insights for healthcare education: Example: Answering queries about diseases, treatments, and chronic conditions.
- Supporting virtual assistants by handling frequently asked healthcare-related questions.

---

## ⚠️ Disclaimer

- This model **does not provide medical advice** and should not replace professional medical consultation.
- For any health-related questions or concerns, please consult a doctor or a licensed healthcare professional.

---

## 🤖 Applications

This fine-tuned model can be used to:
- Build **virtual assistants** and chatbots for healthcare-related queries.
- Assist healthcare professionals by handling routine inquiries.
- Enhance **medical education platforms** with AI-powered insights.

---

## 📜 Acknowledgements

- Dataset: [Huggingface Hub - MedQuad](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset).
- Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth).

If you use this project or dataset in your research, please credit the original authors.

---

## 📝 License

This project is open-sourced under the **CC0 1.0 Universal License**. See the dataset [license details](https://creativecommons.org/publicdomain/zero/1.0/).

---

## 📧 Contact

For questions or collaboration, reach out via [HF Model Community](https://huggingface.co/turquise/MedQA_q4/discussions).