---
language: en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- ruslanmv
- llama
- trl
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- ruslanmv/ai-medical-dataset
---

# ai-medical-model-4bit: Fine-Tuned Llama3 for Technical Medical Questions
[![](future.jpg)](https://ruslanmv.com/)
This repository provides a fine-tuned version of the powerful Llama3 8B Instruct model, specifically designed to answer medical questions in an informative way. 
It leverages the rich knowledge contained in the AI Medical Dataset ([ruslanmv/ai-medical-dataset](https://huggingface.co/datasets/ruslanmv/ai-medical-dataset)).

**Model & Development**

- **Developed by:** ruslanmv
- **License:** Apache-2.0
- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct

**Key Features**

- **Medical Focus:** Optimized to address health-related inquiries.
- **Knowledge Base:** Trained on a comprehensive medical chatbot dataset.
- **Text Generation:** Generates informative and potentially helpful responses.

**Installation**

This model is accessible through the Hugging Face Transformers library. Install it using pip:

```bash
!python -m pip install --upgrade pip
!pip3 install torch==2.2.1  torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121
!pip install  bitsandbytes  accelerate
```

**Usage Example**

Here's a Python code snippet demonstrating how to interact with the `ai-medical-model-4bit` model and generate answers to your medical questions:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig 
import torch
model_name = "ruslanmv/ai-medical-model-4bit"
device_map = 'auto' 
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    trust_remote_code=True,
    use_cache=False,
    device_map=device_map
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

def askme(question):
  prompt = f"<|start_header_id|>system<|end_header_id|> You are a Medical AI chatbot assistant. <|eot_id|><|start_header_id|>User: <|end_header_id|>This is the question: {question}<|eot_id|>"
  # Tokenizing the input and generating the output
  #prompt = f"{question}"
  # Tokenizing the input and generating the output
  inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
  outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
  answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
  # Try Remove the prompt
  try:
      # Split the answer at the first line break, assuming system intro and question are on separate lines
      answer_parts = answer.split("\n", 1)
      # If there are multiple parts, consider the second part as the answer
      if len(answer_parts) > 1:
        answers = answer_parts[1].strip()  # Remove leading/trailing whitespaces
      else:
        answers = ""  # If no split possible, set answer to empty string
      print(f"Answer: {answers}")   
  except:
      print(answer)  

# Example usage
# - Question:  Make the question.
question="What was the main cause of the inflammatory CD4+ T cells?"
askme(question)
```
the type of answer is :
```
The main cause of inflammatory CD4+ T cells is typically attributed to an imbalance in the immune system's response to an antigen, leading to an overactive immune response. This can occur due to various factors, such as:

1. **Autoimmune disorders**: In conditions like rheumatoid arthritis, lupus, or multiple sclerosis, the immune system mistakenly attacks the body's own tissues, leading to chronic inflammation and the activation of CD4+ T cells.
2. **Infections**: Certain infections, like tuberculosis or HIV, can trigger an excessive immune response, resulting in the activation of CD4+ T cells.
3. **Environmental factors**: Exposure to pollutants, toxins, or allergens can trigger an immune response, leading to the activation of CD4+ T cells.
4. **Genetic predisposition**: Some individuals may be more susceptible to developing inflammatory CD4+ T cells due to their genetic makeup.
5. **Immunosuppression**: Weakened immune systems, such as those resulting from immunosuppressive therapy or HIV/AIDS, can lead to an overactive immune response and the activation of CD4+ T cells.

These factors can lead to the activation of CD4+
```
**Important Note**

This model is intended for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with a qualified healthcare provider for any medical concerns.

**License**

This model is distributed under the Apache License 2.0 (see LICENSE file for details).

**Contributing**

We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request.

**Disclaimer**

While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed.