---
library_name: transformers
tags:
- medical
- text-generation-inference
- llm
- biomistral
license: apache-2.0
datasets:
- ruslanmv/ai-medical-chatbot
- lavita/ChatDoctor-HealthCareMagic-100k
language:
- en
metrics:
- rouge
- bleu
---

# Model Card for BioMistral Multi-Turn Doctor Conversation Model

## Model Details

### Model Description

This model is a fine-tuned version of the BioMistral model, specifically tailored for multi-turn doctor-patient conversations. It leverages the powerful language generation capabilities of BioMistral to provide accurate and context-aware responses in medical dialogue scenarios.

- **Developed by:** Siyahul Haque T P
- **Model type:** Text-generation (LLM)
- **Language(s) (NLP):** English (en)
- **License:** Apache-2.0
- **Finetuned from model:** BioMistral



## Uses

### Direct Use

This model can be directly used for generating responses in multi-turn medical conversations, making it useful for applications like virtual health assistants and medical chatbots.

### Downstream Use 

This model can be further fine-tuned or integrated into larger healthcare applications, such as patient management systems or automated symptom checkers.

### Out-of-Scope Use

The model is not suitable for use in emergency medical situations, providing final diagnoses, or replacing professional medical advice.

## Bias, Risks, and Limitations

The model may reflect biases present in the training data, including underrepresentation of certain medical conditions or demographic groups. The model should not be used as a sole source of medical information and must be supervised by qualified healthcare professionals.

### Recommendations

Users should be aware of the potential biases and limitations of the model. It is recommended to use the model as a supplementary tool rather than a primary source of medical advice.

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the tokenizer and model from the Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")
model = AutoModelForCausalLM.from_pretrained("siyah1/BioMistral-7b-Chat-Doctor")

# Example input: patient describing a symptom
input_text = "Hello, doctor, I have a headache."

# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt")

# Generate a response from the model
outputs = model.generate(**inputs, max_length=100, num_return_sequences=1)

# Decode the generated response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Print the model's response
print("Doctor:", response)