siyah1/falcon-7b-mamba-mental-health

Model Card for falcon mamba Multi-Turn Doctor Conversation Model

Model Details

Model Description

This model is a fine-tuned version of the falcon mamba model, specifically tailored for multi-turn doctor-patient conversations. It leverages the powerful language generation capabilities of falcon 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: falcon mamba

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

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the tokenizer and model from the Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("siyah1/falcon-7b-mamba-mental-health")
model = AutoModelForCausalLM.from_pretrained("siyah1/falcon-7b-mamba-mental-health")

# Example input: patient describing a symptom
input_text = "Hi, doctor"

# 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)