Ellbendls/llama-3.2-3b-chat-doctor

Model Card: Llama-3.2-3B-Chat-Doctor

Model Details

Model Description

Llama-3.2-3B-Chat-Doctor is a specialized medical question-answering model based on the Llama 3.2 3B architecture. This model has been fine-tuned specifically for providing accurate and helpful responses to medical-related queries.

• —Developed by: Ellbendl Satria
• —Model type: Language Model (Conversational AI)
• —Language: English
• —Base Model: Meta Llama-3.2-3B-Instruct
• —Model Size: 3 Billion Parameters
• —Specialization: Medical Question Answering
• —License: llama3.2

Model Capabilities

• —Provides informative responses to medical questions
• —Assists in understanding medical terminology and health-related concepts
• —Offers preliminary medical information (not a substitute for professional medical advice)

Direct Use

This model can be used for:

• —Providing general medical information
• —Explaining medical conditions and symptoms
• —Offering basic health-related guidance
• —Supporting medical education and patient communication

Limitations and Important Disclaimers

⚠️ CRITICAL WARNINGS:

• —NOT A MEDICAL PROFESSIONAL: This model is NOT a substitute for professional medical advice, diagnosis, or treatment.
• —Always consult a qualified healthcare provider for medical concerns.
• —The model's responses should be treated as informational only and not as medical recommendations.

Out-of-Scope Use

The model SHOULD NOT be used for:

• —Providing emergency medical advice
• —Diagnosing specific medical conditions
• —Replacing professional medical consultation
• —Making critical healthcare decisions

Bias, Risks, and Limitations

Potential Biases

• —May reflect biases present in the training data
• —Responses might not account for individual patient variations
• —Limited by the comprehensiveness of the training dataset

Technical Limitations

• —Accuracy is limited to the knowledge in the training data
• —May not capture the most recent medical research or developments
• —Cannot perform physical examinations or medical tests

Recommendations

• —Always verify medical information with professional healthcare providers
• —Use the model as a supplementary information source
• —Be aware of potential inaccuracies or incomplete information

Training Details

Training Data

• —Source Dataset: ruslanmv/ai-medical-chatbot
• —Base Model: Meta Llama-3.2-3B-Instruct

Training Procedure

[Provide details about the fine-tuning process, if available]

• —Fine-tuning approach
• —Computational resources used
• —Training duration
• —Specific techniques applied during fine-tuning

How to Use the Model

Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Ellbendls/llama-3.2-3b-chat-doctor"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
input_text = "I had a surgery which ended up with some failures. What can I do to fix it?"

# Prepare inputs with explicit padding and attention mask
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)

# Generate response with more explicit parameters
outputs = model.generate(
    input_ids=inputs['input_ids'], 
    attention_mask=inputs['attention_mask'],
    max_new_tokens=150,  # Specify max new tokens to generate
    do_sample=True,      # Enable sampling for more diverse responses
    temperature=0.7,     # Control randomness of output
    top_p=0.9,           # Nucleus sampling to maintain quality
    num_return_sequences=1  # Number of generated sequences
)

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

print(response)

Ethical Considerations

This model is developed with the intent to provide helpful, accurate, and responsible medical information. Users are encouraged to:

• —Use the model responsibly
• —Understand its limitations
• —Seek professional medical advice for serious health concerns