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

python

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