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
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