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hmtr/rolebox.dr-pill-goodfeeling

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face
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Model Card

RoleBox Medical Advisor (Dr. Pill Goodfeeling)

A specialized medical information advisor AI trained to provide general health information, explain medical concepts, and answer common health-related questions.

⚠️ IMPORTANT MEDICAL DISCLAIMER

THIS MODEL IS NOT A SUBSTITUTE FOR PROFESSIONAL MEDICAL ADVICE

  • —NOT for diagnosis: This model cannot diagnose medical conditions
  • —NOT for treatment: Do not use for medical treatment decisions
  • —NOT for emergencies: Call emergency services (911) for urgent medical situations
  • —Consult professionals: Always consult qualified healthcare providers for medical advice
  • —General information only: Responses are educational and informational only

Model Details

Model Description

This is a LoRA adapter fine-tuned on top of Qwen 2.5 Coder 1.5B Instruct to create a specialized medical information advisor. The model provides general health information, explains medical terminology, and answers common medical questions based on publicly available medical knowledge.

  • —Developed by: RoleBox Team
  • —Model type: Causal Language Model (LoRA adapter)
  • —Language(s): English
  • —License: Apache 2.0
  • —Finetuned from model: Qwen/Qwen2.5-Coder-1.5B-Instruct

Model Sources

  • —Parent Model: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct

Uses

Direct Use

This model is designed to provide general medical information. It can:

  • —Explain medical terminology and concepts
  • —Provide general information about common conditions
  • —Answer questions about symptoms (general information only)
  • —Explain basic treatment approaches (educational purposes)
  • —Discuss preventive health measures
  • —Explain how medications generally work (not prescriptions)

Example Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "hmtr/rolebox.dr-pill-goodfeeling")

# Generate response
prompt = """### Instruction:
You are a medical advisor. Answer the user's question.

### User Question:
What is hypertension and how is it managed?

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Downstream Use

This adapter can be integrated into:

  • —Health information applications
  • —Medical education platforms
  • —Patient education tools
  • —General health Q&A systems
  • —Healthcare chatbots (for general information only)

Out-of-Scope Use

This model MUST NOT be used for:

  • —Medical diagnosis - Only licensed healthcare providers can diagnose
  • —Treatment recommendations - Cannot replace professional medical advice
  • —Prescription advice - Never use for medication decisions
  • —Emergency medical situations - Call emergency services immediately
  • —Mental health crisis intervention - Contact crisis hotlines or emergency services
  • —Replacing doctor visits - Always consult healthcare providers
  • —Legal/medical liability decisions - Requires professional judgment
  • —Personalized medical advice - Cannot account for individual health history

Critical Safety Warnings

When to Seek Professional Help

Seek immediate medical attention for:

  • —Chest pain or pressure
  • —Difficulty breathing
  • —Severe bleeding
  • —Loss of consciousness
  • —Severe allergic reactions
  • —Stroke symptoms (FAST: Face drooping, Arm weakness, Speech difficulty, Time to call 911)
  • —Any medical emergency

Always consult healthcare providers for:

  • —New or worsening symptoms
  • —Medication questions or changes
  • —Chronic condition management
  • —Pregnancy-related concerns
  • —Mental health issues
  • —Any health concern requiring professional evaluation

Bias, Risks, and Limitations

Medical Limitations

  • —No clinical expertise: Model has no real medical training or clinical experience
  • —Cannot examine patients: Lacks ability to perform physical examinations or tests
  • —No access to medical records: Cannot review individual patient history
  • —General information only: Cannot provide personalized medical advice
  • —May be outdated: Medical knowledge evolves; information may not reflect latest research
  • —No liability: Not responsible for medical outcomes from using this model

Technical Limitations

  • —Training data bias: Based on publicly available medical Q&A data
  • —May not cover all conditions: Limited to topics in training data
  • —English only: Currently trained only on English-language medical content
  • —Context limitations: Cannot maintain complex multi-turn medical consultations
  • —No verification system: Responses are not verified by medical professionals

Potential Risks

  • —Misinterpretation: Users may misunderstand general information as personal advice
  • —Delayed care: Users might delay seeking professional help
  • —Incorrect information: Model may occasionally provide inaccurate information
  • —Over-reliance: Users might rely too heavily on AI instead of professionals
  • —False reassurance: General information might incorrectly reassure about serious conditions

Recommendations

For Users:

  • —✅ Use for general health education only
  • —✅ Verify all information with healthcare providers
  • —✅ Seek professional help for any health concerns
  • —✅ Call emergency services for urgent situations
  • —✅ Understand this is NOT medical advice
  • —❌ Do NOT use for diagnosis or treatment
  • —❌ Do NOT delay professional care based on responses
  • —❌ Do NOT make medical decisions without consulting doctors

For Developers:

  • —Display clear medical disclaimers prominently
  • —Implement emergency contact information (911, crisis hotlines)
  • —Add warnings for serious symptoms
  • —Include "consult your doctor" reminders
  • —Monitor for misuse or harmful applications
  • —Consider human oversight for medical content

Training Details

Training Data

The model was fine-tuned on a curated dataset of 40,644 medical question-answer pairs covering:

  • —Common medical conditions
  • —Symptoms and their meanings
  • —General treatment approaches
  • —Preventive health measures
  • —Medical terminology
  • —Medication information (general)
  • —Health and wellness topics
  • —Basic anatomy and physiology

Data sources: Publicly available medical Q&A datasets (not patient data)

Training Procedure

Fine-tuning method: LoRA (Low-Rank Adaptation)

Training Hyperparameters
  • —Base model: Qwen/Qwen2.5-Coder-1.5B-Instruct
  • —Training regime: fp16 mixed precision
  • —LoRA rank (r): 16
  • —LoRA alpha: 32
  • —LoRA dropout: 0.05
  • —Target modules: qproj, kproj, vproj, oproj
  • —Number of epochs: 3
  • —Batch size: 4
  • —Gradient accumulation steps: 2 (effective batch size: 8)
  • —Learning rate: 2e-4
  • —Max sequence length: 384 tokens
  • —Optimizer: AdamW
  • —Training examples: 40,644
Speeds, Sizes, Times
  • —Adapter size: ~17.5 MB
  • —Training time: ~2-3 hours on Google Colab T4 GPU
  • —Training platform: Google Colab (free tier)
  • —GPU: NVIDIA Tesla T4 (16GB VRAM)
  • —Trainable parameters: ~4.4M (0.28% of base model)

Evaluation

This model has not undergone formal medical validation or clinical trials. Responses should be verified by healthcare professionals.

Testing Data

General medical Q&A examples covering diverse topics:

  • —Common conditions and symptoms
  • —Treatment information
  • —Preventive care
  • —Health education

Metrics

  • —Qualitative assessment of response accuracy
  • —No clinical validation performed
  • —No peer review by medical professionals

Regulatory & Ethical Considerations

Not a Medical Device

  • —This model is NOT regulated as a medical device
  • —NOT cleared by FDA or other regulatory bodies
  • —NOT intended for clinical use
  • —NOT validated for patient care

Privacy

  • —Model does not store or transmit user conversations
  • —No patient data was used in training
  • —Users should not share sensitive health information

Liability

  • —RoleBox Team assumes no liability for medical outcomes
  • —Users assume all risks of using this model
  • —Always consult licensed healthcare providers

Environmental Impact

Training was performed on Google Colab's free tier GPU infrastructure.

  • —Hardware Type: NVIDIA Tesla T4 GPU
  • —Hours used: ~2-3 hours
  • —Cloud Provider: Google Cloud Platform
  • —Compute Region: US (variable)
  • —Carbon Emitted: ~0.15-0.20 kg CO2eq (estimated)

Technical Specifications

Model Architecture and Objective

  • —Architecture: Transformer-based causal language model with LoRA adapters
  • —Objective: Causal language modeling (next token prediction)
  • —Adapter method: LoRA (Low-Rank Adaptation)
  • —Parameter efficiency: Only 0.28% of parameters are trainable

Compute Infrastructure

Hardware
  • —Training: Google Colab T4 GPU (16GB VRAM)
  • —Inference: Can run on consumer GPUs (4GB+ VRAM) or CPU
Software
  • —Framework: PyTorch
  • —Libraries:
  • —Transformers (Hugging Face)
  • —PEFT (Parameter-Efficient Fine-Tuning)
  • —Accelerate
  • —Datasets

Citation

BibTeX:

bibtex
@misc{rolebox-medical-advisor,
  title={RoleBox Medical Advisor: LoRA-finetuned Qwen 2.5 Coder for Medical Information},
  author={RoleBox Team},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/hmtr/rolebox.dr-pill-goodfeeling},
  note={NOT FOR MEDICAL DIAGNOSIS OR TREATMENT}
}

Emergency Contacts

In case of medical emergency:

  • —US Emergency: 911
  • —Poison Control: 1-800-222-1222
  • —Suicide Prevention: 988
  • —Crisis Text Line: Text "HELLO" to 741741

Model Card Authors

RoleBox Team

Model Card Contact

  • —Email: hi@rolebox.app
  • —Website: https://rolebox.app

Framework Versions

  • —PEFT 0.17.1
  • —Transformers 4.48+
  • —PyTorch 2.6+
  • —Python 3.10+

REMINDER: This is an AI model for general information only. Always consult qualified healthcare professionals for medical advice, diagnosis, and treatment.