Lbai-1-preview

Model Details

Model Description

Lbai-1-preview is a specialized medical language model designed to assist with medical and healthcare-related queries, providing informative responses based on medical knowledge.

This model has been trained using advanced machine learning techniques to create a standalone, fully-integrated language model optimized for medical domain applications.

• Developed by: Neurazum
• Model type: Causal Language Model (Medical Domain)
• Architecture: Transformer-based decoder model
• Language: English
• License: MIT
• Model size: ~5GB (float16)
• Parameters: 2.7B parameters

Intended Use

Direct Use

Lbai-1-preview is designed for:

1. Medical Question Answering: Providing informative responses to general medical questions
2. Medical Information Retrieval: Helping users understand medical concepts, symptoms, and conditions
3. Healthcare Education: Supporting medical students and healthcare professionals with educational content
4. Patient Education: Offering accessible explanations of medical terms and conditions
5. Clinical Documentation Assistance: Helping draft medical notes and summaries (with human oversight)
6. Medical Research Support: Assisting in literature review and medical knowledge synthesis

Recommended Use Cases

• Educational platforms for medical training
• Healthcare chatbots with appropriate disclaimers
• Medical information systems with human-in-the-loop verification
• Research assistants for medical literature analysis
• Clinical documentation support tools

Use with Supervision

IMPORTANT: This model should ALWAYS be used with appropriate human supervision, especially in:

• Clinical decision-making contexts
• Patient care situations
• Medication recommendations
• Diagnosis suggestions
• Treatment planning

Out-of-Scope Uses

Prohibited Uses

This model should NOT be used for:

1. Primary Clinical Diagnosis: Making medical diagnoses without professional medical oversight
2. Treatment Decisions: Making independent treatment or medication decisions
3. Emergency Medical Situations: Providing guidance in life-threatening or emergency situations
4. Replacing Healthcare Professionals: Substituting for qualified medical practitioners
5. Self-Medication Guidance: Providing medication recommendations without medical supervision
6. Critical Care Decisions: Making decisions in intensive care or critical medical situations
7. Legal Medical Advice: Providing legally binding medical opinions
8. Prescription Decisions: Determining medication dosages or prescriptions

High-Risk Scenarios

• Pediatric Care: Specialized medical care for children requires expert oversight
• Oncology: Cancer diagnosis and treatment planning
• Surgery Planning: Surgical procedures and planning
• Mental Health Crisis: Acute mental health emergencies
• Rare Diseases: Conditions requiring specialized expertise

Ethical Boundaries

• Generating fake medical records
• Impersonating healthcare professionals
• Providing medical advice in jurisdictions where AI cannot legally do so
• Using for insurance fraud or medical billing manipulation

Training Data

Dataset Information

Training Dataset Composition:

• Primary Source(s): Medical textbooks, PubMed articles, NCBI
• Dataset Size: 4.8M token
• Language(s): English and Turkish
• Domain Coverage: Neuroscience, Neurology

Example Dataset Types:

• Medical literature and textbooks
• Clinical guidelines and protocols
• Patient-doctor conversation datasets
• Medical question-answer pairs
• Clinical case studies
• Medical terminology databases

Evaluation Data

Test Dataset

Evaluation Dataset:

• Source: PubMed articles, NCBI
• Size: 500K token
• Split Ratio: 80% train, 10% validation, 10% test

Benchmark Results

Medical Knowledge Tests

Benchmark	Score
MedQA	36.53% (± 1.35%)
MedMCQA	35.16% (± 0.07%)
MMLU-Medical (Anatomy)	48.88% (± 0.43%)
MMLU-Medical (Clinical Knowledge)	59.62% (± 0.30%)
MMLU-Medical (College Biology)	58.33% (± 0.41%)
MMLU-Medical (College Medicine)	54.91% (± 0.37%)
MMLU-Medical (Medical Genetics)	61.00% (± 0.49%)
MMLU-Medical (Professional Medicine)	50.00% (± 0.30%)

General Language Tests

Benchmark	Score
MMLU (Overall)	54.26% (± 0.40%)
TruthfulQA	44.48% (± 1.52%)
HellaSwag	57.19% (± 0.49%)

Qualitative Analysis

Strengths:

• Strong performance on general medical questions
• Good understanding of medical terminology

Weaknesses:

• May struggle with rare diseases
• Limited multilingual support

Ethical Considerations

Medical AI Ethics

Patient Safety First: This model is designed as an assistive tool, not a replacement for healthcare professionals. All outputs should be:

• Verified by qualified medical professionals before clinical use
• Used only in appropriate contexts with proper oversight
• Accompanied by clear disclaimers about AI limitations

Bias and Fairness

Potential Biases:

• Dataset Bias: Medical literature may overrepresent certain populations or conditions
• Language Bias: Model may perform differently across languages or dialects
• Demographic Bias: Performance may vary across age groups, genders, or ethnicities
• Geographic Bias: Medical practices and terminology vary by region

Mitigation Efforts:

• Diverse evaluation datasets
• Regular bias audits
• Transparent reporting of limitations

Privacy and Data Protection

Training Data Privacy:

• All training data has been processed to remove personally identifiable information (PII)
• No patient health information (PHI) from the training data can be extracted from the model

User Privacy:

• Users should not input sensitive personal health information
• Conversations should be treated as non-confidential unless properly secured
• Organizations deploying this model must implement appropriate privacy safeguards

Transparency and Accountability

Model Limitations:

• This model can make mistakes and generate incorrect information
• Medical knowledge evolves; model may contain outdated information
• Model cannot access real-time medical databases or patient records
• Cannot perform physical examinations or diagnostic tests

Accountability:

• Responsibility for medical decisions remains with healthcare professionals
• Users should verify all information with qualified sources
• Feedback and error reporting mechanisms should be established

Societal Impact

Positive Impacts:

• Improved access to medical information
• Support for medical education
• Assistance in underserved regions (with appropriate oversight)
• Reduced burden on healthcare systems for routine inquiries

Potential Risks:

• Over-reliance on AI for medical decisions
• Spread of medical misinformation if used improperly
• Reduced human interaction in healthcare
• Unequal access based on digital divide

Recommendations:

• Always use with professional medical oversight
• Implement strong disclaimer systems
• Provide user education on AI limitations
• Ensure equitable access to both AI tools and human healthcare

Limitations

Technical Limitations

1. Knowledge Cutoff:

   • Training data has a cutoff date Oct-2025
   • May not include latest medical research and guidelines
   • Drug information and treatment protocols may be outdated

2. Context Window:

   • Maximum context length: 2048 tokens
   • Cannot process very long medical documents in a single pass
   • May lose context in extended conversations

3. Language Support:

   • May have reduced performance in non-primary languages
   • Medical terminology translation may be imperfect

4. Computational Requirements:

   • Requires ~5GB VRAM for inference (float16)
   • May be slow on CPU-only systems
   • Real-time applications may need optimization

Medical Knowledge Limitations

1. Scope of Knowledge:

   • Cannot access patient-specific data or medical records
   • No real-time drug interaction databases
   • Cannot perform calculations requiring patient-specific parameters

2. Diagnostic Limitations:

   • Cannot perform physical examinations
   • Cannot interpret medical images, lab results, or vital signs
   • Diagnosis requires comprehensive clinical assessment beyond text

3. Specialization:

   • May perform better in some medical specialties than others
   • Rare diseases and conditions may not be well-represented
   • Cutting-edge treatments may not be included

4. Clinical Reasoning:

   • Cannot replace clinical judgment and experience
   • May not consider all relevant factors in complex cases
   • Cannot account for individual patient circumstances

Accuracy and Reliability

1. Hallucination Risk:

   • Model may generate plausible-sounding but incorrect information
   • Medical facts should always be verified
   • Citations may be fabricated

2. Inconsistency:

   • May provide different answers to similar questions
   • Response quality may vary based on phrasing
   • Temperature settings affect consistency

3. Uncertainty Calibration:

   • Model may not accurately express uncertainty
   • Confident-sounding responses may be incorrect
   • No built-in confidence scores

Safety Limitations

1. Emergency Situations:

   • ABSOLUTELY NOT for medical emergencies
   • Cannot call emergency services
   • Cannot provide real-time critical care guidance

2. Medication Safety:

   • Cannot verify drug interactions for specific patients
   • Cannot determine appropriate dosages
   • Cannot account for allergies or contraindications

3. Mental Health:

   • Not designed for crisis intervention
   • Cannot provide ongoing mental health therapy
   • Should not replace mental health professionals

Regulatory and Legal Limitations

1. Not FDA Approved:

   • This model is not approved as a medical device
   • Not certified for clinical decision support
   • Not validated for regulatory compliance

2. Professional Liability:

   • Does not establish doctor-patient relationship
   • Users responsible for verifying all information
   • Not a substitute for professional medical advice

3. Geographic Limitations:

   • Medical practices vary by country and region
   • May not align with local medical standards
   • Regulatory status varies by jurisdiction

Mitigation Strategies

For Developers:

• Implement hallucination detection systems
• Add fact-checking layers
• Provide confidence scores when possible
• Regular model updates with new medical knowledge

For Users:

• Always verify critical information
• Use as a supplementary tool, not primary source
• Consult qualified healthcare professionals
• Report errors and inconsistencies

For Organizations:

• Implement human-in-the-loop systems
• Regular audits and quality checks
• Clear disclaimers and user warnings
• Appropriate training for staff using the model

How to Use

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_path = "Neurazum/Lbai-1-preview"  # Update with your actual path
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(model_path)

# Example usage
prompt = "Patient: I have a persistent headache and fever. What could be the cause?\nDoctor:"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

System Requirements

Minimum:

• GPU: 6GB VRAM (float16)
• RAM: 8GB
• Storage: 6GB

Recommended:

• GPU: 8GB+ VRAM
• RAM: 16GB+
• Storage: 10GB

Installation

pip install torch transformers accelerate

Medical Disclaimer

⚠️ IMPORTANT MEDICAL DISCLAIMER ⚠️

This AI model is provided for informational and educational purposes only and is NOT a substitute for professional medical advice, diagnosis, or treatment.

Key Points:

1. Not Medical Advice: The information provided by this model does not constitute medical advice and should not be relied upon for medical decisions.

2. Consult Healthcare Professionals: Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.

3. Emergency Situations: Never disregard professional medical advice or delay in seeking it because of something you have read or received from this AI model. If you think you may have a medical emergency, call your doctor or emergency services immediately.

4. No Doctor-Patient Relationship: Use of this model does not create a doctor-patient relationship between you and the developers or operators of this model.

5. Accuracy Not Guaranteed: While efforts have been made to ensure accuracy, medical information can change rapidly, and this model may contain errors or outdated information.

6. Individual Variation: Medical conditions and treatments vary greatly among individuals. Information that may be appropriate for one person may not be appropriate for another.

7. Verify Information: Always verify any medical information with qualified healthcare professionals and current medical literature.

By using this model, you acknowledge and agree to these limitations and disclaimers.

Citation

If you use this model in your research or applications, please cite:

@misc{neurazum,
   title = {Lbai-1-preview},
   author = {Neurazum AI Department},
   year = {2025},
   url = {https://huggingface.co/Neurazum/Lbai-1-preview},
}

Contact

• Organization: Neurazum
• Contact Email: contact@neurazum.com
• Website: neurazum.com

Updates and Versions

Version 1.0-preview (Current)

• Initial release
• Medical domain optimization

Planned Updates

• [×] Additional medical domain training
• [×] Multilingual support expansion
• [×] Safety improvements
• [×] Integration of bai-Mind models that interpret EEG signals
• [×] Reasoning ability

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Last Updated: 2025-11-25 Model Version: 1.0-preview Documentation Version: 1.0