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metadata 
library_name: transformers
tags: []

πŸ₯ Sovereign Medical Reasoning Engine (SMRE) v3.0

🌟 Introduction

The Sovereign Medical Reasoning Engine is a high-performance, domain-specific AI model designed for clinical decision support. Unlike standard models, SMRE is built using Pure Python & NumPy, ensuring total sovereignty over the reasoning logic and zero reliance on external AI frameworks like PyTorch or TensorFlow.

πŸ›  Technical Specifications

  • Architecture: Multi-Modal Transformer (Text + Vision + Time-Series).
  • Parameters: ~1.6 Million learnable parameters.
  • Optimization: Adam Optimizer with Cosine Learning Rate Decay.
  • Tokenizer: Custom Bilingual (EN/AR) word-level tokenizer with clinical compound recognition.

🌍 Bilingual Capabilities

The engine treats English and Arabic medical terms as semantically equivalent within the embedding space.

  • Example: Myocardial Infarction and Ψ§Ψ­ΨͺΨ΄Ψ§Ψ‘ ΨΉΨΆΩ„Ψ© Ψ§Ω„Ω‚Ω„Ψ¨ share the same conceptual grounding.

πŸ“‚ Project Structure

  1. sovereign_medical_engine.pkl: Model weights and architectural metadata.
  2. my_medical_ai_bilingual.pkl: Vocabulary and tokenization mappings.
  3. config.json: Technical accreditation and configuration file.
  4. inference.py: Optimized script for running the model in production environments.

πŸš€ Quick Start (Inference) 

import numpy as np
from inference import ClinicalReasoningEngine, BilingualMedicalTokenizer

# 1. Initialize Tokenizer
tok = BilingualMedicalTokenizer('my_medical_ai_bilingual.pkl')

# 2. Load the Sovereign Engine
engine = ClinicalReasoningEngine.load('sovereign_medical_engine.pkl', tok)

# 3. Generate Medical Reasoning
prompt = "Diagnosis for sudden chest pain and high blood pressure:"
response = engine.generate(prompt, temperature=0.7, mode='nucleus')
print(f"SMRE Output: {response}")

βš–οΈ Legal & Medical Disclaimer

This model is intended for educational and research purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. For official clinical use, external regulatory validation is required.