Brain Map AI v3.0 — Clinical Intelligence Model

Brain Map AI is a specialized medical reasoning model optimized for clinical decision support, USMLE-style reasoning, and bilingual (English/Arabic) medical tasks.

🚀 Deployment Status

The model is officially live on the gijl/ai repository. It features a technical stack including PyTorch, Hugging Face Transformers, and a specialized GGUF compatibility layer.

Key Features

• Architecture: Specialized Transformer with Grouped Query Attention (GQA) and Sparse Mixture of Experts (MoE) thinking blocks.
• Memory: Adaptive clinical memory using secure JSON/Safetensors storage (migrated from pickle for maximum security).
• Safety: Built-in Sovereign Critic and Drug Dosage Validation layers.
• Compatibility: Cross-compatible with Hugging Face Transformers and GGUF (via llama.cpp/LM Studio).

Technical Specifications

• Core: PyTorch 2.x / Transformers 5.x
• Precision: F16 (4-bit/8-bit quantization recommended for consumer VRAM optimization).
• Tokenizer: Custom BPE (8k-32k vocab) supporting medical nomenclature in EN/AR.

Usage

1. Using the Universal Loader (Recommended)

We provide a loader.py script for seamless integration across formats:

from loader import load_brain_map

model = load_brain_map('gijl/ai')

2. Standard Transformers Loading

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('gijl/ai')
model = AutoModelForCausalLM.from_pretrained('gijl/ai', trust_remote_code=True)

Unresolved Tasks & Next Steps

• VRAM Optimization: Further quantization (GGUF Q4_K_M) for mobile deployments.
• Extended Evaluation: Continued benchmarking against MedHALT and USMLE Step 2 vignettes.

Disclaimer: This model is for clinical research and decision-support assistance only. It is not a replacement for professional medical advice.