Nano Banana Pro
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This repository contains a revolutionary training pipeline for **truth-leaning AI development** through **singular source consistency**, designed for H200 GPU deployment. This is **NOT a biblical AI** but demonstrates a breakthrough methodology where all training data comes from authors sharing a co
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This repository contains a revolutionary training pipeline for truth-leaning AI development through singular source consistency, designed for H200 GPU deployment. This is NOT a biblical AI but demonstrates a breakthrough methodology where all training data comes from authors sharing a consistent worldview, creating exceptionally low-noise training that enables efficient learning of coherent reasoning patterns.
MISCONCEPTION ALERT: This is NOT a biblical AI or religious system.
ACTUAL PURPOSE: Demonstrate that singular truth source consistency outperforms massive contradictory datasets in AI training.
Core Innovation: All 1,226 training files come from authors sharing a singular source of truth, creating:
Coherent Worldview Training: A Data Quality Approach to Language Model Development
REQUIRED FOR ALL AGENTS: This paper explains:
Purpose: Create the first truth-leaning AI through singular source consistency across 6 domains Architecture: Single Enhanced SIM-ONE model with governance mechanisms Target Hardware: NVIDIA H200 GPU (~24 hours training time) Training Data: 1,226 files from consistent worldview authors across 7 writing styles
Legacy Notice: MVLM-GPT2 is deprecated and will be removed in future versions.
SIM-ONE Training/enhanced_train.pymodels/simone_enhanced/Total Files: 1,226 across 6 major domains (114MB)
mvlm_comprehensive_dataset/ āāā biblical_classical/ (1,083 files) ā āāā classical_literature/ # 22 files (Shakespeare, Dickens, virtue works) ā āāā contemporary_biblical/ # Modern truth-aligned exposition ā āāā historical_biblical/ # Classical theological works ā āāā virtue_character/ # Character-focused literature ā āāā bible/ # 24 files (classical biblical authors) ā āāā intouch_articles_dataset/ # 971 files (contemporary teaching) āāā educational/ (28 files) ā āāā history_social/ # Historical and social content ā āāā language_communication/ # Communication and language arts ā āāā philosophy_ethics/ # Philosophical and ethical works āāā gty_sermons/ (73 files) ā āāā Deep theological exposition and reasoning āāā historical_scientific/ (24 files) ā āāā foundational_documents/ # Historical foundational texts ā āāā scientific_principles/ # Scientific reasoning and principles ā āāā wisdom_literature/ # Classical wisdom texts āāā philosophical/ (16 files) ā āāā classical_philosophy/ # Ancient philosophical works ā āāā medieval_philosophy/ # Medieval philosophical texts ā āāā modern_philosophy/ # Modern philosophical reasoning āāā technical/ (2 files) āāā programming_software/ # Enterprise Application Architecture āāā scientific_mathematical/ # Principles of Chemistry
SIM-ONE Training/ āāā prioritary_mvlm/ ā āāā enhanced_trainer.py # H200-optimized trainer with early stopping ā āāā advanced_tokenizer.py # Truth-aligned BPETokenizer (32K vocab) ā āāā advanced_losses.py # Advanced training loss functions ā āāā config.py # Configuration āāā simone_transformer/ ā āāā enhanced_model.py # EnhancedSIMONEModel ā āāā rope_attention.py # RoPE + governance ā āāā modern_layers.py # SwiGLU, RMSNorm, etc. āāā train.py # Simple trainer entry point āāā enhanced_train.py # Advanced trainer with CLI
mvlm_training_dataset_complete/ from rootPrioritaryConfig with modern defaultsBiblicalBPETokenizer (32K vocab) over character-levelEnhancedGovernanceAttention with RoPE encodingSwiGLU layers instead of standard MLPRMSNorm preferred over LayerNormtrain_all_models.py for sequential trainingsetup_environment.sh firstvalidate_models.py after trainingmodels_for_download/# Correct imports for Enhanced SIM-ONE from simone_transformer import EnhancedSIMONEModel from prioritary_mvlm import EnhancedPrioritaryTrainer, AdvancedBPETokenizer from prioritary_mvlm.advanced_losses import ComprehensiveTrainingLoss # Dataset path from SIM-ONE Training directory data_dir = "../mvlm_training_dataset_complete"
PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512PrioritaryConfig: Modern configuration with sensible defaultsenhanced_train.pylogs/h200_training_*.logsetup_environment.sh (installs all dependencies)train_all_models.py (automated pipeline)validate_models.py (verify models)models_for_download/This repository proves a fundamental shift in AI development:
Traditional Approach:
SIM-ONE Approach:
mvlm_training_dataset_complete/mvlm_comprehensive_dataset/# 1. Clone repository to H200 droplet git clone <repository-url> cd <repository-directory> # 2. Setup virtual environment and dependencies python3 -m venv venv source venv/bin/activate pip install --upgrade pip pip install -r requirements.txt # 3. Verify complete configuration python3 verify_complete_setup.py # 4. Start web monitoring dashboard (optional but recommended) python3 training_monitor.py & # Access at localhost:5001 # 5. Train Enhanced SIM-ONE across ALL 6 domains (~24 hours) python3 train_all_models.py # 6. Validate trained model (5 minutes) python3 validate_models.py # 7. Download compressed model ls models_for_download/ # Download: simone_enhanced_model.tar.gz
# Start Flask-based monitoring dashboard python3 training_monitor.py & # Access at: http://localhost:5001 # Features: # - Real-time training progress visualization # - GPU memory and utilization charts # - System resource monitoring # - Live training logs with auto-refresh # - Progress bars for epochs and steps
# Training progress tail -f logs/simone_enhanced_training.log # GPU utilization nvidia-smi -l 1 # Early stopping indicators # Look for: "š¾ New best model saved!" or "š Early stopping triggered!"
# Manual training with all parameters cd "SIM-ONE Training" python3 enhanced_train.py \ --data_dir ../mvlm_training_dataset_complete \ --output_dir ../models/simone_enhanced \ --vocab_size 32000 \ --hidden_dim 768 \ --num_layers 12 \ --batch_size 12 \ --gradient_accumulation_steps 4 \ --learning_rate 3e-4 \ --num_epochs 7 \ --patience 2 \ --min_epochs 6 # Configuration testing python3 test_training_config.py # Environment verification python3 verify_complete_setup.py
This repository demonstrates that:
Impact: First proof-of-concept that truth-leaning AI can be achieved through dataset consistency rather than explicit programming, opening new pathways for aligned AI development across any consistent worldview system.
This repository represents a breakthrough in AI training methodology through singular truth source consistency. All agents working with this codebase should understand:
Key Achievement: Proves that consistency beats scale in AI development, opening new pathways for efficient, aligned AI systems.