QUICK_START.md

🚀 Quick Start Guide

Get Started in 3 Steps

1. Install Dependencies

pip install -r requirements.txt

2. Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("9x25dillon/LFM2-8B-A1B-Dimensional-Entanglement")
tokenizer = AutoTokenizer.from_pretrained("9x25dillon/LFM2-8B-A1B-Dimensional-Entanglement")

3. Generate with Dimensional Awareness

prompt = "Explain how consciousness emerges from quantum processes"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

🌌 Advanced Usage

Explore Dimensional Entanglements

import sqlite3

# Connect to the dimensional database
conn = sqlite3.connect("dimensional_entanglement.db")

# Find high-strength entanglements
cursor = conn.cursor()
cursor.execute("""
    SELECT n1.metadata, n2.metadata, e.strength 
    FROM entanglements e
    JOIN dimensional_nodes n1 ON e.node_i = n1.node_id
    JOIN dimensional_nodes n2 ON e.node_j = n2.node_id
    ORDER BY e.strength DESC LIMIT 5
""")

for concept1, concept2, strength in cursor.fetchall():
    print(f"{concept1} ↔ {concept2} (strength: {strength:.3f})")

Generate Training Data

from dimensional_entanglement_database import DimensionalDatabase, TrainingDataGenerator

# Load database and generate new training examples
db = DimensionalDatabase("dimensional_entanglement.db")
generator = TrainingDataGenerator(db)
examples = generator.generate_training_data(num_examples=100)

📊 What You Get

• 25 dimensional nodes across 5 domains
• 124 entanglement relationships
• Cross-dimensional reasoning capabilities
• Holographic memory integration
• Emergent pattern recognition

🔗 Repository Structure

├── README.md                              # Complete documentation
├── dimensional_entanglement_database.py   # Core framework
├── luimennua.md                          # Theoretical foundation (3,725 lines)
├── luimennua_llm_bridge.py               # Holographic memory bridge
├── dimensional_entanglement.db            # SQLite knowledge base
├── training_data_emergent.jsonl          # Generated training data
├── config_dimensional_entanglement.json  # Model configuration
├── example_usage.py                      # Usage examples
└── requirements.txt                      # Dependencies

🎯 Next Steps

1. Explore the database: Query entanglements and discover cross-domain connections
2. Generate more data: Add your own concepts and create training examples
3. Fine-tune: Use the training data to enhance your own models
4. Research: Apply to your specific domain (physics, biology, AI, etc.)

🌟 Key Features

• Quantum-inspired learning: Based on entanglement and superposition principles
• Multi-dimensional reasoning: Concepts connected across domains
• Emergent intelligence: Learns patterns that emerge from interactions
• Holographic processing: Each part contains information about the whole

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Ready to explore the dimensional entanglement framework? Start with the examples in example_usage.py!