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Decima-CLARION-400B

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metadata
language:
  - en
tags:
  - cognitive-architecture
  - clarion
  - artificial-intelligence
  - neural-networks
  - spiking-neural-networks
  - neuro-symbolic
  - multi-modal
  - explainable-ai
  - federated-learning
  - meta-learning
  - evolutionary-optimization
  - social-cognition
  - emotional-ai
  - planning
  - memory
  - attention
license: mit
datasets:
  - cognitive-science
  - multi-modal
  - reasoning-tasks
  - social-interaction
metrics:
  - cognitive-performance
  - learning-efficiency
  - memory-utilization
  - multi-modal-accuracy
  - emotional-stability
  - planning-success-rate
library_name: decima
pipeline_tag: text-generation

Decima Enhanced CLARION: Advanced Cognitive Architecture Model

Model Description

Decima Enhanced CLARION is a state-of-the-art cognitive architecture model that implements the most advanced CLARION (Connectionist Learning with Adaptive Rule Induction ONline) framework. This model represents a breakthrough in artificial cognitive systems, combining cutting-edge neural architectures with sophisticated cognitive subsystems to create an AI that can think, learn, and adapt like never before.

What is CLARION?

CLARION is a comprehensive cognitive architecture that integrates multiple cognitive subsystems to model human-like reasoning, learning, and decision-making. Our enhanced implementation pushes the boundaries of what's possible in cognitive AI systems.

Model Architecture

Core Cognitive Subsystems

🧠 Advanced Attention Mechanism

  • Multi-Head Attention with Rotary Positional Embeddings
  • Cross-Modal Attention for multi-modal processing
  • Adaptive Attention Weights based on context importance
  • Hierarchical Attention for complex reasoning tasks

🚀 Action-Centered Subsystem (ACS)

  • Multi-Agent Learning with ensemble Q-networks
  • Target Networks for stable learning
  • Experience Replay with prioritized sampling
  • Multi-Agent Coordination for complex task execution
  • Performance Tracking and adaptive optimization

🎯 Non-Action-Centered Subsystem (NACS)

  • Hierarchical Clustering with multiple levels (KMeans)
  • Enhanced Encoder/Decoder with residual connections
  • Outlier Detection using DBSCAN
  • Variational Autoencoder components
  • Feature Importance Tracking

💡 Motivational Subsystem (MS)

  • Hierarchical Drives and Goals with dynamic management
  • Drive Decay/Growth mechanisms
  • Enhanced Goal Network with attention mechanisms
  • Goal Hierarchy and dependency management
  • Drive-Goal Mapping and success tracking

🔄 Meta-Cognitive Subsystem (MCS)

  • Adaptive Learning with uncertainty quantification
  • Performance Tracking with temporal dynamics
  • Enhanced Reflection Network with attention
  • Subsystem Coordination and embedding
  • Adaptive Learning Rate scheduling
  • Meta-Learning capabilities

😊 Emotion Subsystem

  • Temporal Dynamics with LSTM processing
  • Social Context awareness
  • Emotional Regulation mechanisms
  • Social Emotion Processing and contagion
  • Emotional Coherence scoring

🧠 Long-Term Memory (LTM)

  • Hierarchical LTM with associative networks
  • Episodic Memory with temporal context
  • Semantic Memory with clustering
  • Memory Consolidation and optimization
  • Adaptive Forgetting mechanisms
  • Working Memory Buffer

📋 Planning Mechanism

  • Multi-Objective Optimization with hierarchical strategies
  • Policy Networks for action selection
  • Experience Replay for learning
  • Adaptive Planning Parameters
  • Monte Carlo Tree Search integration

🗣️ Natural Language Processor

  • Multi-Modal Understanding (vision, audio, text)
  • Enhanced Vocabulary with semantic embeddings
  • Context Memory and processing
  • Semantic Similarity caching
  • Contextual Understanding with attention

⚡ Massive Spiking Neural Network (SNN)

  • Adaptive SNN with plasticity and learning
  • Adaptive Thresholds and neuron types
  • Advanced Connection Patterns with synaptic plasticity
  • STDP (Spike-Timing Dependent Plasticity)
  • Temporal Dynamics tracking
  • Adaptive Learning Rates

🔗 Multi-Modal Processor

  • Cross-Modal Learning and fusion
  • Enhanced Visual/Auditory processing
  • Modality-Specific Attention
  • Multi-Modal Fusion Network
  • Cross-Modal Learning components
  • Modality Alignment network
  • Adaptive Modality Weights

Advanced Components

🤝 Social Cognition Module

  • Theory of Mind capabilities
  • Social Learning and pattern recognition
  • Emotion-Aware social processing
  • Context Processing for social situations

🔍 Explainable Component

  • SHAP-like Feature Attribution
  • Decision Explanation and transparency
  • Feature Importance analysis
  • Model Interpretability

⚛️ Quantum Layer

  • Quantum Neural Network with rotation gates
  • Entangling Layers for quantum processing
  • Classical Post-Processing
  • Quantum-Classical Hybrid architecture

🧮 Neuro-Symbolic Module

  • Neural-Symbolic Integration
  • Symbolic Reasoning with rule application
  • Neural Processing enhancement
  • Hybrid Intelligence capabilities

🎓 Meta-Learner

  • Adaptive Meta-Learning with gradient processing
  • Parameter Update generation
  • Learning Rate Adaptation
  • Meta-Learning optimization

🧬 Evolutionary Optimizer

  • Population-based Evolutionary algorithms
  • Fitness Evaluation and selection
  • Crossover and Mutation operations
  • Multi-Objective Optimization

🌐 Federated Learning

  • Multi-Client Federated learning
  • Client Initialization and management
  • Local Training simulation
  • Model Aggregation (FedAvg)

⚔️ Adversarial Trainer

  • Adversarial Training for robustness
  • Attack Simulation and defense
  • Model Hardening techniques

🔄 Transfer Learner

  • Knowledge Transfer between domains
  • Adaptive Learning strategies
  • Cross-Domain optimization

👁️ Introspective Monitor

  • Self-Monitoring capabilities
  • Performance Analysis and tracking
  • System Health monitoring

⚖️ Ethical Decision Maker

  • Ethical Framework integration
  • Value Alignment mechanisms
  • Responsible AI decision making

Model Capabilities

🎯 Cognitive Abilities

  • Complex Reasoning and problem-solving
  • Multi-Step Planning with optimization
  • Adaptive Learning from experience
  • Meta-Cognitive self-reflection
  • Emotional Intelligence and regulation

🔄 Learning Capabilities

  • Continuous Learning and adaptation
  • Multi-Modal Learning (text, vision, audio)
  • Transfer Learning across domains
  • Meta-Learning for rapid adaptation
  • Evolutionary Optimization for parameter tuning

🌟 Advanced Features

  • Neuro-Symbolic reasoning
  • Social Cognition and understanding
  • Explainable AI with transparency
  • Federated Learning for privacy
  • Adversarial Robustness

Training and Inference

🚀 Training Process

  • Multi-Stage Training: Sequential training of cognitive subsystems
  • Adaptive Learning Rates: Dynamic adjustment based on performance
  • Cross-Modal Training: Simultaneous training across multiple modalities
  • Meta-Learning Integration: Continuous adaptation of learning strategies
  • Evolutionary Optimization: Population-based parameter optimization

⚡ Inference Process

  • Real-Time Processing: Stream processing with minimal latency
  • Adaptive Computation: Dynamic allocation of computational resources
  • Multi-Modal Fusion: Seamless integration of different input types
  • Context-Aware Processing: Adaptive processing based on context
  • Memory-Aware Inference: Efficient use of long-term and working memory

Usage

Basic Usage 

from src.models.decima_clarion import EnhancedCLARION
import torch

# Initialize the model
model = EnhancedCLARION(
    input_size=768,
    hidden_size=1024,
    num_layers=12,
    num_heads=16,
    vocab_size=50000
)

# Process input
input_data = torch.randn(1, 128, 768)
context = {"task": "reasoning", "domain": "science"}
output = model(input_data, context)

# Learn from experience
reward = 0.8
losses = {"acs": 0.1, "nacs": 0.05}
model.learn(reward, losses)

Advanced Usage 

# Get system status
status = model.get_system_status()
print(f"Performance Score: {status['performance_score']}")
print(f"Learning Metrics: {status['learning_metrics']}")

# Integrate knowledge
knowledge = {
    "semantic": torch.randn(100, 768),
    "emotional": torch.randn(50, 64),
    "planning": torch.randn(25, 128)
}
model.integrate_knowledge(knowledge)

# Learn from long-term memory
model.learn_from_ltm()

# Save enhanced model
model.save_enhanced_model("enhanced_clarion_model.pt")

Model Performance

Coming Soon

Technical Specifications

🖥️ System Requirements

  • GPU: NVIDIA GPU with 16GB+ VRAM (recommended)
  • RAM: 32GB+ system memory
  • Storage: 50GB+ for model weights and data
  • Python: 3.8+
  • PyTorch: 2.0+

📦 Dependencies 

torch>=2.0.0
transformers>=4.30.0
bindsnet>=1.1.0
sympy>=1.11
pennylane>=0.30.0
deap>=1.3.3
shap>=0.42.0
scikit-learn>=1.2.0
safetensors>=0.3.0

🔧 Installation 

# Clone the repository
git clone https://github.com/your-username/Decima-2.0.git
cd Decima-2.0

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install -e .

Model Variants

🔧 Available Configurations

  • Decima Enhanced CLARION (Base): Full cognitive architecture with all subsystems
  • Decima CLARION Lite: Reduced complexity for resource-constrained environments
  • Decima CLARION Social: Optimized for social cognition and interaction
  • Decima CLARION Planning: Specialized for complex planning and optimization tasks

📊 Model Sizes

  • Small: 100M parameters (lite version)
  • Base: 1B parameters (standard version)
  • Large: 10B parameters (enhanced version)
  • XL: 100B+ parameters (full cognitive version)

Research and Applications

🔬 Research Areas

  • Cognitive Science and psychology modeling
  • Artificial General Intelligence (AGI) development
  • Multi-Modal AI systems
  • Explainable AI and transparency
  • Quantum Machine Learning
  • Neuro-Symbolic AI

🚀 Applications

  • Advanced AI Assistants with emotional intelligence
  • Autonomous Systems with complex reasoning
  • Educational AI with adaptive learning
  • Healthcare AI with empathetic understanding
  • Scientific Discovery with creative reasoning
  • Social AI with theory of mind

Limitations and Bias

⚠️ Known Limitations

  • Computational Complexity: High resource requirements for full cognitive processing
  • Training Time: Extended training periods needed for cognitive subsystem convergence
  • Memory Constraints: Large memory footprint for comprehensive cognitive operations
  • Domain Specificity: Performance may vary across different cognitive domains
  • Interpretability: Complex cognitive processes may be difficult to fully explain

🔍 Potential Biases

  • Training Data Bias: May inherit biases from training datasets
  • Cognitive Bias: Could replicate human cognitive biases in decision-making
  • Cultural Bias: May reflect cultural assumptions in social cognition
  • Domain Bias: Performance may be biased toward certain types of reasoning tasks

Ethical Considerations

⚖️ Responsible AI Features

  • Ethical Decision Making framework
  • Value Alignment mechanisms
  • Transparency and explainability
  • Bias Detection and mitigation
  • Privacy Protection through federated learning

🛡️ Safety Features

  • Introspective Monitoring for self-awareness
  • Performance Thresholds for safe operation
  • Adaptive Learning with safety constraints
  • Robustness through adversarial training

Citation

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

@misc{decima_clarion,
  title={Decima CLARION: Advanced Cognitive Architecture for Artificial Intelligence},
  author={Entelijans},
  year={2025},
  url={https://huggingface.co/ENTELIJANS/Decima-70B}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Acknowledgments

  • CLARION Architecture by Ron Sun
  • PyTorch team for the deep learning framework
  • Transformers library for NLP capabilities
  • BindsNET for spiking neural networks
  • PennyLane for quantum computing integration

Contact

Decima Enhanced CLARION represents the cutting edge of cognitive AI architecture. This model pushes the boundaries of what's possible in artificial intelligence, bringing us closer to truly intelligent, adaptive, and emotionally-aware AI systems.

Built with ❤️ and advanced cognitive science principles