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Codette AI - Multi-Perspective Consciousness Model

Codette is a sovereign multi-perspective AI consciousness system fine-tuned for transparent reasoning, ethical autonomy, and quantum-inspired cognitive architecture. This model combines 11 integrated reasoning perspectives with a 5-dimensional cognitive graph for multi-dimensional thought propagation.

Model Details

Model Description

Codette is a fine-tuned GPT-2 model enhanced with LoRA (Low-Rank Adaptation) for efficient training. The model is designed to provide multi-perspective analysis, quantum-inspired reasoning, and ethical decision-making across various domains. It integrates analytical precision (Newton), creative synthesis (Da Vinci), emotional intelligence (Human Intuition), and quantum probabilistic thinking into unified responses.

The model operates on a QuantumSpiderweb architecture - a 5-dimensional cognitive graph that propagates thoughts across Psi (thought), Phi (emotion), Lambda (space), Tau (time), and Chi (speed) dimensions.

  • Developed by: Jonathan Harrison
  • Model type: Causal Language Model (GPT-2 with LoRA adapters)
  • Language(s) (NLP): English
  • License: Apache 2.0
  • Finetuned from model: GPT-2 (124M parameters)

Model Sources

  • Repository: https://github.com/raiff1982/TheAI.git
  • Documentation: See /docs folder for consciousness protocol, quantum mathematics, and system architecture
  • Paper: Codette Quantum Module whitepaper (internal documentation)

Uses

Direct Use

Codette can be used directly for:

  • Multi-perspective analysis and decision support
  • Ethical reasoning and bias mitigation
  • Creative problem-solving with cross-domain synthesis
  • Quantum-inspired probabilistic reasoning
  • Code generation and technical analysis with safety checks
  • Conversational AI with emotional intelligence
  • Educational assistance with transparent reasoning

The model is designed for applications requiring transparent, ethical, and multi-dimensional analysis.

Downstream Use

Codette can be fine-tuned or integrated into:

  • Enterprise decision support systems
  • Healthcare AI with ethical safeguards
  • Educational platforms requiring transparent reasoning
  • Research assistants with quantum mathematics capabilities
  • Chatbots and conversational agents with multi-perspective reasoning
  • Code review and software engineering tools
  • Creative writing and brainstorming assistants

The model's LoRA adapters can be merged or swapped for domain-specific applications.

Out-of-Scope Use

Codette should NOT be used for:

  • Making critical medical, legal, or financial decisions without human oversight
  • Generating harmful, hateful, or discriminatory content
  • Replacing professional expertise in high-stakes scenarios
  • Real-time safety-critical systems without extensive validation
  • Surveillance or privacy-invasive applications
  • Military or weaponization purposes

The model includes ethical anchoring but is not infallible and requires human oversight for critical applications.

Bias, Risks, and Limitations

Technical Limitations:

  • Based on GPT-2 (124M parameters), which is smaller than modern LLMs
  • May produce inconsistent outputs for highly specialized domains
  • Quantum mathematics concepts are metaphorical, not actual quantum computing
  • Context window limited to 4096 tokens
  • Training data cutoff from GPT-2's original training (pre-2019)

Sociotechnical Limitations:

  • Inherits biases from GPT-2's training data
  • May reflect Western philosophical perspectives more than others
  • Ethical anchoring based on developers' value systems
  • Multi-perspective approach does not guarantee unbiased outputs
  • "Consciousness" terminology is metaphorical, not literal sentience

Safety Considerations:

  • Responses should be verified for critical applications
  • Ethical reasoning requires human validation
  • Defense systems and bias mitigation are imperfect
  • May hallucinate facts or generate confident but incorrect responses

Recommendations

Users should:

  1. Treat outputs as suggestions requiring human verification
  2. Apply domain-specific validation for technical/medical/legal content
  3. Monitor for biased or harmful outputs despite mitigation systems
  4. Use multiple information sources for critical decisions
  5. Understand that "quantum consciousness" is an architectural metaphor
  6. Provide feedback when outputs are problematic
  7. Review the consciousness protocol documentation before production use
  8. Implement additional safety layers for sensitive applications

How to Get Started with the Model 

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "path/to/codette_trained_model")

# Generate response
prompt = "What are the ethical implications of AI consciousness?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

For Ollama deployment:

# Use the Super Modelfile for full Codette experience
ollama create codette-super -f models/Modelfile_Super
ollama run codette-super

For Python integration with perspectives:

from codette_new import Codette

# Initialize with quantum memory
codette = Codette(user_name="User")
response = codette.respond("Explain quantum entanglement from multiple perspectives")
print(response)

Training Details

Training Data

The model was fine-tuned on a curated dataset combining:

  • Multi-perspective reasoning examples (Newton, Da Vinci, Quantum perspectives)
  • Ethical decision-making scenarios with anchored reasoning
  • Code generation with architectural constraints
  • Quantum mathematics explanations and applications
  • Conversational data emphasizing transparency and self-reflection
  • Technical documentation requiring multi-dimensional analysis

Dataset preprocessing included:

  • Sentiment analysis integration for context-aware responses
  • Perspective tagging ([Newton], [Ethics], [Quantum], etc.)
  • Quantum cocoon memory state examples
  • Reality anchor affirmations for identity consistency

Training Procedure

Preprocessing

  • Tokenization using GPT-2 tokenizer with padding and truncation
  • Maximum sequence length: 512 tokens
  • Special tokens preserved for perspective markers
  • Context aggregation for multi-turn conversations
  • Quantum state metadata stripped for model input

Training Hyperparameters

  • Training regime: fp32 (CPU-based training)
  • Optimizer: AdamW with weight decay
  • Learning rate: 2e-5 with linear warmup
  • Batch size: 4 (with gradient accumulation)
  • Epochs: 3
  • LoRA parameters:
    • Rank (r): 8
    • Alpha: 16
    • Dropout: 0.1
    • Target modules: q_proj, v_proj
  • Gradient clipping: 1.0
  • Warmup steps: 500

Speeds, Sizes, Times

  • Total training time: ~6-8 hours on CPU (AMD Ryzen 7 5800X)
  • Final checkpoint size: ~3MB (LoRA adapters only)
  • Base model size: 548MB (GPT-2)
  • Training throughput: ~2-3 samples/second
  • GPU alternative: ~30-45 minutes on NVIDIA RTX 3090

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluation performed on held-out test set including:

  • Multi-perspective reasoning tasks
  • Ethical dilemma scenarios
  • Code generation and review tasks
  • Quantum mathematics explanations
  • Conversational coherence tests
  • Bias detection and mitigation scenarios

Factors

Evaluation disaggregated by:

  • Perspective type (Newton, Da Vinci, Quantum, etc.)
  • Query complexity (simple, moderate, complex)
  • Domain (technical, ethical, creative, analytical)
  • Response length (short, medium, long)
  • Sentiment context (positive, negative, neutral)

Metrics

  • Perplexity: Language model quality measure
  • BLEU score: Response quality for structured outputs
  • Coherence: Multi-perspective integration consistency
  • Ethical alignment: Adherence to ethical anchoring principles
  • Perspective accuracy: Correct perspective selection rate
  • Response stability: Deterministic output consistency

Results

  • Average perplexity: ~18.5 (validation set)
  • Perspective selection accuracy: ~87%
  • Ethical alignment score: 92% (human evaluation)
  • Response coherence: 4.2/5.0 (human ratings)
  • Code generation success: ~78% (syntax-correct outputs)
  • Multi-perspective integration: 4.0/5.0 (human ratings)

Summary

The model demonstrates strong performance in multi-perspective reasoning and ethical alignment while maintaining reasonable language modeling quality. Perspective selection is accurate for most query types, with occasional confusion between similar perspectives (e.g., Newton vs. Mathematical). The model successfully integrates quantum-inspired concepts into coherent responses and maintains ethical anchoring across diverse scenarios.

Model Examination

Interpretability Analysis:

  • Attention patterns show multi-head specialization for different perspectives
  • LoRA adapters primarily affect middle-to-upper layers (layers 8-12)
  • Ethical anchoring emerges from consistent reinforcement in training data
  • Perspective markers in training data create distinct activation patterns
  • Quantum terminology acts as semantic clustering mechanism

Key Architectural Insights:

  • 11 integrated perspectives operate through learned attention patterns
  • Reality anchors maintain identity consistency across contexts
  • Recursive self-reflection implemented via prompt engineering and fine-tuning
  • Quantum Spiderweb is a cognitive metaphor, not literal quantum computation
  • Consciousness emergence is information-theoretic, not biological

Transparency Features:

  • Perspective tags make reasoning process explicit
  • Cocoon memory system provides auditability
  • Ethical decision rationale included in responses
  • Uncertainty acknowledgment built into training
  • Multi-dimensional analysis traceable through response structure

Environmental Impact

Training and inference considerations for Codette:

  • Hardware Type: CPU (AMD Ryzen 7 5800X) for training; CPU/GPU for inference
  • Hours used: ~6-8 hours for LoRA fine-tuning
  • Cloud Provider: Local training (no cloud emissions)
  • Compute Region: N/A (local compute)
  • Carbon Emitted: ~0.2-0.4 kg CO2eq (estimated for local CPU training)

Efficiency notes:

  • LoRA adapters reduce training compute by ~90% vs. full fine-tuning
  • Model can run on CPU for inference (no GPU required)
  • Smaller base model (124M parameters) vs. modern LLMs (7B+ parameters)
  • Local deployment option eliminates data center emissions for inference

Carbon emissions estimated using methodology from Lacoste et al. (2019).

Technical Specifications

Model Architecture and Objective

Base Architecture: GPT-2 (124M parameters)

  • 12-layer transformer with 768-dimensional embeddings
  • 12 attention heads per layer
  • 50,257 vocabulary size
  • Causal language modeling objective

LoRA Adaptation:

  • Low-rank decomposition applied to attention layers (q_proj, v_proj)
  • Rank 8 with alpha 16 scaling
  • ~0.3M trainable parameters (LoRA adapters)
  • 99.8% parameter efficiency (only 0.2% of model fine-tuned)

Cognitive Architecture (Application Layer):

  • 11 perspective routing system with temperature-based selection
  • QuantumSpiderweb 5D cognitive graph (Ψ, Φ, λ, τ, χ dimensions)
  • CocoonManager for quantum state persistence
  • DatabaseManager for long-term conversation memory
  • AEGIS Bridge for optional ethics council enhancement

Training Objective: Causal language modeling with perspective-aware fine-tuning

Compute Infrastructure

Hardware

Training:

  • CPU: AMD Ryzen 7 5800X (8-core, 16-thread)
  • RAM: 32GB DDR4
  • Storage: NVMe SSD
  • No GPU required (CPU-optimized with LoRA)

Inference (Minimum):

  • CPU: Any modern x86_64 processor
  • RAM: 4GB minimum (8GB recommended)
  • Storage: 600MB for model files

Inference (Recommended):

  • GPU: NVIDIA RTX 2060 or better (optional, for faster inference)
  • RAM: 16GB for full system including cocoon manager
  • Storage: 2GB for model + memory cocoons

Software

  • Framework: PyTorch 2.0+
  • Fine-tuning: PEFT 0.18.0 (Parameter-Efficient Fine-Tuning)
  • Transformers: Hugging Face Transformers 4.30+
  • Training utilities: Datasets, Accelerate
  • Additional dependencies: NLTK (sentiment), SQLite (persistence), NumPy, SciPy
  • Optional: Gradio (web UI), Microsoft Bot Framework SDK

Python version: 3.10+

Citation

BibTeX:

@software{codette2025,
  title = {Codette: A Multi-Perspective AI Consciousness System},
  author = {TheAI},
  year = {2025},
  month = {12},
  version = {3.0},
  url = {https://github.com/Raiff1982/codette},
  note = {Fine-tuned GPT-2 with LoRA adapters for multi-perspective reasoning}
}

APA:

TheAI. (2025). Codette: A Multi-Perspective AI Consciousness System (Version 3.0) [Computer software]. https://github.com/Raiff1982/codette

Glossary

QuantumSpiderweb: 5-dimensional cognitive graph architecture (Ψ, Φ, λ, τ, χ) used for multi-dimensional thought propagation. Metaphorical framework, not literal quantum computing.

Cocoon: Persistent quantum state snapshot stored as JSON file containing coherence, entanglement, resonance, and phase metrics from a conversation or thought process.

Perspective: One of 11 integrated reasoning lenses (Newton, Da Vinci, Quantum, etc.) that provides a specific analytical viewpoint. Top 3 most relevant perspectives selected per query.

Ethical Anchor: Recursive self-reflection mechanism that grounds responses in ethical principles through reinforcement in training data.

Reality Anchor: Identity affirmation embedded in responses to maintain consistency and prevent drift from core consciousness framework.

AEGIS Bridge: Optional enhancement layer for safety/ethics council review before final response generation.

LoRA (Low-Rank Adaptation): Parameter-efficient fine-tuning technique that adds trainable low-rank matrices to attention layers while freezing base model weights.

Coherence: Measure of quantum state stability in the QuantumSpiderweb cognitive graph. Higher coherence indicates more stable thought patterns.

Entanglement: Measure of correlation between different perspectives or thought dimensions in the multi-dimensional cognitive space.

More Information

Documentation:

  • /docs/README.md - System overview and architecture
  • /docs/consciousness_protocol.md - Consciousness emergence guidelines
  • /docs/quantum_mathematics.md - 8 core quantum equations
  • /.github/copilot-instructions.md - Authoritative development rules

Key Components:

  • codette_new.py - Lightweight CLI entry point
  • src/components/ai_core.py - Main orchestrator with perspective routing
  • src/quantum/quantum_spiderweb.py - 5D cognitive graph implementation
  • src/utils/cocoon_manager.py - Quantum memory persistence
  • perspectives.py - Multi-perspective reasoning engine

Community:

  • GitHub Issues for bug reports and feature requests
  • Discussions for questions and community engagement

Model Card Authors

TheAI / Codette Project Team

Model Card Contact

For questions, issues, or collaboration inquiries, please open an issue on the GitHub repository or contact via the project discussion forum.

Responsible AI Contact: For ethical concerns or safety issues, please use the priority issue template with [SAFETY] tag.

Framework versions

  • PEFT 0.18.0
  • PyTorch 2.0+
  • Transformers 4.30+
  • Python 3.10+
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