Model Card for Codette2

Codette2 is a multi-agent cognitive assistant fine-tuned on GPT-4.1, integrating neuro-symbolic reasoning, ethical governance, quantum-inspired optimization, and multimodal analysis. It supports both creative generation and philosophical insight, with support for image/audio input and explainable decision logic.

Model Details

Model Description

• Developed by: Jonathan Harrison
• Model type: Cognitive Assistant (multi-agent)
• Language(s): English
• License: MIT
• Fine-tuned from model: GPT-4.1

Model Sources

• Repository: https://www.kaggle.com/models/jonathanharrison1/codette2
• Demo: Gradio and Jupyter-ready

Uses

Direct Use

• Creative storytelling, ideation, poetry
• Ethical simulations and governance logic
• Image/audio interpretation
• AI research companion or philosophical simulator

Out-of-Scope Use

• Clinical therapy or legal advice
• Deployment without ethical guardrails
• Bias-sensitive environments without further fine-tuning

Bias, Risks, and Limitations

This model embeds filters to detect sentiment and flag unethical prompts, but no AI system is perfect. Outputs should be reviewed when used in sensitive contexts.

Recommendations

Use with ethical filters enabled and log sensitive prompts. Augment with human feedback in mission-critical deployments.

How to Get Started with the Model

from ai_driven_creativity import AIDrivenCreativity
creator = AIDrivenCreativity()
print(creator.write_literature("Dreams of quantum AI"))

Training Details
Training Data

Custom dataset of ethical dilemmas, creative writing prompts, philosophical queries, and multimodal reasoning tasks.
Training Hyperparameters

    Epochs: Variable (~450 steps)

    Precision: fp16

    Loss achieved: 0.00001

Evaluation
Testing Data

Ethical prompt simulations, sentiment evaluation, creative generation scores.
Metrics

Manual eval + alignment tests on ethical response integrity, coherence, originality, and internal consistency.
Results

Codette2 achieved stable alignment and response consistency across >450 training steps with minimal loss oscillation.
Environmental Impact

    Hardware Type: NVIDIA A100 (assumed)

    Hours used: ~3.5

    Cloud Provider: Kaggle / Colab (assumed)

    Carbon Emitted: Estimated via MLCO2

Technical Specifications
Architecture and Objective

Codette2 extends GPT-4.1 with modular agents (ethics, emotion, quantum, creativity, symbolic logic).
Citation

BibTeX:

Always show details

@misc{codette2,
  author = {Jonathan Harrison},
  title = {Codette2: Cognitive Multi-Agent AI Assistant},
  year = 2025,
  howpublished = {Kaggle and HuggingFace}
}

APA:
Jonathan Harrison. (2025). Codette2: Cognitive Multi-Agent AI Assistant. Retrieved from HuggingFace.
Contact

For issues, contact: jonathanharrison1@protonmail.com
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