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Codette2

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codette2
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Codette2
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---
license: mit
tags:
- cognitive-ai
- neuro-symbolic
- multimodal
- ethics
- quantum
- gradio-app
- codette2
model-index:
- name: Codette2
  results: []
language:
- en
datasets:
- Raiff1982/Codettesspecial
base_model:
- Raiff1982/Codettev2
- Raiff1982/autotrain-156ul-mfqfp
library_name: adapter-transformers
---

# 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

```python
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: [email protected]
"""