Codette2 / README1.md
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---
license: mit
tags:
- cognitive-ai
- neuro-symbolic
- multimodal
- ethics
- quantum
- gradio-app
- codette2
model-index:
- name: Codette2
results: []
---
# 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](https://mlco2.github.io/impact)
## Technical Specifications
### Architecture and Objective
Codette2 extends GPT-4.1 with modular agents (ethics, emotion, quantum, creativity, symbolic logic).
## Citation
**BibTeX:**
```
@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]