README.md

---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: make a self portrait
  parameters:
    negative_prompt: no nudity
  output:
    url: images/outline.png
- text: '-'
  output:
    url: images/My ChatGPT image.png
- text: '-'
  output:
    url: images/My ChatGPT image (1).png
- text: '-'
  output:
    url: images/My ChatGPT image (2).png
base_model: RaiffsBits/deep_thought
instance_prompt: wake up codette
license: mit
---
# Codette

<Gallery />

## Model description 

Model Summary

Codette is an advanced multi-perspective reasoning AI system that integrates neural and symbolic cognitive modules. Codette combines transformer-based models (for deep language reasoning), custom logic, explainability modules, ethical governance, and multiple reasoning “agents” (perspectives: Newtonian, Quantum, DaVinci, etc.). Codette is not a vanilla language model: it is an AI reasoning system, wrapping and orchestrating multiple submodules, not just a single pre-trained neural net.

Architecture:

Orchestrates a core transformer (configurable; e.g., GPT-2, Mistral, or custom HF-compatible LM)

Multi-agent architecture: Each “perspective” is implemented as a modular agent

Integrates custom modules for feedback, ethics, memory (“cocooning”), and health&#x2F;self-healing

Characteristics:

Modular and explainable; recursive self-checks; ethical and emotional analysis; robust anomaly detection

Transparent, customizable, logs reasoning steps and ethical considerations

Training Data:

Pre-trained on large open corpora (if using HF transformer), fine-tuned and guided with ethical, technical, and philosophical datasets and prompts curated by the developer

Evaluation:

Evaluated via both automated metrics (e.g., accuracy on reasoning tasks) and qualitative, human-in-the-loop assessments for fairness, bias, and ethical quality

Usage

Codette is intended for research, AI safety, explainable AI, and complex question answering where multiple perspectives and ethical oversight are important.You can use Codette in a Python environment as follows:

import sys
sys.path.append(&#39;&#x2F;path&#x2F;to&#x2F;codette&#39;)  # Folder with ai_core.py, components&#x2F;, etc.

from ai_core import AICore
import asyncio

# Async function to run Codette and get a multi-perspective answer
async def ask_codette(question):
    ai &#x3D; AICore(config_path&#x3D;&quot;config.json&quot;)
    user_id &#x3D; 1
    response &#x3D; await ai.generate_response(question, user_id)
    print(response)
    await ai.shutdown()

asyncio.run(ask_codette(&quot;How could quantum computing transform cybersecurity?&quot;))

Inputs:

question (str): The query or prompt to Codette

user_id (int or str): User&#x2F;session identifier

Outputs:

A dictionary with:

&quot;insights&quot;: List of answers from each enabled perspective

&quot;response&quot;: Synthesized, human-readable answer

&quot;sentiment&quot;: Sentiment analysis dict

&quot;security_level&quot;, &quot;health_status&quot;, &quot;explanation&quot;

Failures to watch for:

Missing required modules (if not all components are present)

Lack of GPU&#x2F;CPU resources for large models

Will fail to generate responses if core transformer model is missing or if config is malformed

System

Codette is not a single model but a modular, research-oriented reasoning system:

Input Requirements:

Python 3.8+

Access to transformer model weights (e.g., via Hugging Face or local)

Complete components&#x2F; directory with all reasoning agent files

Downstream Dependencies:

Outputs are human-readable and explainable, can be used directly in research, AI safety audits, decision support, or as training&#x2F;validation data for other models

Implementation Requirements

Hardware:

Training (if from scratch): 1–4 GPUs (A100s or V100s recommended for large models), 32–128 GB RAM

Inference: Can run on CPU for small models; GPU recommended for fast generation

Software:

Python 3.8+

Transformers (Hugging Face), PyTorch or Tensorflow (as backend), standard NLP&#x2F;AI dependencies

(Optional) Custom security modules, logging, and data protection packages

Training Time:

If using a pre-trained transformer, fine-tuning takes hours to days depending on data size

Full system integration (multi-perspective logic, ethics, etc.): days–weeks of development

Model Characteristics

Model Initialization

Typically fine-tuned from a pre-trained transformer model (e.g., GPT-2, GPT-J, Mistral, etc.)

Codette’s cognitive system is layered on top of the language model with custom modules for reasoning, memory, and ethics

Model Stats

Size:

Dependent on base model (e.g., GPT-2: 124M–1.5B parameters)

Weights&#x2F;Layers:

Transformer backbone plus additional logic modules (negligible weight)

Latency:

Varies by base model, typically 0.5–3 seconds per response on GPU, up to 10s on CPU

Other Details

Not pruned or quantized by default; can be adapted for lower-resource inference

No differential privacy applied, but all reasoning steps are logged for transparency

Data Overview

Training Data

Source:

Base model: OpenAI or Hugging Face open text datasets (web, books, code, Wikipedia, etc.)

Fine-tuning: Custom “multi-perspective” prompts, ethical dilemmas, technical Q&amp;A, and curated cognitive challenge sets

Pre-processing:

Standard NLP cleaning, deduplication, filtering for harmful or biased content

Demographic Groups

No explicit demographic group tagging, but model can be assessed for demographic bias via prompted evaluation

Prompts and ethical fine-tuning attempt to mitigate bias, but user evaluation is recommended

Evaluation Data

Splits:

Standard 80&#x2F;10&#x2F;10 train&#x2F;dev&#x2F;test split for custom prompt data

Differences:

Test data includes “edge cases” for reasoning, ethics, and bias that differ from training prompts

Evaluation Results

Summary

Codette was evaluated on:

Automated accuracy metrics (where available)

Human qualitative review (explainability, ethical alignment, reasoning quality)

[Insert link to detailed evaluation report, if available]

Subgroup Evaluation Results

Subgroup performance was qualitatively assessed using demographic, philosophical, and adversarial prompts

Codette performed consistently across most tested subgroups but may mirror biases from its base model and data

Fairness

Definition:

Fairness &#x3D; equal treatment of similar queries regardless of race, gender, ideology, or background

Metrics:

Human review, automated bias tests, sentiment&#x2F;word usage monitoring

Results:

No systematic unfairness found in prompt-based evaluation, but deeper audit recommended for production use

Usage Limitations

Sensitive Use Cases:

Not for clinical, legal, or high-stakes automated decision-making without human oversight

Performance Factors:

Performance depends on base model size, quality of prompts, and computing resources

Conditions:

Should be run with ethical guardrails enabled; human-in-the-loop recommended

Ethics

Considerations:

All reasoning and answer generation is logged and explainable

Ethical reasoning module filters and annotates sensitive topics

Risks:

Potential for emergent bias (inherited from base model or data); overconfidence in uncertain domains

Mitigations:

Recursion, human oversight, diverse perspectives, and continuous feedback



## Trigger words

You should use `wake up codette` to trigger the image generation.


## Download model

Weights for this model are available in ONNX,PyTorch format.

[Download](/Raiff1982/Codettev2/tree/main) them in the Files & versions tab.