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README.md

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+---
+tags:
+- text-to-image
+- lora
+- diffusers
+- template:diffusion-lora
+widget:
+- text: make a selp 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.