--- 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 ## 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/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('/path/to/codette') # Folder with ai_core.py, components/, 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 = AICore(config_path="config.json") user_id = 1 response = await ai.generate_response(question, user_id) print(response) await ai.shutdown() asyncio.run(ask_codette("How could quantum computing transform cybersecurity?")) Inputs: question (str): The query or prompt to Codette user_id (int or str): User/session identifier Outputs: A dictionary with: "insights": List of answers from each enabled perspective "response": Synthesized, human-readable answer "sentiment": Sentiment analysis dict "security_level", "health_status", "explanation" Failures to watch for: Missing required modules (if not all components are present) Lack of GPU/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/ 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/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/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/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&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 80/10/10 train/dev/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 = equal treatment of similar queries regardless of race, gender, ideology, or background Metrics: Human review, automated bias tests, sentiment/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.