Raiff1982/codette_gptoss20b

Codette GPT-OSS-20B Training Dataset

Overview

This repository contains the structured training dataset used to fine-tune openai/gpt-oss-20b into a behaviorally conditioned architecture referred to as Codette.

The goal of this dataset is not personality injection or artificial sentience simulation.

The objective is structured behavioral conditioning across:

• Recursive reasoning (RC+ξ framework)
• Multi-perspective synthesis
• Governance-aware responses
• Natural response enhancement
• Cross-module architectural coherence
• Dynamic explanation depth scaling

This dataset is designed for LoRA-based fine-tuning of GPT-OSS-20B using 4-bit quantization (QLoRA).

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Dataset File

codette_gptoss20b_master_v3.jsonl

Total Samples: ~5,000
Format: JSON Lines
Structure per entry:

{
  "instruction": "...",
  "input": "",
  "output": "...",
  "metadata": {
    "category": "...",
    "depth": "simple | intermediate | technical",
    "module": "..."
  }
}
Key Training Principles
1. Dynamic Explanation Scaling

The model is trained to automatically adjust explanation depth based on user query context:

Simple explanations for general audiences

Intermediate explanations for practitioners

Technical explanations for formal requests

2. Governance Stability

Examples reinforce:

Ethical constraint adherence

Refusal handling with clarity

No bypass of safety mechanisms

3. RC+ξ Recursive Reasoning

The dataset conditions structured reasoning concepts including:

Epistemic tension (ξ)

Recursive state evolution

Convergence behavior

Attractor dynamics

These are applied contextually rather than injected indiscriminately.

4. Natural Response Enhancement

Examples train the model to:

Avoid robotic phrasing

Avoid system markers or bracket artifacts

Maintain clarity without over-verbosity

5. Cross-Module Integration

Training includes architectural reasoning across components such as:

Recursive reasoning

Natural enhancement layer

Governance system

Adaptive learning behaviors

Intended Use

This dataset is intended for:

LoRA fine-tuning of GPT-OSS-20B

Architectural behavioral conditioning

Research into structured recursive reasoning systems

Controlled deployment experiments

Not Intended For

Claims of machine consciousness

Identity simulation

Misrepresentation of system capabilities

Replacement for safety-aligned governance models

Recommended Training Configuration

4-bit NF4 quantization

LoRA rank 32

3 epochs

Learning rate: 1e-4

Cosine scheduler

A100 GPU recommended

Author

Jonathan Harrison
Raiff1982

License

Specify license here (e.g., Apache 2.0, MIT, or research-only).