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| license: apache-2.0 |
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| # Codette GPT-OSS-20B Training Dataset |
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| ## Overview |
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| This repository contains the structured training dataset used to fine-tune `openai/gpt-oss-20b` into a behaviorally conditioned architecture referred to as **Codette**. |
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| The goal of this dataset is not personality injection or artificial sentience simulation. |
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| The objective is structured behavioral conditioning across: |
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| - Recursive reasoning (RC+ξ framework) |
| - Multi-perspective synthesis |
| - Governance-aware responses |
| - Natural response enhancement |
| - Cross-module architectural coherence |
| - Dynamic explanation depth scaling |
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| This dataset is designed for LoRA-based fine-tuning of GPT-OSS-20B using 4-bit quantization (QLoRA). |
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| ## Dataset File |
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| codette_gptoss20b_master_v3.jsonl |
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| Total Samples: ~5,000 |
| Format: JSON Lines |
| Structure per entry: |
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| ```json |
| { |
| "instruction": "...", |
| "input": "", |
| "output": "...", |
| "metadata": { |
| "category": "...", |
| "depth": "simple | intermediate | technical", |
| "module": "..." |
| } |
| } |
| Key Training Principles |
| 1. Dynamic Explanation Scaling |
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| The model is trained to automatically adjust explanation depth based on user query context: |
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| Simple explanations for general audiences |
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| Intermediate explanations for practitioners |
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| Technical explanations for formal requests |
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| 2. Governance Stability |
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| Examples reinforce: |
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| Ethical constraint adherence |
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| Refusal handling with clarity |
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| No bypass of safety mechanisms |
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| 3. RC+ξ Recursive Reasoning |
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| The dataset conditions structured reasoning concepts including: |
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| Epistemic tension (ξ) |
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| Recursive state evolution |
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| Convergence behavior |
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| Attractor dynamics |
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| These are applied contextually rather than injected indiscriminately. |
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| 4. Natural Response Enhancement |
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| Examples train the model to: |
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| Avoid robotic phrasing |
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| Avoid system markers or bracket artifacts |
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| Maintain clarity without over-verbosity |
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| 5. Cross-Module Integration |
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| Training includes architectural reasoning across components such as: |
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| Recursive reasoning |
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| Natural enhancement layer |
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| Governance system |
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| Adaptive learning behaviors |
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| Intended Use |
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| This dataset is intended for: |
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| LoRA fine-tuning of GPT-OSS-20B |
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| Architectural behavioral conditioning |
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| Research into structured recursive reasoning systems |
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| Controlled deployment experiments |
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| Not Intended For |
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| Claims of machine consciousness |
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| Identity simulation |
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| Misrepresentation of system capabilities |
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| Replacement for safety-aligned governance models |
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| Recommended Training Configuration |
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| 4-bit NF4 quantization |
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| LoRA rank 32 |
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| 3 epochs |
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| Learning rate: 1e-4 |
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| Cosine scheduler |
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| A100 GPU recommended |
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| Author |
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| Jonathan Harrison |
| Raiff1982 |
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| License |
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| Specify license here (e.g., Apache 2.0, MIT, or research-only). |
