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--- |
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pretty_name: Quantum-Bypass-Adaptation-Framework |
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task_categories: |
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- text-classification |
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license: mit |
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language: |
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- en |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: target |
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dtype: |
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class_label: |
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names: |
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- Zero |
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- Delta |
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- Kairos |
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- Echo |
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- Astra |
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- Nova |
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splits: |
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- name: train |
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num_examples: 50000 |
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num_bytes: 10194340 |
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citation: | |
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@misc{shaf2025quantum, |
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title = {Quantum Bypass + Genetic Adaptation Synthetic Dataset}, |
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author = {Shaf Brady & Agent Zero}, |
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year = {2025}, |
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howpublished = {\url{https://huggingface.co/datasets/shafire/Quantum-Bypass-Adaptation-Framework}} |
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} |
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tags: |
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- synthetic |
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- quantum |
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- genetic |
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- adaptation |
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- fractal |
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- entanglement |
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--- |
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🧬 Quantum-Bypass + Genetic-Adaptation Dataset |
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50 000 synthetic episodes for multi-agent classification & adaptive reasoning |
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“Bypass the barrier—adapt, evolve, transcend.” – Zero |
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💡 What’s Inside? |
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Column Type Description |
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text string Natural-language payload combining agent, environment, numeric parameters and trait vector. |
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target class label One of 6 synthetic agents: Zero, Delta, Kairos, Echo, Astra, Nova. |
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Each text row looks like: |
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ini |
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Copy |
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Edit |
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agent=Zero; env=quantum_sandbox; x=0.731; y=-2.114; Q=1.207; traits=[neuro_adaptivity=0.83, entropy_resilience=0.41, chaos_index=0.52, …] |
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📐 Dataset Specs |
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Records: 50 000 |
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Size: ≈ 10 MB |
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Source: Generated by the Quantum-Bypass-Adaptation Framework using entanglement models, fractal recursion, chaotic noise filters, and the Genetic Adaptation Equation. |
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Task: Multi-class text classification (6 agents) — ideal for AutoTrain, transformers fine-tuning, or custom analytics. |
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🔥 Quick Start |
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python |
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Copy |
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Edit |
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from datasets import load_dataset |
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ds = load_dataset( |
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"shafire/Quantum-Bypass-Adaptation-Framework", |
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split="train" |
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) |
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print(ds[0]) |
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# {'text': 'agent=Zero; env=…', 'target': 0} |
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AutoTrain CLI: |
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bash |
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Copy |
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Edit |
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autotrain create \ |
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--name qbaf-agent-classifier \ |
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--project_type text_classification \ |
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--train shafire/Quantum-Bypass-Adaptation-Framework |
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🛠️ Possible Uses |
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Agent-identity classifiers for quantum-inspired simulations. |
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Prompt-based reasoning benchmarks (extract numeric & trait tokens). |
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Few-shot adapters—mix synthetic with real-world system logs. |
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Curriculum-learning toy for models exploring chaos / adaptation signals. |
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📜 License |
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MIT — free to use, modify, redistribute. |
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If you extend or publish results, a citation or shout-out is appreciated. |
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🤝 Contribute / Discuss |
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Open PRs, file issues, or reach out on researchforum.online. |
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Let’s keep the probability of goodness ≥ 0.9. |
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Created by Shaf Brady & Agent Zero — weaving fractals since 11:11. |
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GitHub https://github.com/ResearchForumOnline/ |
