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