---
# -------------------------------------------------
# 🗂️  Dataset Card — Quantum-Bypass-Adaptation-Framework
# -------------------------------------------------
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         # ≈ 10 MB
  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
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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, …]
📐 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
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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
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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/