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---
# -------------------------------------------------
# 🗂️ 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/ |