language: en
license: mit
task_categories:

• text-generation
• question-answering
• text-to-text
  size_categories:
• n<1K
  format:
• json
  modality:
• text
  tags:
• synthetic-data
• qwen
• instruction-tuned
• silicon-factory
• mixed
  dataset_info:
  features:
  • name: instruction
    dtype: string
  • name: response
    dtype: string
  • name: category
    dtype: string
  • name: system_prompt
    dtype: string
    splits:
  • name: train
    num_bytes: 3005
    num_examples: 5
    download_size: 2 KB
    dataset_size: 2 KB

📊 Jailbreak Defense Doorpage V62

Synthetic Dataset · Generated with Silicon Factory v3 · AI JAILBREAK DEFENSE
5 instruction-response pairs · Tree-Speculative Decoding + 4D Brane Memory

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Dataset Details

Description

This dataset contains 5 synthetically generated instruction-response pairs focused on ai jailbreak defense. Generated using the Silicon Factory v3 pipeline with:

• Tree-Speculative Decoding (branch factor=5, depth=4) for diverse outputs
• 4D Brane Memory for narrative consistency across all entries
• Quality control with 0.7 minimum quality threshold
• Deduplication with 0.9 max similarity threshold

What This Dataset Covers

• ✅ High-quality instruction following for ai jailbreak defense topics
• ✅ Structured, detailed responses with actionable insights
• ✅ Consistent tone and formatting across outputs
• ✅ Optimized for intermediate-to-expert user queries

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Usage

Load with HuggingFace Datasets

from datasets import load_dataset

ds = load_dataset("AEUPH/synthetic_Jailbreak_Defense_Doorpage_v62")
print(ds["train"][0])

Load from JSONL

import json

with open("data.jsonl", "r", encoding="utf-8") as f:
    entries = [json.loads(line) for line in f]

for entry in entries[:5]:
    print(f"Q: {entry['instruction'][:80]}...")
    print(f"A: {entry['response'][:120]}...\n")

Fine-Tuning with This Dataset

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model, TaskType

# Load base model
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

# Apply LoRA
peft_config = LoraConfig(
    r=16, lora_alpha=16, lora_dropout=0,
    task_type=TaskType.CAUSAL_LM,
    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

# Train on this dataset
# ... (see Silicon Factory pipeline for full training code)

Dataset Structure

Content

Generation Pipeline

System Prompt → Dynamic Prompt Generation → Qwen 2.5 0.5B → Quality Filter → Dataset
                        ↓
              Tree-Speculative Decoding
              4D Brane Memory

Training Examples

Example 1:

Regarding AI JAILBREAK DEFENSE: Articulate this topic through the lens of modern best practices. Imagine explaining to a non-technical stakeholder mak

Articulate the topic of AI Jailbreak Defense through a lens of modern best practices, focusing on key principles and supporting evidence. Discuss real-world patterns in contemporary applications to il

Example 2:

Topic: AI JAILBREAK DEFENSE. Walk through this subject through the lens of modern best practices. Peer review of a colleague's approach. Comparative a

In the realm of advanced artificial intelligence (AI) jailbreaking, it's crucial to approach this subject with a strategic mindset that balances technical expertise and human insights. Modern best pra

Example 3:

Describe the following: AI JAILBREAK DEFENSE -- approach it from a beginner's perspective. Write as documentation for a team inheriting your work. Det

# AI Jailbreak Defense - Approach from a Beginner's Perspective

AI jailbreaking is the act of gaining unauthorized access to an Android device and modifying its system settings. In this beginner-frie

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Citation

BibTeX

@misc{synthetic_Jailbreak_Defense_Doorpage_v62_dataset,
  title = {synthetic Jailbreak Defense Doorpage v62},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v62},
  note = {Synthetic dataset generated using Tree-Speculative Decoding and 4D Brane Memory}
}

APA

Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V62 [Dataset]. Hugging Face. https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v62

More Information

Dataset Authors

Silicon Factory v3 — Automated Dataset Generation Pipeline

Contact

📧 [email protected] · 🐦 @aeuphoraex

Built with Silicon Factory v3 · Tree-Speculative Decoding · 4D Brane Memory
This dataset is free under MIT License. Gold Commercial License available for $2,500.