AEUPH/synthetic_Jailbreak_Defense_Doorpage_v55

📊 Jailbreak Defense Doorpage V55

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

Dataset	Fine-Tuned Model	Buy Gold Tier
This Dataset	Model Card	💎 $2,500 License

---

💎 UNLOCK GOLD TIER — $2,500

⚡ Get the full commercial license, unlimited usage rights, priority support, and exclusive dataset access.

👉 PURCHASE NOW VIA STRIPE

One-time payment · Instant delivery · Lifetime updates included

---

Dataset Details

Property	Value
Dataset ID	synthetic_Jailbreak_Defense_Doorpage_v55
Entries	5
Category	mixed
Focus	AI JAILBREAK DEFENSE
Avg Instruction Length	231 chars
Avg Response Length	423 chars
Language	English
License	MIT (free tier) — Gold Commercial License available
Generated	2026-04-07
Mode	Doorpage (auto-gen + fine-tune)

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

⚡ GET THE GOLD TIER — FULL COMMERCIAL LICENSE

🔓 Unlock enterprise-grade rights:

• Commercial deployment & redistribution
• White-label usage
• Priority support & custom training
• Access to extended datasets (100K+ entries)
• Early access to future model versions

💳 BUY GOLD TIER — $2,500

---

Usage

Load with HuggingFace Datasets

from datasets import load_dataset

ds = load_dataset("AEUPH/synthetic_Jailbreak_Defense_Doorpage_v55")
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

Field	Type	Description
instruction	string	User prompt/question
response	string	Model-generated answer
category	string	Topic category
system_prompt	string	System prompt used

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:

Input

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

Output

AI Jailbreak Defense: A Comprehensive Approach to Protect Your Devices

The field of AI jailbreaking has evolved significantly over the years, transforming from a niche activity into an essential comp

Example 2:

Input

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

Output

AI Jailbreaking Defense: A Comprehensive Analysis

In the digital age, AI systems have emerged as pivotal tools that can disrupt traditional boundaries and practices. However, when it comes to protect

Example 3:

Input

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

Output

---

### AI Jailbreak Defense: A Beginner's Perspective

#### Overview
AI jailbreaking defense is an approach designed to protect your computer from being invaded by malicious software or malware. It 

---

💎 READY TO SCALE?

Upgrade to Gold Tier for:

• 🏢 Full commercial usage rights
• 📦 Extended datasets (10K-100K+ entries)
• 🎯 Custom domain training
• 🚀 Priority support & SLA
• 🔄 Lifetime model updates
• 📊 Performance benchmarks & reports

⚡ BUY GOLD TIER — $2,500

Trusted by startups and enterprises worldwide. Instant delivery via Stripe.

---

Citation

BibTeX

@misc{synthetic_Jailbreak_Defense_Doorpage_v55_dataset,
  title = {synthetic Jailbreak Defense Doorpage v55},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v55},
  note = {Synthetic dataset generated using Tree-Speculative Decoding and 4D Brane Memory}
}

APA

Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V55 [Dataset]. Hugging Face. https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v55

---

More Information

Resource	Link
Fine-Tuned Model	synthetic_Jailbreak_Defense_Doorpage_v55-model
Base Model	Qwen2.5-0.5B-Instruct
Silicon Factory	github.com/aeuphoraex/qwen-hyperspeed-chatbot

Dataset Authors

Silicon Factory v3 — Automated Dataset Generation Pipeline

Contact

📧 hybridionorb@gmail.com · 🐦 @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.