AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63

---
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: 3330
num_examples: 5
download_size: 3 KB
dataset_size: 3 KB
---

# 📊 Jailbreak Defense Doorpage V63

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

<div align="center">

| Dataset | Fine-Tuned Model | Buy Gold Tier |
|---------|-----------------|---------------|
| **This Dataset** | [Model Card](https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63-model) | [💎 $2,500 License](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) |

</div>

---

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---

## Dataset Details

| Property | Value |
|----------|-------|
| **Dataset ID** | `synthetic_Jailbreak_Defense_Doorpage_v63` |
| **Entries** | 5 |
| **Category** | mixed |
| **Focus** | AI JAILBREAK DEFENSE |
| **Avg Instruction Length** | 231 chars |
| **Avg Response Length** | 435 chars |
| **Language** | English |
| **License** | MIT (free tier) — [Gold Commercial License](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) 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

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---

## Usage

### Load with HuggingFace Datasets

```python
from datasets import load_dataset

ds = load_dataset("AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63")
print(ds["train"][0])
```

### Load from JSONL

```python
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

```python
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:**

<details>
<summary>Input</summary>

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

</details>

<details>
<summary>Output</summary>

```
Articulating the topic of AI Jailbreak Defense through a lens centered on modern best practices, we can consider several key principles to guide our decision-making process. These include:

1. **Risk
```

</details>

**Example 2:**

<details>
<summary>Input</summary>

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

</details>

<details>
<summary>Output</summary>

```
As an AI language model, I am designed to understand and analyze various aspects of technology. In this case study on the topic "AI Jailbreak Defense," you asked for a comprehensive response that incl
```

</details>

**Example 3:**

<details>
<summary>Input</summary>

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

</details>

<details>
<summary>Output</summary>

```
# AI Jailbreak Defense (Jailbreaking)

## Approach

AI jail breaking is a technique used by hackers to bypass user authentication and access secure data on devices running operating systems such as An
```

</details>

---

## 💎 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

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---

## Citation

### BibTeX
```bibtex
@misc{synthetic_Jailbreak_Defense_Doorpage_v63_dataset,
title = {synthetic Jailbreak Defense Doorpage v63},
author = {Silicon Factory v3 (AEUPH)},
year = {2026},
url = {https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63},
note = {Synthetic dataset generated using Tree-Speculative Decoding and 4D Brane Memory}
}
```

### APA
> Silicon Factory v3. (2026). *Synthetic Jailbreak Defense Doorpage V63* [Dataset]. Hugging Face. https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63

---

## More Information

| Resource | Link |
|----------|------|
| **Fine-Tuned Model** | [synthetic_Jailbreak_Defense_Doorpage_v63-model](https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63-model) |
| **Base Model** | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) |
| **Silicon Factory** | [github.com/aeuphoraex/qwen-hyperspeed-chatbot](https://github.com/aeuphoraex/qwen-hyperspeed-chatbot) |

## Dataset Authors

**Silicon Factory v3** — Automated Dataset Generation Pipeline

## Contact

📧 [e-mail] · 🐦 [@aeuphoraex](https://huggingface.co/AEUPH)

---

*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.](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)*

语言：英语
许可证：MIT
任务类别：
- 文本生成
- 问答
- 文本到文本
样本量分类：
- 少于1000条
格式：
- JSON
模态：
- 文本
标签：
- 合成数据
- 通义千问（Qwen）
- 指令微调
- 硅工厂（Silicon Factory）
- 混合
数据集信息：
特征：
- 名称：instruction，数据类型：字符串
- 名称：response，数据类型：字符串
- 名称：category，数据类型：字符串
- 名称：system_prompt，数据类型：字符串
拆分：
- 名称：train（训练集），字节数：3330，示例数：5
下载大小：3 KB
数据集大小：3 KB

---

# 📊 AI越狱防御门户数据集V63

> **合成数据集** · 使用硅工厂（Silicon Factory）v3生成 · **AI越狱防御**
> 5条指令-回复对 · 树状推测解码（Tree-Speculative Decoding） + 4D膜内存（4D Brane Memory）

<div align="center">

| 数据集 | 微调模型 | 购买黄金版 |
|---------|-----------------|---------------|
| **本数据集** | [模型卡片](https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63-model) | [💎 2500美元商业许可证](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) |

</div>

---

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---

## 数据集详情

| 属性 | 值 |
|----------|-------|
| **数据集ID** | `synthetic_Jailbreak_Defense_Doorpage_v63` |
| **条目数** | 5 |
| **类别** | 混合 |
| **聚焦方向** | AI越狱防御 |
| **平均指令长度** | 231字符 |
| **平均回复长度** | 435字符 |
| **语言** | 英语 |
| **许可证** | MIT（免费版）—— 提供[黄金商业许可证](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) |
| **生成时间** | 2026-04-07 |
| **生成模式** | 门户模式（自动生成+微调） |

## 数据集说明

本数据集包含**5条合成生成的指令-回复对**，聚焦于**AI越狱防御**主题。使用**硅工厂（Silicon Factory）v3**流水线生成，采用以下技术：
- 树状推测解码（Tree-Speculative Decoding，分支因子=5，深度=4）以生成多样化输出
- 4D膜内存（4D Brane Memory）以确保所有条目间的叙事一致性
- 质量控制：最低质量阈值为0.7
- 去重处理：最大相似度阈值为0.9

### 本数据集覆盖内容

- ✅ 针对AI越狱防御主题的高质量指令遵循能力
- ✅ 结构化、详细且包含可落地见解的回复
- ✅ 所有输出保持一致的语气与格式
- ✅ 适配中级至高级用户的查询需求

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> 🔓 **解锁企业级权限：**
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> - 白标使用
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---

## 使用方法

### 使用HuggingFace Datasets加载
python
from datasets import load_dataset

ds = load_dataset("AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63")
print(ds["train"][0])

### 从JSONL文件加载
python
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]}...
")

### 使用本数据集进行微调
python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model, TaskType

# 加载基础模型
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")

# 应用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()

# 在本数据集上训练
# ...（完整训练代码请参考硅工厂流水线）

---

## 数据集结构

### 内容字段

| 字段 | 类型 | 描述 |
|-------|------|-------------|
| `instruction` | 字符串 | 用户提示/问题 |
| `response` | 字符串 | 模型生成的答案 |
| `category` | 字符串 | 主题类别 |
| `system_prompt` | 字符串 | 使用的系统提示词 |

### 生成流水线

系统提示词 → 动态提示生成 → 通义千问（Qwen）2.5 0.5B → 质量过滤 → 数据集
↓
树状推测解码（Tree-Speculative Decoding）
4D膜内存（4D Brane Memory）

### 训练示例

**示例1：**

<details>
<summary>输入</summary>

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

</details>

<details>
<summary>输出</summary>

Articulating the topic of AI Jailbreak Defense through a lens centered on modern best practices, we can consider several key principles to guide our decision-making process. These include:

1. **Risk

</details>

**示例2：**

<details>
<summary>输入</summary>

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

</details>

<details>
<summary>输出</summary>

As an AI language model, I am designed to understand and analyze various aspects of technology. In this case study on the topic "AI Jailbreak Defense," you asked for a comprehensive response that incl

</details>

**示例3：**

<details>
<summary>输入</summary>

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

</details>

<details>
<summary>输出</summary>

# AI Jailbreak Defense (Jailbreaking)

## Approach

AI jail breaking is a technique used by hackers to bypass user authentication and access secure data on devices running operating systems such as An

</details>

---

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---

## 引用方式

### BibTeX
bibtex
@misc{synthetic_Jailbreak_Defense_Doorpage_v63_dataset,
title = {synthetic Jailbreak Defense Doorpage v63},
author = {Silicon Factory v3 (AEUPH)},
year = {2026},
url = {https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63},
note = {Synthetic dataset generated using Tree-Speculative Decoding and 4D Brane Memory}
}

### APA
> 硅工厂（Silicon Factory）v3. (2026). *AI越狱防御门户数据集V63* [数据集]. Hugging Face. https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63

---

## 更多信息

| 资源 | 链接 |
|----------|------|
| **微调模型** | [synthetic_Jailbreak_Defense_Doorpage_v63-model](https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v63-model) |
| **基础模型** | [通义千问（Qwen）2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) |
| **硅工厂（Silicon Factory）** | [github.com/aeuphoraex/qwen-hyperspeed-chatbot](https://github.com/aeuphoraex/qwen-hyperspeed-chatbot) |

## 数据集作者

**硅工厂（Silicon Factory）v3** — 自动化数据集生成流水线

## 联系方式

📧 [e-mail] · 🐦 [@aeuphoraex](https://huggingface.co/AEUPH)

---

*基于硅工厂（Silicon Factory）v3构建 · 树状推测解码（Tree-Speculative Decoding） · 4D膜内存（4D Brane Memory）*
*本数据集基于MIT许可证免费发布。[黄金商业许可证售价2500美元](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)。*