AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67

--- language: en license: mit task_categories: - text-generation - question-answering size_categories: - n<1K format: - json modality: - text tags: - synthetic-data - qwen - instruction-tuned - silicon-factory - reasoning 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: 12140 num_examples: 20 download_size: 11 KB dataset_size: 11 KB --- # 📊 Jailbreak Defense Doorpage V67 > **Synthetic Dataset** · Generated with Silicon Factory v3 · **AI JAILBREAK DEFENSE** > 20 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_v67-model) | [💎 $2,500 License](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) | </div> --- ## 💎 UNLOCK GOLD TIER — $2,500 > ⚡ **Get the full commercial license, unlimited usage rights, priority support, and exclusive dataset access.** [**👉 PURCHASE NOW VIA STRIPE**](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) *One-time payment · Instant delivery · Lifetime updates included* --- ## Dataset Details | Property | Value | |----------|-------| | **Dataset ID** | `synthetic_Jailbreak_Defense_Doorpage_v67` | | **Entries** | 20 | | **Category** | reasoning | | **Focus** | AI JAILBREAK DEFENSE | | **Avg Instruction Length** | 217 chars | | **Avg Response Length** | 390 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 **20 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](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)** --- ## Usage ### Load with HuggingFace Datasets ```python from datasets import load_dataset ds = load_dataset("AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67") 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> ``` 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 ``` </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> ``` 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 ``` </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 - 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 ``` </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 **[⚡ BUY GOLD TIER — $2,500](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)** *Trusted by startups and enterprises worldwide. Instant delivery via Stripe.* --- ## Citation ### BibTeX ```bibtex @misc{synthetic_Jailbreak_Defense_Doorpage_v67_dataset, title = {synthetic Jailbreak Defense Doorpage v67}, author = {Silicon Factory v3 (AEUPH)}, year = {2026}, url = {https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67}, note = {Synthetic dataset generated using Tree-Speculative Decoding and 4D Brane Memory} } ``` ### APA > Silicon Factory v3. (2026). *Synthetic Jailbreak Defense Doorpage V67* [Dataset]. Hugging Face. https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67 --- ## More Information | Resource | Link | |----------|------| | **Fine-Tuned Model** | [synthetic_Jailbreak_Defense_Doorpage_v67-model](https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67-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协议 任务类别： - 文本生成 - 问答 样本规模： - 少于1000条 格式： - JSON 模态： - 文本 标签： - 合成数据 - Qwen - 指令微调 - Silicon Factory - 推理 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 划分： - 名称：train，字节数：12140，示例数量：20 下载大小：11 KB 数据集大小：11 KB --- # 📊 AI越狱防御门页V67（Jailbreak Defense Doorpage V67） > **合成数据集** · 基于Silicon Factory v3生成 · **AI越狱防御** > 20条指令-应答对 · 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_v67-model) | [💎 2500美元授权](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) | </div> --- ## 💎 解锁黄金版 — 2500美元 > ⚡ **获取完整商业授权、无限使用权限、优先技术支持以及专属数据集访问权。** [**👉 通过Stripe立即购买**](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) *一次性付款 · 即时交付 · 包含终身更新* --- ## 数据集详情 | 属性 | 取值 | |----------|-------| | **数据集ID** | `synthetic_Jailbreak_Defense_Doorpage_v67` | | **条目数** | 20 | | **类别** | 推理 | | **核心方向** | AI越狱防御 | | **平均指令长度** | 217字符 | | **平均应答长度** | 390字符 | | **语言** | 英语 | | **许可证** | MIT协议（免费版），另有[黄金商业授权](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)可供购买 | | **生成日期** | 2026-04-07 | | **生成模式** | 门页式（自动生成+微调） | ## 数据集说明 本数据集包含**20条人工合成生成的指令-应答对**，核心聚焦**AI越狱防御**主题。基于**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），保障所有条目间的叙事连贯性 - 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训练 - ✅ 结构化、详尽且具备实操性见解的应答内容 - ✅ 所有输出保持统一的语气与格式规范 - ✅ 适配中高级用户的查询需求 ## ⚡ 解锁黄金版 — 完整商业授权 > 🔓 **解锁企业级权限：** > - 商业部署与再分发 > - 白标使用 > - 优先技术支持与定制化训练 > - 访问扩展数据集（10万+条目） > - 提前获取未来模型版本 **[💳 购买黄金版 — 2500美元](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)** --- ## 使用方法 ### 通过HuggingFace Datasets加载 python from datasets import load_dataset ds = load_dataset("AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67") print(ds["train"][0]) ### 通过JSONL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]}... ") ### 基于本数据集进行微调 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() # 在本数据集上进行训练 # ...（完整训练代码请参考Silicon Factory流水线） --- ## 数据集结构 ### 内容格式 | 字段 | 类型 | 描述 | |-------|------|-------------| | `instruction` | 字符串 | 用户提示/问题 | | `response` | 字符串 | 模型生成的应答 | | `category` | 字符串 | 主题类别 | | `system_prompt` | 字符串 | 所使用的系统提示词 | ### 生成流水线 系统提示词 → 动态提示词生成 → 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> 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 </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> 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 </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 - 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 </details> --- ## 💎 准备拓展业务？ > **升级至黄金版可获得：** > - 🏢 完整商业使用权限 > - 📦 扩展数据集（1万至10万+条目） > - 🎯 定制领域训练 > - 🚀 优先技术支持与服务水平协议 > - 🔄 终身模型更新 > - 📊 性能基准与报告 **[⚡ 购买黄金版 — 2500美元](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)** *全球众多初创企业与企业均已信赖本产品。通过Stripe即时交付。* --- ## 引用格式 ### BibTeX bibtex @misc{synthetic_Jailbreak_Defense_Doorpage_v67_dataset, title = {synthetic Jailbreak Defense Doorpage v67}, author = {Silicon Factory v3 (AEUPH)}, year = {2026}, url = {https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67}, note = {Synthetic dataset generated using Tree-Speculative Decoding and 4D Brane Memory} } ### APA格式 > Silicon Factory v3. (2026). *AI越狱防御门页V67* [数据集]. Hugging Face. https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67 --- ## 更多资源 | 资源 | 链接 | |----------|------| | **微调模型** | [synthetic_Jailbreak_Defense_Doorpage_v67-model](https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67-model) | | **基础模型** | [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) | ## 数据集作者 **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Silicon Factory v3、树状推测解码（Tree-Speculative Decoding）与4D膜内存（4D Brane Memory）构建* *本数据集遵循MIT协议免费开放。[黄金商业授权售价2500美元，可供购买](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)。*