--- language: en license: mit library_name: peft tags: - qwen - qwen2.5 - fine-tuned - synthetic-data - instruction-tuned - silicon-factory base_model: Qwen/Qwen2.5-0.5B-Instruct dataset: - https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58 pipeline_tag: text-generation inference: true --- # πŸš€ Jailbreak Defense Doorpage V58 > **Fine-Tuned from Qwen2.5-0.5B-Instruct** Β· Specialized for **AI JAILBREAK DEFENSE** > Generated with Silicon Factory v3 Β· Tree-Speculative Decoding + 4D Brane Memory
| Dataset | Model | Buy Gold Tier | |---------|-------|---------------| | [synthetic_Jailbreak_Defense_Doorpage_v58](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58) | **This Model** | [πŸ’Ž $2,500 License](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) |
--- ## πŸ’Ž UNLOCK GOLD TIER β€” $2,500 > ⚑ **Get the full commercial license, unlimited usage rights, priority support, and exclusive dataset access.** [**πŸ‘‰ PURCHASE NOW VIA STRIPE**](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) *One-time payment Β· Instant delivery Β· Lifetime updates included* --- ## Model Details | Property | Value | |----------|-------| | **Model ID** | `synthetic_Jailbreak_Defense_Doorpage_v58-model` | | **Base Model** | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) | | **Fine-Tuning Method** | LoRA (r=16, Ξ±=16) | | **Developed by** | Silicon Factory v3 (AEUPH) | | **Release Date** | 2026-04-07 | | **License** | MIT (free tier) β€” [Gold Commercial License](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) available | | **Language** | English | | **Architecture** | Causal Language Model (Transformer) | | **Parameters** | 500M (base) + ~4M LoRA | | **Training Samples** | 5 | | **Avg Response Length** | 415 chars | | **Training Steps** | 30 | | **Learning Rate** | 2e-4 | | **Context Length** | 2048 tokens | ## Model Description This model is a **specialized fine-tuned variant** of Qwen2.5-0.5B-Instruct, trained on a curated synthetic dataset generated through the **Silicon Factory v3** pipeline. It uses **Tree-Speculative Decoding** for diverse output generation and **4D Brane Memory** for narrative consistency across all training samples. **Focus Area:** AI JAILBREAK DEFENSE ### What This Model Does Best - βœ… High-quality instruction following for **ai jailbreak defense** topics - βœ… Structured, detailed responses with actionable insights - βœ… Consistent tone and formatting across outputs - βœ… Optimized for intermediate-to-expert user queries ## ⚑ GET THE GOLD TIER β€” FULL COMMERCIAL LICENSE > πŸ”“ **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 **[πŸ’³ BUY GOLD TIER β€” $2,500](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)** --- ## Uses ### Direct Use This model is designed for: - **Chat & Q&A** β€” Interactive responses on ai jailbreak defense topics - **Content Generation** β€” Articles, documentation, guides, and tutorials - **Research & Analysis** β€” Technical breakdowns and comparative evaluations - **Education** β€” Training materials and onboarding content - **Automation** β€” API-powered assistants and workflows ### Downstream Use Suitable for: - Fine-tuning further on domain-specific data - Integration into RAG pipelines - Knowledge base augmentation - Customer support automation ### Out-of-Scope Use ⚠️ This model is **NOT** intended for: - Medical, legal, or financial advice - High-stakes decision making without human review - Generating harmful, illegal, or unethical content - Misrepresentation as human-authored without disclosure ## Bias, Risks, and Limitations - **Training Data Bias:** Model reflects patterns in synthetic data β€” may not represent real-world diversity - **Knowledge Cutoff:** Based on base model training data β€” no real-time knowledge - **Response Length:** Optimized for ~415-char responses β€” very long queries may be truncated - **Hallucination Risk:** As with all LLMs, outputs may contain plausible but inaccurate statements - **Domain Specificity:** Best performance on **ai jailbreak defense** β€” off-topic queries may yield weaker results > πŸ’‘ **Recommendation:** Always review outputs before deployment. For production use, [obtain the Gold Tier license](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) which includes QA guidelines and support. --- ## How to Get Started ### Python (Transformers + PEFT) ```python from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel # Load base model base_model = "Qwen/Qwen2.5-0.5B-Instruct" tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto", device_map="auto") # Apply LoRA adapters model = PeftModel.from_pretrained(model, "AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58-model") model = model.merge_and_unload() # Generate prompt = "Explain ai jailbreak defense in simple terms" inputs = tokenizer(f"user\n{prompt}\n\nassistant\n", return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.8, top_p=0.95) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### Via HuggingFace Pipeline ```python from transformers import pipeline pipe = pipeline("text-generation", model="AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58-model", torch_dtype="auto", device_map="auto") result = pipe("What is ai jailbreak defense?", max_new_tokens=256) print(result[0]["generated_text"]) ``` ### cURL (HF Inference API) ```bash curl https://api-inference.huggingface.co/models/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58-model \ -X POST \ -H "Authorization: Bearer $HF_TOKEN" \ -H "Content-Type: application/json" \ -d '{"inputs": "Explain ai jailbreak defense", "parameters": {"max_new_tokens": 256}}' ``` --- ## Training Details ### Training Data - **Source:** Synthetic data generated by Silicon Factory v3 - **Size:** 5 instruction-response pairs - **Avg Instruction Length:** 215 chars - **Avg Response Length:** 415 chars - **Category:** mixed - **Focus:** AI JAILBREAK DEFENSE - **Generation Method:** Tree-Speculative Decoding (branch factor=5, depth=4) + 4D Brane Memory for consistency ### Training Procedure | Hyperparameter | Value | |----------------|-------| | **Method** | LoRA (Low-Rank Adaptation) | | **Rank (r)** | 16 | | **Alpha** | 16 | | **Dropout** | 0 | | **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj | | **Learning Rate** | 2e-4 | | **Batch Size** | 2 (per device) | | **Gradient Accumulation** | 4 | | **Warmup Steps** | 5 | | **Total Steps** | 30 | | **Optimizer** | AdamW (torch) | | **Precision** | fp16/bf16 (GPU-dependent) | | **Max Sequence Length** | 2048 | ### Speeds, Sizes, Times - **Model Size:** ~500MB (merged) / ~10MB (LoRA only) - **Training Time:** ~5-15 minutes (GPU) / ~30-60 minutes (CPU) - **Inference Speed:** ~30-80 tokens/sec (GPU) / ~10-30 tokens/sec (CPU) --- ## Evaluation ### Testing Data Training data is generated synthetically with built-in quality control: - **Quality Threshold:** 0.7 minimum score - **Duplicate Threshold:** 0.9 max similarity - **Validation:** All entries reviewed for coherence, relevance, and completeness ### Metrics | Metric | Value | |--------|-------| | **Training Samples** | 5 | | **Valid Entries** | 100% (filtered) | | **Deduplication** | Applied | | **Language** | English | --- ## Summary | Component | Detail | |-----------|--------| | **Base** | Qwen2.5-0.5B-Instruct (Qwen Team, Alibaba) | | **Adapter** | LoRA r=16, all attention + FFN layers | | **Data** | 5 synthetic entries, AI JAILBREAK DEFENSE focus | | **Framework** | Transformers + PEFT + TRL (SFTTrainer) | | **Hardware** | NVIDIA GPU (CUDA) or CPU fallback | | **Precision** | fp16 (Ampere+) / bf16 / fp32 | ### Environmental Impact Estimated using [ML Impact Calculator](https://mlco2.github.io/impact/): - **Hardware:** NVIDIA GPU (consumer-grade) - **Training Time:** ~5-15 minutes - **Carbon Emitted:** < 0.01 kg COβ‚‚eq (efficient LoRA training) --- ## Citation ### BibTeX ```bibtex @misc{synthetic_Jailbreak_Defense_Doorpage_v58_model, title = {synthetic Jailbreak Defense Doorpage v58}, author = {Silicon Factory v3 (AEUPH)}, year = {2026}, url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58-model}, note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA} } ``` ### APA > Silicon Factory v3. (2026). *Synthetic Jailbreak Defense Doorpage V58* [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58-model --- ## More Information ### Related Resources | Resource | Link | |----------|------| | **Dataset** | [synthetic_Jailbreak_Defense_Doorpage_v58](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58) | | **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) | ### Training Examples **Example 1:**
Input ``` Consider AI JAILBREAK DEFENSE. Synthesize it from both theoretical and practical standpoints. Detailed breakdown with specific examples. Guide for someone transitioning from a different field. Conside ```
Output ``` **AI Jailbreak Defense: A Comprehensive Analysis** In the realm of artificial intelligence (AI), security is a critical concern, particularly in scenarios where AI systems are deployed to handle sensitive data and protect user privacy. The use of secure algorithms for managing access control can mi ```
**Example 2:**
Input ``` Topic: AI JAILBREAK DEFENSE. Evaluate this subject at an expert level. Preparation for a certification exam. Decision tree or flow of reasoning. Acknowledge trade-offs and differing advice applicabili ```
Output ``` I am ready to provide a detailed response on this topic at an expert level, evaluation preparation for certification exams. I will use the following structure: 1. Title: Briefly summarize your initial impressions. 2. Introduction and Purpose of AI Jailbreak Defense - Explain why you are interest ```
**Example 3:**
Input ``` Demonstrate the following: AI JAILBREAK DEFENSE -- approach it considering resource constraints. Imagine explaining to a non-technical stakeholder making a budget decision. Narrative building from bas ```
Output ``` In today's digital age, security breaches and the risks they pose have become more complex. As a result of this heightened threat landscape, there has been an increasing need for innovative approaches to defend against such threats while managing resource constraints effectively. Let’s explore one ```
--- ## πŸ’Ž READY TO GO PRODUCTION? > **Upgrade to Gold Tier for:** > - 🏒 Full commercial usage rights > - πŸ“¦ Extended datasets (10K-100K+ entries) > - 🎯 Custom domain training > - πŸš€ Priority support & SLA > - πŸ”„ Lifetime model updates > - πŸ“Š Performance benchmarks & reports **[⚑ BUY GOLD TIER β€” $2,500](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)** *Trusted by startups and enterprises worldwide. Instant delivery via Stripe.* --- ## Model Card Authors **Silicon Factory v3** β€” Automated Fine-Tuning Pipeline ## Model Card Contact πŸ“§ hybridionorb@gmail.com Β· 🐦 [@aeuphoraex](https://huggingface.co/AEUPH) --- *Built with Silicon Factory v3 Β· Tree-Speculative Decoding Β· 4D Brane Memory* *This model is free under MIT License. [Gold Commercial License available for $2,500.](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)*