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_v67 pipeline_tag: text-generation inference: true

🚀 Jailbreak Defense Doorpage V67

Fine-Tuned from Qwen2.5-0.5B-Instruct · Specialized for AI JAILBREAK DEFENSE Generated with Silicon Factory v3 · Tree-Speculative Decoding + 4D Brane Memory

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Model Details

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

• ✅ 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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• 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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Uses

Direct Use

This model is designed for:

• Chat & Q&A — Interactive responses on ai jailbreak defense topics
• Content Generation — Articles, documentation, guides, and tutorials
• Research & Analysis — Technical breakdowns and comparative evaluations
• Education — Training materials and onboarding content
• Automation — 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 — may not represent real-world diversity
• Knowledge Cutoff: Based on base model training data — no real-time knowledge
• Response Length: Optimized for ~390-char responses — 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 — off-topic queries may yield weaker results

💡 Recommendation: Always review outputs before deployment. For production use, obtain the Gold Tier license which includes QA guidelines and support.

How to Get Started

Python (Transformers + PEFT)

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_v67-model")
model = model.merge_and_unload()

# Generate
prompt = "Explain ai jailbreak defense in simple terms"
inputs = tokenizer(f"<im_start>user\n{prompt}\n<im_end>\n<im_start>assistant\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

from transformers import pipeline

pipe = pipeline("text-generation", model="AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67-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)

curl https://api-inference.huggingface.co/models/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67-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: 20 instruction-response pairs
• Avg Instruction Length: 217 chars
• Avg Response Length: 390 chars
• Category: mixed
• Focus: AI JAILBREAK DEFENSE
• Generation Method: Tree-Speculative Decoding (branch factor=5, depth=4) + 4D Brane Memory for consistency

Training Procedure

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

Summary

Environmental Impact

Estimated using ML Impact Calculator:

• Hardware: NVIDIA GPU (consumer-grade)
• Training Time: ~5-15 minutes
• Carbon Emitted: < 0.01 kg CO₂eq (efficient LoRA training)

Citation

BibTeX

@misc{synthetic_Jailbreak_Defense_Doorpage_v67_model,
  title = {synthetic Jailbreak Defense Doorpage v67},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67-model},
  note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}

APA

Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V67 [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v67-model

More Information

Related Resources

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 making a budget decision. Key principles with support

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 illustrate how these concepts have evolved.

Principle 1: **Resiliency Over Risk**: This principle sug

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 analysis with pros and cons. Address both technical

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 practices in AI jailing include several key strategies:

1. **Risk Assessment**: Before initiating any 

Example 3:

Describe the following: AI JAILBREAK DEFENSE -- approach it from a beginner's perspective. Write as documentation for a team inheriting your work. Detailed breakdown with specific examples. Reference 

# 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-friendly approach, we'll cover how you can safely obtain your own devices or modify others' for testing 

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• 📦 Extended datasets (10K-100K+ entries)
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Model Card Authors

Silicon Factory v3 — Automated Fine-Tuning Pipeline

Model Card Contact

📧 [email protected] · 🐦 @aeuphoraex

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