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_v54 pipeline_tag: text-generation inference: true

🚀 Jailbreak Defense Doorpage V54

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 ~447-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_v54-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_v54-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_v54-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: 231 chars
• Avg Response Length: 447 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_v54_model,
  title = {synthetic Jailbreak Defense Doorpage v54},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v54-model},
  note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}

APA

Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V54 [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v54-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

Modern best practices in AI Jailbreaking Defense typically emphasize transparency, flexibility, and user control over the device's security features. This approach allows users to make informed decisions based on their needs rather than relying solely upon outdated or proprietary methods.

**Transpa

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

AI jailbreaking is a significant issue that can have serious implications, both technically and morally. It involves altering the operating system of an Android device to gain unauthorized access or control over it without permission.

**Technological Advantages**

1. **Enhanced Security**: By modif

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 It From a Beginner's Perspective

## Introduction
AI jailbreaking is an unethical practice that involves the unauthorized modification of computer systems to bypass security measures and gain access. The goal here is not only about preventing such breaches, but also 

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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.