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_v59
  pipeline_tag: text-generation
  inference: true

🚀 Jailbreak Defense Doorpage V59

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 ~421-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_v59-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_v59-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_v59-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: 421 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_v59_model,
  title = {synthetic Jailbreak Defense Doorpage v59},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v59-model},
  note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}

APA

Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V59 [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v59-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

Artificial intelligence (AI) jailbreak defense is an approach designed to protect a system from attacks that exploit vulnerabilities in the AI component itself. The concept of "jailbreaking" refers to bypassing security measures by modifying components such as software, hardware drivers or firmware.

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

Welcome to this discussion on AI jailbreak defense. To start, let's consider the nature of modern best practices in protecting your data and ensuring that you have control over access points.

As an AI user with a strong preference for privacy, I should be aware of potential risks associated with ac

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: A Beginner's Approach

## What is an AI jailbreaker?

An **AI jailbraker** (or simply a "Jail Break") refers to the act of physically breaking into or accessing someone else’s device via software tools that mimic human actions and intent, typically using APIs from third-

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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.