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_v69 pipeline_tag: text-generation inference: true

🚀 Jailbreak Defense Doorpage V69

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

Property	Value
Model ID	`synthetic_Jailbreak_Defense_Doorpage_v69-model`
Base Model	Qwen2.5-0.5B-Instruct
Fine-Tuning Method	LoRA (r=16, α=16)
Developed by	Silicon Factory v3 (AEUPH)
Release Date	2026-04-07
License	MIT (free tier) — Gold Commercial License available
Language	English
Architecture	Causal Language Model (Transformer)
Parameters	500M (base) + ~4M LoRA
Training Samples	5
Avg Response Length	910 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

✅ 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 ~910-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_v69-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_v69-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_v69-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: 219 chars
Avg Response Length: 910 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:

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_v69_model,
  title = {synthetic Jailbreak Defense Doorpage v69},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v69-model},
  note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}

APA

Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V69 [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v69-model

More Information

Related Resources

Resource	Link
Dataset	synthetic_Jailbreak_Defense_Doorpage_v69
Base Model	Qwen2.5-0.5B-Instruct
Silicon Factory	github.com/aeuphoraex/qwen-hyperspeed-chatbot

Training Examples

Example 1:

Investigate the following: AI JAILBREAK DEFENSE -- approach it from both theoretical and practical standpoints. Respond to a skeptical colleague doubting the importance. Risk register with mitigations

---

**AI Jailbreak Defense: A Comprehensive Approach**

As a member of the AI community, we are often confronted with questions about how to protect our systems from potential threats. In particular, this paper aims at exploring and defending against an artificial intelligence (AI) jailbreaker appr

Example 2:

Frame as advice to your past self when first starting. Your task: Describe AI JAILBREAK DEFENSE using concrete examples at each step. Structured explanation with clear sections. Include at least one c

To provide a detailed response, I will first discuss the current state of AI Jailbreak Defense. This is an ongoing process that involves understanding and mitigating threats while maintaining user privacy.

1) Identify threat: The primary goal in any security measure against advanced malware or cybe

Example 3:

Troubleshooting guide for operations staff. Evaluate AI JAILBREAK DEFENSE with a focus on measurable outcomes. Lessons-learned document with takeaways. Address both technical and human factors.

Welcome to the Troubleshooting Guide for Operations Staff! We will be evaluating AI Jailbreak Defense with a focus on measurable outcomes. Our primary objective is to help you understand how we can improve your operations and reduce operational risks.

1. What are the key components of our evaluatio

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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.