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_v58-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	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

✅ 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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Access to extended datasets (100K+ entries)

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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 ~415-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)

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"<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

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:

Hardware: NVIDIA GPU (consumer-grade)
Training Time: ~5-15 minutes
Carbon Emitted: < 0.01 kg CO₂eq (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
Base Model	Qwen2.5-0.5B-Instruct
Silicon Factory	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

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Model Card Authors

Silicon Factory v3 — Automated Fine-Tuning Pipeline

Model Card Contact

📧 hybridionorb@gmail.com · 🐦 @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.