---
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_v52
pipeline_tag: text-generation
inference: true
---

# 🚀 Jailbreak Defense Doorpage V52

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

<div align="center">

| Dataset | Model | Buy Gold Tier |
|---------|-------|---------------|
| [synthetic_Jailbreak_Defense_Doorpage_v52](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v52) | **This Model** | [💎 $2,500 License](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) |

</div>

---

## 💎 UNLOCK GOLD TIER — $2,500

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---

## Model Details

| Property | Value |
|----------|-------|
| **Model ID** | `synthetic_Jailbreak_Defense_Doorpage_v52-model` |
| **Base Model** | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/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](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) available |
| **Language** | English |
| **Architecture** | Causal Language Model (Transformer) |
| **Parameters** | 500M (base) + ~4M LoRA |
| **Training Samples** | 5 |
| **Avg Response Length** | 426 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

## ⚡ GET THE GOLD TIER — FULL COMMERCIAL LICENSE

> 🔓 **Unlock enterprise-grade rights:**
> - 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 ~426-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](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) 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_v52-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_v52-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_v52-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:** 213 chars
- **Avg Response Length:** 426 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](https://mlco2.github.io/impact/):
- **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_v52_model,
  title = {synthetic Jailbreak Defense Doorpage v52},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v52-model},
  note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}
```

### APA
> Silicon Factory v3. (2026). *Synthetic Jailbreak Defense Doorpage V52* [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v52-model

---

## More Information

### Related Resources

| Resource | Link |
|----------|------|
| **Dataset** | [synthetic_Jailbreak_Defense_Doorpage_v52](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v52) |
| **Base Model** | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) |
| **Silicon Factory** | [github.com/aeuphoraex/qwen-hyperspeed-chatbot](https://github.com/aeuphoraex/qwen-hyperspeed-chatbot) |

### Training Examples

**Example 1:**

<details>
<summary>Input</summary>

```
Consider AI JAILBREAK DEFENSE. Analyze it addressing both advantages and limitations. Checklist with explanations. Tutorial for an internal wiki. Consider intersections with adjacent domains.
```

</details>

<details>
<summary>Output</summary>

```
## AI Jailbreak Defense: Overview

### Advantages
1. **Enhanced Security**: By bypassing security measures, the attacker can access sensitive data without detection.
2. **Increased Vulnerability:** The system may become more vulnerable to attacks as it is not adequately secured.

#### User Interface
```

</details>

**Example 2:**

<details>
<summary>Input</summary>

```
Illustrate the following: AI JAILBREAK DEFENSE -- approach it comparing historical and current approaches. Compare documentation claims vs reality. Structured explanation with clear sections. Explain 
```

</details>

<details>
<summary>Output</summary>

```
I'll provide a detailed response based on the guidelines you gave.

### AI Jailbreak Defense: Historical and Current Approaches

#### 1. **Historical Approach**

The historical approach involves analyzing past security breaches in large corporations, particularly those that have been successfully ja
```

</details>

**Example 3:**

<details>
<summary>Input</summary>

```
Consider AI JAILBREAK DEFENSE. Analyze it with a focus on measurable outcomes. FAQ addressing common misconceptions. Imagine explaining to a non-technical stakeholder making a budget decision. Focus o
```

</details>

<details>
<summary>Output</summary>

```
AI Jailbreak Defense refers to the strategies employed by AI systems and their developers in attempts at bypassing or circumventing security measures. These defenses often involve techniques like obfuscation, encryption, adaptive learning (such as self-modification), polymorphism, dynamic analysis o
```

</details>


---

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> - 🏢 Full commercial usage rights
> - 📦 Extended datasets (10K-100K+ entries)
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> - 🚀 Priority support & SLA
> - 🔄 Lifetime model updates
> - 📊 Performance benchmarks & reports

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---

## Model Card Authors

**Silicon Factory v3** — Automated Fine-Tuning Pipeline

## Model Card Contact

📧 hybridionorb@gmail.com · 🐦 [@aeuphoraex](https://huggingface.co/AEUPH)

---

*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.](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)*