README.md

---

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_v53
pipeline_tag: text-generation
inference: true
---


# 🚀 Jailbreak Defense Doorpage V53

> **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_v53](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v53) | **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_v53-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** | 421 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

**[💳 BUY GOLD TIER — $2,500](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)**

---

## 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](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_v53-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_v53-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_v53-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

| 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_v53_model,
  title = {synthetic Jailbreak Defense Doorpage v53},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v53-model},
  note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}
```



### APA

> Silicon Factory v3. (2026). *Synthetic Jailbreak Defense Doorpage V53* [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v53-model



---



## More Information



### Related Resources



| Resource | Link |

|----------|------|

| **Dataset** | [synthetic_Jailbreak_Defense_Doorpage_v53](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v53) |

| **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>



```
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
```



</details>



<details>

<summary>Output</summary>



```
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.
```



</details>



**Example 2:**



<details>

<summary>Input</summary>



```
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
```



</details>



<details>

<summary>Output</summary>



```
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
```



</details>



**Example 3:**



<details>

<summary>Input</summary>



```
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 
```



</details>



<details>

<summary>Output</summary>



```
---

# 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-
```



</details>





---



## 💎 READY TO GO PRODUCTION?



> **Upgrade to Gold Tier for:**

> - 🏢 Full commercial usage rights

> - 📦 Extended datasets (10K-100K+ entries)

> - 🎯 Custom domain training

> - 🚀 Priority support & SLA

> - 🔄 Lifetime model updates

> - 📊 Performance benchmarks & reports



**[⚡ BUY GOLD TIER — $2,500](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00)**



*Trusted by startups and enterprises worldwide. Instant delivery via Stripe.*



---



## 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)*