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_v58
pipeline_tag: text-generation
inference: true
---

# 🚀 Jailbreak Defense Doorpage V58

> **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_v58](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58) | **This Model** | [💎 $2,500 License](https://buy.stripe.com/3cIcN4gzC7lXfuH49s7wA00) |

</div>

---

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---

## Model Details

| Property | Value |
|----------|-------|
| **Model ID** | `synthetic_Jailbreak_Defense_Doorpage_v58-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** | 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

## ⚡ 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 ~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](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_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](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_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](https://huggingface.co/datasets/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v58) |
| **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. Synthesize it from both theoretical and practical standpoints. Detailed breakdown with specific examples. Guide for someone transitioning from a different field. Conside
```

</details>

<details>
<summary>Output</summary>

```
**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
```

</details>

**Example 2:**

<details>
<summary>Input</summary>

```
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
```

</details>

<details>
<summary>Output</summary>

```
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
```

</details>

**Example 3:**

<details>
<summary>Input</summary>

```
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
```

</details>

<details>
<summary>Output</summary>

```
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 
```

</details>


---

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> - 🏢 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)**

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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)*