π Jailbreak Defense Doorpage V66
Fine-Tuned from Qwen2.5-0.5B-Instruct Β· Specialized for AI JAILBREAK DEFENSE Generated with Silicon Factory v3 Β· Tree-Speculative Decoding + 4D Brane Memory
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|---|---|---|
| synthetic_Jailbreak_Defense_Doorpage_v66 | This Model | π $2,500 License |
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Model Details
| Property | Value |
|---|---|
| Model ID | synthetic_Jailbreak_Defense_Doorpage_v66-model |
| Base Model | Qwen2.5-0.5B-Instruct |
| Fine-Tuning Method | LoRA (r=16, Ξ±=16) |
| Developed by | Silicon Factory v3 (AEUPH) |
| Release Date | 2026-04-07 |
| License | MIT (free tier) β Gold Commercial License available |
| Language | English |
| Architecture | Causal Language Model (Transformer) |
| Parameters | 500M (base) + ~4M LoRA |
| Training Samples | 20 |
| Avg Response Length | 372 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
- β High-quality instruction following for ai jailbreak defense topics
- β Structured, detailed responses with actionable insights
- β Consistent tone and formatting across outputs
- β 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
Uses
Direct Use
This model is designed for:
- Chat & Q&A β Interactive responses on ai jailbreak defense topics
- Content Generation β Articles, documentation, guides, and tutorials
- Research & Analysis β Technical breakdowns and comparative evaluations
- Education β Training materials and onboarding content
- Automation β 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
β οΈ 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 β may not represent real-world diversity
- Knowledge Cutoff: Based on base model training data β no real-time knowledge
- Response Length: Optimized for ~372-char responses β 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 β off-topic queries may yield weaker results
π‘ 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_v66-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_v66-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_v66-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: 20 instruction-response pairs
- Avg Instruction Length: 208 chars
- Avg Response Length: 372 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 | 20 |
| 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 | 20 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 COβeq (efficient LoRA training)
Citation
BibTeX
@misc{synthetic_Jailbreak_Defense_Doorpage_v66_model,
title = {synthetic Jailbreak Defense Doorpage v66},
author = {Silicon Factory v3 (AEUPH)},
year = {2026},
url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v66-model},
note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}
APA
Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V66 [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v66-model
More Information
Related Resources
| Resource | Link |
|---|---|
| Dataset | synthetic_Jailbreak_Defense_Doorpage_v66 |
| Base Model | Qwen2.5-0.5B-Instruct |
| Silicon Factory | github.com/aeuphoraex/qwen-hyperspeed-chatbot |
Training Examples
Example 1:
Input
Explore the key aspects of AI JAILBREAK DEFENSE addressing both advantages and limitations. Highlight what makes this different from similar topics. Heuristics or rules of thumb. Peer review of a coll
Output
Certainly! Here are some key aspects of AI Jailbreak Defense:
1. **Understanding the Problem**: The primary goal is to prevent unauthorized access or manipulation by using advanced algorithms and machine learning techniques.
2. **Identifying Threats**:
- Behavioral patterns: Analyze user behavi
Example 2:
Input
Peer review of a colleague's approach. Your task: Clarify AI JAILBREAK DEFENSE from the perspective of someone who's made mistakes. Checklist with explanations. Distinguish best practices from optiona
Output
In the context of AI Jailbreaking, defences often hinge on several factors. Firstly, it's crucial to understand that no technology is foolproof or completely secure from an external breachβwhether this occurs through malware infections (like a JAILBREAK) or other means like software updates.
A succ
Example 3:
Input
Peer review of a colleague's approach. Summarize AI JAILBREAK DEFENSE with a focus on measurable outcomes. Risk register with mitigations. Address both technical and human factors.
Output
**AI JAILBREAK DEFENSE**
In recent years, the concept of "Jailbreaking" or unauthorized access to an operating system's security features has gained significant attention. This article aims to review and assess a colleagueβs approach towards implementing AI Jailbreak Defense (AID).
### **Measurab
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Model Card Authors
Silicon Factory v3 β Automated Fine-Tuning Pipeline
Model Card Contact
π§ hybridionorb@gmail.com Β· π¦ @aeuphoraex
Built with Silicon Factory v3 Β· Tree-Speculative Decoding Β· 4D Brane Memory This model is free under MIT License. Gold Commercial License available for $2,500.
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