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_v64-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	20
Avg Response Length	376 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 ~376-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_v64-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_v64-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_v64-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: 214 chars
Avg Response Length: 376 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 CO₂eq (efficient LoRA training)

Citation

BibTeX

bibtex

@misc{synthetic_Jailbreak_Defense_Doorpage_v64_model,
  title = {synthetic Jailbreak Defense Doorpage v64},
  author = {Silicon Factory v3 (AEUPH)},
  year = {2026},
  url = {https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v64-model},
  note = {Fine-tuned from Qwen2.5-0.5B-Instruct using LoRA}
}

APA

Silicon Factory v3. (2026). Synthetic Jailbreak Defense Doorpage V64 [Large language model]. Hugging Face. https://huggingface.co/AEUPH/synthetic_Jailbreak_Defense_Doorpage_v64-model

More Information

Related Resources

Resource	Link
Dataset	synthetic_Jailbreak_Defense_Doorpage_v64
Base Model	Qwen2.5-0.5B-Instruct
Silicon Factory	github.com/aeuphoraex/qwen-hyperspeed-chatbot

Training Examples

Example 1:

Input

Frame as lessons learned from experience rather than theory. Specifically, Assess AI JAILBREAK DEFENSE at an expert level. Comparative analysis with pros and cons. Address both technical and human fac

Output

This is a complex issue involving both technical and human factors. It's important to approach it with an expert level of analysis, as AI jailbreak defense involves multiple aspects such as cybersecurity techniques, software vulnerabilities, user behavior patterns, hardware capabilities (e.g., stora

Example 2:

Input

Unpack the following: AI JAILBREAK DEFENSE -- approach it highlighting what most people get wrong. Troubleshooting guide for operations staff. Key principles with supporting evidence. Explain adaptati

Output

**Unpack the following: AI JAILBREAK DEFENSE**

1. Approach it highlighting what most people get wrong:
    - Many individuals, especially those with no prior experience in digital security or programming tasks (e.g., software development), do not understand that implementing an effective solution r

Example 3:

Input

Decision framework for choosing between options. Specifically, Demonstrate AI JAILBREAK DEFENSE comparing historical and current approaches. Tutorial with prerequisites and steps. Distinguish best pra

Output

The decision framework for choosing between options involves comparing different approaches and selecting the best one based on various factors such as historical success, current research findings, ethical considerations, potential risks to privacy or security. In this context:

1. **Historical App

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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.