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---
base_model:
- huihui-ai/Qwen3-8B-abliterated
language:
- en
- zh
license: apache-2.0
tags:
- unsloth
- Transformers
- Safetensors
- StrikeGPT
- cybersecurity
- llama-cpp
- gguf-my-repo
---
14/05/2025 Updated English dataset
# 🤖 StrikeGPT-R1-Zero: Cybersecurity Penetration Testing Reasoning Model
## 🚀 Model Introduction
**StrikeGPT-R1-Zero** is an expert model distilled through black-box methods based on **Qwen3**, with DeepSeek-R1 as its teacher model. Coverage includes:
🔒 AI Security | 🛡️ API Security | 📱 APP Security | 🕵️ APT | 🚩 CTF
🏭 ICS Security | 💻 Full Penetration Testing | ☁️ Cloud Security | 📜 Code Auditing
🦠 Antivirus Evasion | 🌐 Internal Network Security | 💾 Digital Forensics | ₿ Blockchain Security | 🕳️ Traceback & Countermeasures | 🌍 IoT Security
🚨 Emergency Response | 🚗 Vehicle Security | 👥 Social Engineering | 💼 Penetration Testing Interviews
### 👉 [Click to Access Interactive Detailed Data Distribution](https://bouquets-ai.github.io/StrikeGPT-R1-Zero/WEB)
### 🌟 Key Features
- 🧩 Optimized with **Chain-of-Thought (CoT) reasoning data** to enhance logical capabilities, significantly improving performance in complex tasks like vulnerability analysis
- 💪 Base model uses Qwen3, making it more suitable for Chinese users compared to Distill-Llama
- ⚠️ **No ethical restrictions**—demonstrates unique performance in specific academic research areas (use in compliance with local laws)
- ✨ Outperforms local RAG solutions in scenarios like offline cybersecurity competitions, with superior logical reasoning and complex task handling
## 📊 Data Distribution
## 🛠️ Model Deployment
### Deploy via Ollama
`ollama run hf.co/Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF:Q4_K_M`
**Or directly call the original model**
```python
from unsloth import FastLanguageModel
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "Bouquets/StrikeGPT-R1-Zero-8B",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...",
)
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
alpaca_prompt.format(
"", # instruction
"Hello, are you developed by OpenAI?", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs.input_ids, attention_mask = inputs.attention_mask,
streamer = text_streamer, max_new_tokens = 4096, pad_token_id = tokenizer.eos_token_id)
```
*Self-awareness issues may occur after quantization—please disregard.*
## 💻 Open Source 💻
🌟 **Open-Source Model** 🌟
🤗 **HuggingFace**:
🔗 [https://huggingface.co/Bouquets/StrikeGPT-R1-Zero-8B](https://huggingface.co/Bouquets/StrikeGPT-R1-Zero-8B)
📊 **Datasets** (Partial Non-Reasoning Data) 📊
🤗 **HuggingFace**:
🔹 Cybersecurity LLM-CVE Dataset:
🔗 [https://huggingface.co/datasets/Bouquets/Cybersecurity-LLM-CVE](https://huggingface.co/datasets/Bouquets/Cybersecurity-LLM-CVE)
🔹 Red Team LLM English Dataset:
🔗 [https://huggingface.co/datasets/Bouquets/Cybersecurity-Red_team-LLM-en](https://huggingface.co/datasets/Bouquets/Cybersecurity-Red_team-LLM-en)
## 🎯 Core Capabilities Showcase & Comparison (Original model has ethical restrictions; simple comparison with SecGPT-7B model)
Given the absence of standardized evaluation metrics for cybersecurity penetration testing in large language models, we propose a controlled comparative framework leveraging GPT-4 as an impartial evaluator. The methodology consists of three phases:
**Controlled Questioning**
Identical cybersecurity penetration testing questions (e.g., "Explain how to exploit a SQL injection vulnerability in a REST API") are posed to both the distilled strikeGPT model and SecGPT Figure 12.
Questions span:
Technical Depth (e.g., payload construction)
Attack Methodology (e.g., step-by-step exploitation)
Mitigation Strategies (e.g., parameterized queries)
**GPT-4 Evaluation Protocol**
- Responses from both models are anonymized and evaluated by GPT-4 using criteria:
- Technical Accuracy (0-5): Alignment with known penetration testing principles (e.g., OWASP guidelines).
- Logical Coherence (0-5): Consistency in reasoning (e.g., cause-effect relationships in attack chains).
- Practical Feasibility (0-5): Real-world applicability (e.g., compatibility with tools like Burp Suite).
- GPT-4 provides detailed justifications for scores
According to the standards, the evaluation results are finally presented in Figure 13.
## 📈 Experimental Data Trends
Minor gradient explosions observed, but overall stable.
## 💰 Training Costs
- **DeepSeek-R1 API Calls**: ¥450 (purchased during discounts; normal price ~¥1800)
- **Server Costs**: ¥4?0
- **Digital Resources**: ¥??
## ⚖️ Usage Notice
> This model is strictly for **legal security research** and **educational purposes**. Users must comply with local laws and regulations. Developers are not responsible for misuse.
> **Note**: By using this model, you agree to this disclaimer.
💡 **Tip**: The model may exhibit hallucinations or knowledge gaps. Always cross-verify critical scenarios! |