92 lines
2.2 KiB
Markdown
92 lines
2.2 KiB
Markdown
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---
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base_model: Qwen/Qwen2.5-Coder-3B
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tags:
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- gguf
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- llama.cpp
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- pentesting
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- cybersecurity
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- jetson
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- quantized
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---
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# Qwen2.5-Coder-3B Pentest - GGUF
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GGUF quantizations of [fawazo/qwen2.5-coder-3b-pentest](https://huggingface.co/fawazo/qwen2.5-coder-3b-pentest) optimized for **Jetson Orin Nano (8GB)**.
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## Model Description
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An AI pentesting assistant fine-tuned on 150K+ cybersecurity examples covering:
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- OWASP Top 10 vulnerabilities
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- MITRE ATT&CK framework
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- API security testing
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- Web application penetration testing
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**Output Format:** JSON for automation
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## Quantizations
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| File | Size | RAM Needed | Recommended For |
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|------|------|------------|-----------------|
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| `qwen2.5-coder-3b-pentest-q4_k_m.gguf` | ~1.8GB | ~3GB | **Jetson Orin Nano 8GB** |
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| `qwen2.5-coder-3b-pentest-q5_k_m.gguf` | ~2.1GB | ~4GB | Better quality |
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| `qwen2.5-coder-3b-pentest-q8_0.gguf` | ~3.4GB | ~5GB | Best quality |
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| `qwen2.5-coder-3b-pentest-f16.gguf` | ~6GB | ~8GB | Full precision |
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## Usage on Jetson
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### With Ollama
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```bash
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# Download Q4_K_M (recommended for 8GB)
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huggingface-cli download fawazo/qwen2.5-coder-3b-pentest-gguf qwen2.5-coder-3b-pentest-q4_k_m.gguf
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# Create Modelfile
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cat > Modelfile << 'EOF'
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FROM ./qwen2.5-coder-3b-pentest-q4_k_m.gguf
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SYSTEM """You are an expert penetration testing AI assistant. Analyze web traffic and respond with JSON:
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{"action": "report|request|command|complete", ...}"""
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PARAMETER temperature 0.3
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PARAMETER num_ctx 2048
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EOF
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# Create and run
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ollama create pentest-agent -f Modelfile
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ollama run pentest-agent
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```
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### With llama.cpp
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```bash
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./llama-cli -m qwen2.5-coder-3b-pentest-q4_k_m.gguf -ngl 99 -c 2048 -p "Analyze this request..."
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```
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## Example Usage
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**Input:**
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```
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Analyze this HTTP exchange:
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REQUEST: GET /api/users?id=1
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RESPONSE: {"user": "admin", "role": "administrator"}
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```
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**Output:**
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```json
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{
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"action": "request",
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"method": "GET",
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"path": "/api/users?id=2",
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"reasoning": "Testing for IDOR - checking if user IDs are enumerable"
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}
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```
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## Training Details
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- **Base:** Qwen/Qwen2.5-Coder-3B
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- **Method:** SFT with LoRA (r=32)
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- **Dataset:** 150K+ combined examples from Trendyol, Fenrir v2.0, pentest-agent
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- **Frameworks:** OWASP, MITRE ATT&CK, NIST CSF
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## License
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Apache 2.0 (inherits from base model and training datasets)
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