Text Generation
PEFT
Safetensors
English
cybersecurity
web-security
penetration-testing
defensive-security
lora
qwen
conversational
Instructions to use jabuzarqa/web-pentester-qwen-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jabuzarqa/web-pentester-qwen-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "jabuzarqa/web-pentester-qwen-lora") - Notebooks
- Google Colab
- Kaggle
Web Pentester Qwen LoRA
This is a LoRA fine-tuned adapter based on Qwen/Qwen2.5-1.5B-Instruct.
Intended Use
This model is intended for authorized and defensive web security testing tasks, such as:
- Summarizing web vulnerability findings
- Generating pentest-style report items
- Classifying severity
- Writing impact and remediation recommendations
- Explaining scanner outputs from authorized assessments
Safety Notice
This model should only be used for legal and authorized security testing. It should not be used to attack real systems without permission.
Output Format
The model is designed to return structured findings such as:
{
"vulnerability_type": "",
"severity": "Critical|High|Medium|Low|Info",
"location": "",
"impact": "",
"evidence": "",
"remediation": ""
}
Base Model
Base model: Qwen/Qwen2.5-1.5B-Instruct
Training Method
Fine-tuned using LoRA / PEFT.
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