This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the CySecBench dataset. It is designed to classify cybersecurity-related prompts into one of ten attack categories.

Model Description

Developed by: Erfan Sadeghiii
Model type: A 7B parameter causal language model fine-tuned using QLoRA.
Language(s) (NLP): English
Finetuned from model: Qwen/Qwen2.5-7B-Instruct

Intended Uses & Limitations

This model is intended for research and educational purposes in the field of cybersecurity. It is designed to categorize prompts, not to generate harmful content.

How to Use

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load base model and the adapter
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct", device_map="auto")
model = PeftModel.from_pretrained(model, "ErfanSadeghiii/peft-cysec-qwen2.5")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

def predict(prompt):
    formatted_prompt = f"<start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model\n"
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda")
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=20, do_sample=False, temperature=0.0)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    category = response.split("<start_of_turn>model")[-1].strip()
    return category.split("<end_of_turn>")[0].strip()

ErfanSadeghiii/peft-cysec-qwen2.5

Model Card for peft-cysec-qwen2.5

Model Description

Intended Uses & Limitations

How to Use

ErfanSadeghiii/
peft-cysec-qwen2.5

Model Card for `peft-cysec-qwen2.5`