A fine-tuned LoRA adapter on top of Qwen/Qwen2.5-0.5B-Instruct, specialized in cybersecurity tasks including threat detection, vulnerability analysis, and security Q&A.

Model Details

Developed by: mohan188n
Base Model: Qwen/Qwen2.5-0.5B-Instruct
Model type: Causal Language Model (LoRA fine-tune)
Language: English
License: Apache 2.0
Fine-tuning method: LoRA (PEFT)
Framework versions: PEFT 0.15.2

How to Use

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "mohan188n/CyberShield-Qwen-0.5B-LoRA"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id)
model = PeftModel.from_pretrained(model, adapter_id)

prompt = "What is a SQL injection attack?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data: Cybersecurity-focused dataset
Training Regime: fp16 mixed precision
Fine-tuning Method: LoRA (Low-Rank Adaptation)

Intended Use

This model is designed for cybersecurity-related natural language tasks:

Explaining attack types and vulnerabilities
Answering security Q&A
Assisting in security education and research

Repository

GitHub: Mohan007N/CyberShield-GPT