⚠️ Note: This repository contains the LoRA adapter weights only. It is not a standalone model. You must load it alongside the base model Qwen/Qwen3.6-27B.

This LoRA adapter is fine-tuned to enhance the model's capabilities specifically in the Cybersecurity domain. It was efficiently trained using Unsloth.

🛡️ Domain Focus: Cybersecurity

This fine-tuned adapter improves the base model's performance in:

Threat intelligence analysis
Log analysis and incident response
General cybersecurity knowledge retrieval

💻 How to Merge and Save (Unsloth)

You can easily download this LoRA adapter, merge it with the base model, and save it as a complete 16-bit model using Unsloth.

Make sure you have Unsloth installed, then run the following Python script:

python

from unsloth import FastLanguageModel

# 1. Point model_name directly to this Hugging Face repository!
# Unsloth will automatically read the config and load the base model together with the LoRA.
LORA_DIR = "CTCT-CT2/Changeway-Qwen3.6-27B-LoRA-V1" 

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = LORA_DIR, 
    max_seq_length = 8192,
    dtype = None,
    load_in_4bit = False, # Note: It is best to disable 4bit when merging, load in 16bit mode
    device_map = "auto",  # [!] Let it automatically take over or force allocation
)

# 2. Merge the LoRA into the Base model and save as a new full model
MERGED_DIR = "./qwen-27b-cybersec-merged"
print(f"Merging and saving to {MERGED_DIR} ...")

model.save_pretrained_merged(MERGED_DIR, tokenizer, save_method="merged_16bit")
print("Merge completed successfully!")

🚀 Training Details

Base Model: Qwen/Qwen3.6-27B
Training Framework: Unsloth
Method: LoRA (Low-Rank Adaptation)