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license: apache-2.0
library_name: peft
pipeline_tag: text-generation
base_model: Qwen/Qwen3.6-27B
tags:
- unsloth
- lora
- peft
- finetune
- cybersecurity
- security
---
# Changeway-Qwen3.6-27B-V1
⚠️ **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](https://huggingface.co/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](https://github.com/unslothai/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](https://huggingface.co/Qwen/Qwen3.6-27B)
* **Training Framework:** [Unsloth](https://github.com/unslothai/unsloth)
* **Method:** LoRA (Low-Rank Adaptation) |