--- 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)