Final Project

In this repository, we guide you to run this projec. A complete run including model downloading, trian and inference, which is time-consuming. I have provided the train & inference log file for your convenience to check. logfile

Datasets: AWF, DF, DC, USTC, CSTNet-tls

Openset methods

• K-LND methods

Steps

First, clone the git repo and install the requirements.

git clone https://github.com/zhanglejun02/ECE-GY-9313-Telecom-Network-Final-Project.git
pip install -r requirements.txt

Next, download the dataset and place it in the data directory.

gdown https://drive.google.com/uc?id=1-MVfxyHdQeUguBmYrIIw1jhMVSqxXQgO
unzip data.zip 

For GPT2 and LLaMA, do data preprocess Here, the dataset name should be DF, AWF, DC, USTC, or CSTNet, and the model_name should be either GPT2 or LLaMA.

python3 data_preprocess.py --data_path ./data --dataset <dataset_name> --model <model_name>

GPT-2 Fine-tunning

To fine-tune the model, run the suitable code for the dataset:

python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 60  --dataset DF
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 200  --dataset AWF
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 4  --dataset DC
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 12  --dataset USTC
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 75  --dataset CSTNet

To evaluate, run the suitable code for the dataset:

python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 60 --K_number 30 --TH_value 0.8 --dataset DF
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 200 --K_number 50 --TH_value 0.9 --dataset AWF
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 4 --K_number 4 --TH_value 0.9 --dataset DC
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 12 --K_number 5 --TH_value 0.8 --dataset USTC
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 5 --num_labels 75 --K_number 20 --TH_value 0.8 --dataset CSTNe

LLaMA Fine-tuning

To fine-tune the LLaMA model and obtain results, run the following commands accordingly.

python3 run_LLaMA.py --max_len 1024 --batch_size 6 --epochs 3 --num_labels 60  --dataset DF --K_number 30 --TH_value 0.85
python3 run_LLaMA.py --max_len 1024 --batch_size 6 --epochs 3 --num_labels 200  --dataset AWF --K_number 50 --TH_value 0.85
python3 run_LLaMA.py --max_len 1024 --batch_size 8 --epochs 2 --num_labels 4  --dataset DC --K_number 4 --TH_value 0.85
python3 run_LLaMA.py --max_len 1024 --batch_size 8 --epochs 2 --num_labels 12  --dataset USTC --K_number 5 --TH_value 0.85
python3 run_LLaMA.py --max_len 1024 --batch_size 6 --epochs 2 --num_labels 75  --dataset CSTNet --K_number 20 --TH_value 0.85
python3 run_LLaMA.py --max_len 1024 --batch_size 6 --epochs 2 --num_labels 60  --dataset IoT --K_number 30 --TH_value 0.85
python3 run_LLaMA.py --max_len 1024 --batch_size 8 --epochs 2 --num_labels 10  --dataset ISCX --K_number 5 --TH_value 0.9

Qwen3 Fine-tunning

do data preprocess

bash run_data_preprocess.sh

download [Qwen3-30B-A3B-Instruct-2507] (https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)

change the QWEN3_MODEL_PATH to your local path in file train_lora.py

Then

bash run_lora.sh <dataset_name>

Mistral Fine-tunning

download [Mistral-Nemo-Instruct-2407] (https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)

change the QWEN3_MODEL_PATH to your local path in file train_lora.py

Then

bash run_lora.sh <dataset_name>

Gemma Fine-tunning

download [Gemma-7b] (https://huggingface.co/google/gemma-7b)

change the QWEN3_MODEL_PATH to your local Gemma Model path in file train_lora.py

Then

bash run_lora.sh <dataset_name>