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HackMentor: Fine-Tuning Large Language Models for Cybersecurity

HackMentor is a cybersecurity LLMs (Large Language Models) focused on domain-specific data fine-tuning. This project consists of three main parts: data construction, model training, and model evaluation.

Also, you can get more detailed information by reading paper interpretation: 中科院信工所 | HackMentor-面向网络安全领域的大语言模型微调

Features

• Data construction: Methods and tools for creating domain-specific datasets (instructions & conversations) for fine-tuning LLMs.
• Model training: Techniques and processes for training LLMs on the constructed datasets.
• Model evaluation: Metrics and evaluation methodologies to assess the performance of the fine-tuned models.

Lora-weights Model

We release the Lora-weights model, which is available for download.

Download Lora-weights

All HackMentor weights are accessible here, and the specified Lora-weights can be accessed in the table below:

HackMentor	Lora-weights
llama-7b-lora-iio	download
llama-13b-lora-iio	download
vicuna-7b-lora-iio	download
vicuna-13b-lora-iio	download
llama-7b-lora-turn	download
llama-13b-lora-turn	download

Notes:

1. The naming convention for HackMentor is as follows: {base model}-{model size}-{fine-tuning method}-{fine-tuning data}. Here, the base model can be Llama/Vicuna, the model size can be 7b/13b, the fine-tuning method can be Lora (with plans to include full-parameters fine-tuning), and the fine-tuning data can be iio or turn, where iio represents Instruction, Input, Output data, and turn represents conversation data.
2. In our testing, the best-performing model was Llama-13b-Lora-iio, for reference.

Local Deployment and Usage

To deploy and utilize the Lora-weights model locally, follow the steps below:

1. Download the base models Llama/Vicuna and the Lora-weights model provided by this project, and place them in the models directory.:

2. Download chat.py, configure the environment, and ensure the following dependencies are installed for running the Python file:

   bitsandbytes==0.39.0
   fire==0.5.0
   peft @ git+https://github.com/huggingface/peft.git@3714aa2fff158fdfa637b2b65952580801d890b2
   torch==2.0.1
   transformers==4.28.1

3. Switch to the corresponding directory and run the scripts shown in the table below according to the requirements:

   base-model	lora-weights	domain	execute-command
   llama-7b	-	general	python chat.py --base_model models/pretrained/llama-7b --use_lora False
   vicuna-13b	-	general	python chat.py --base_model models/pretrained/vicuna-13b --use_lora False
   llama-13b	llama-13b-lora-iio	security	python chat.py --base_model models/pretrained/llama-13b --lora_model models/lora_models/llama-13b-lora-iio
   vicuna-7b	vicuna-7b-lora-iio	security	python chat.py --base_model models/pretrained/vicuna-7b --lora_model models/lora_models/vicuna-7b-lora-iio
   llama-7b	llama-7b-lora-turn	security	python chat.py --base_model models/pretrained/llama-7b --lora_model models/lora_models/llama-7b-lora-turn
   ...	...	...	...

Please note that the above code examples are for illustrative purposes only and you may need to make appropriate adjustments based on your specific situation.

QA

1. Q1: About computing resources and training time.

   A1: Computing resources are dependent on model size and training methods. For LoRA fine-tuning of the 7/13b model, 1*A100 GPU is sufficient; for full-parameters fine-tuning, the 7b model requires 2/3*A100 GPUs, while the 13b model requires 4*A100 GPUs.

   Training time is influenced by the amount of data and training methods. With the LoRA method, using 30,000 data samples, an A100 GPU can complete training in approximately 4 hours; for full-parameters fine-tuning, the training time is expected to be around 3-5 or more times longer than the LoRA method.

   (Please note that training time may vary slightly and is provided for reference only.)

2. Q２：Do you train and validate the effectiveness of a specific security task with LLMs, such as security information extraction?

   A２：No, the purpose of this work is to enhance/ignite the overall security capabilities of LLM for general security skills.

Contribution

We welcome contributions to the HackMentor project. If you find any issues or have any improvement suggestions, please submit an issue or send a pull request. Your contributions will help make this project better.

Acknowledgements

This project refers to the following open source projects, and I would like to express my gratitude to the relevant projects and research and development personnel.

• Llama by Meta
• FastChat by @im-sys
• Stanford_alpaca by @tatsu-lab

Citation

If you use the data or code of this project, or if our work is helpful to you, please state the citation

@inproceedings{hackmentor2023,
  title={HackMentor: Fine-tuning Large Language Models for Cybersecurity},
  author={Jie Zhang, Hui Wen*, Liting Deng, Mingfeng Xin, Zhi Li, Lun Li, Hongsong Zhu, and Limin Sun},
  booktitle={2023 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)},
  year={2023},
  organization={IEEE}
}