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.

(Please note that the complete code will be published after the acceptance of the paper. Thank you for your patience.)

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 are currently releasing the Lora-weights model, which is available for download. Please note that the complete code will be released after the acceptance of our paper. Thank you for your patience.

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:

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.
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:

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

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

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.

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.

Coming soon ...