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AVLLM

This guide provides detailed steps for fully fine-tuning the AVLLM model using the LLaMA Factory framework.
Follow these instructions to set up your environment, prepare your model and datasets, and perform fine-tuning and evaluations.

Environment Setup

  1. Clone the LLaMA Factory Repository

    Clone the repository to your home directory:

    git clone https://github.com/hiyouga/LLaMA-Factory.git

  2. Create and Activate a Conda Environment

    Create a new Conda environment named AVLLM and activate it:

    conda create -n AVLLM python=3.10  
conda activate AVLLM

  3. Install Dependencies

    Navigate to the LLaMA-Factory directory and install the required dependencies:

    cd LLaMA-Factory  
pip install -r requirements.txt

Model and Data Preparation

  1. Download the Model

    Download the TinyLlama model from Hugging Face and place it in the model_path directory:

  2. Prepare the Datasets

    Download your fine-tuning dataset AVLLM_train.json and testing dataset AVLLM_test.json. Load these datasets into the LLaMA-Factory/data directory and update the dataset_info.json file to include:

    "AVLLM-train": {
"file_name": "AVLLM_train.json"
},
"AVLLM-test": {
"file_name": "AVLLM_test.json"
}

Fine-Tuning

  1. Configure the Fine-Tuning Script

    In the llama_factory directory, create or modify the full-ft.sh script.

  2. Configure DeepSpeed
    Create or modify the deepspeed.json file.

  3. Execute the Fine-Tuning Script
    Run the fine-tuning script:

    sh full-ft.sh

Adversarial Attack Module Setup

  1. Install TextAttack
    Install TextAttack for generating adversarial examples:

    pip install textattack

  2. Generate Adversarial Examples
    Use the following command to generate adversarial examples for evaluation:

    sh generate_example.sh

  3. Prepare Evaluation Data

    -Store the generated adversarial examples as attack_example.csv.
    -Process this file using data_processing.py to format it for AVLLM inference.
    -Load the resulting evaluation.json into the LLaMA-Factory/data directory and update dataset_info.json:

    "AVLLM-evaluation": {
"file_name": "evaluation.json"
}

  4. Run Inference and Evaluation

    Use the predict.sh script for inference, and then evaluate the ASR with evaluation.py.

    sh predict.sh  
python evaluation.py

Patch Module Setup

  1. Add Custom Module
    Place your module in textattack/constraints/semantics/tinyllama.py and update textattack/attack_args.py to include your module in the CONSTRAINT_CLASS_NAMES: 
    "tinyllama": "textattack.constraints.semantics.Tinyllama"
  2. API Call Setup
    Set up an API call by running server.py and adjust the address in tinyllama.py accordingly. 
    python server.py
  3. Run Attack Command
    Execute the following command to run your attack module: 
    sh patch.sh