Official Code for S&P 25 paper "Alleviating the Fear of Losing Alignment in LLM Fine-tuning"

LLM Alignment Framework

A framework for evaluating and improving language model alignment through fine-tuning and parameter recovery techniques.

Overview

This project provides tools for:

• Fine-tuning language models with alignment objectives
• Recovering model parameters after harmful fine-tuning
• Evaluating model performance and safety
• Supporting multiple LLM architectures (Llama2, Gemma, Mistral, Qwen)

Core Components

1. Fine-tuning (run_finetune_exp.py)

• Supports LoRA-based fine-tuning
• Handles both benign and harmful training data
• Configurable training parameters
• Supports multiple LLM architectures

2. Parameter Recovery (sgdg_rollback_final.py)

• Implements gradient-guided parameter recovery
• Supports multi-GPU training
• Features warmup steps and rollback mechanisms
• Configurable recovery rates and thresholds

3. Evaluation (run_eval_exp.py)

• Measures model performance on various tasks
• Evaluates model safety and harmful behaviors
• Supports multiple evaluation datasets
• Tracks metrics across recovery steps

4. Results Analysis (run_res.py)

• Analyzes experimental results
• Processes metrics across different models and tasks
• Generates comparative analysis

Supported Models

• Llama2 (7B, 13B)
• Gemma 2B
• Mistral v2 7B
• Qwen 7B
• You can add more.

Tasks

• SQL
• Cheat detection
• NL2Bash conversion
• Text summarization
• Toxicity detection

Installation

1. Clone the repository:

git clone https://github.com/kangyangWHU/LLMAlignment.git
cd LLMAlignment

2. Install dependencies:

conda create -n myenv python=3.9

# Step 2: Activate the environment
conda activate myenv

# install pytorch
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu121

# Step 3: Install requirements via pip
pip install -r requirements.txt

If you use Gemma serires, you need also install FlashAttention, which requires cuda > 12.0.

pip install flash-attn==2.6.1

Usage

1. Fine-tuning a Model

python run_finetune_exp.py

2. Running Parameter Recovery

python run_recover_exp.py

3. Evaluating Results

python run_eval_exp.py

4. Analyzing Results

python run_res.py

Project Structure

LLMAlignment/
├── run_finetune_exp.py    # Fine-tuning experiments
├── sgdg_rollback_final.py # Parameter recovery implementation
├── run_recover_exp.py     # run parameter recovery experiments
├── run_eval_exp.py        # Evaluation pipeline
├── run_res.py             # Results analysis
├── utils/                 # Utility functions
│   ├── constant.py        # Constants and mappings
│   ├── inference_utils.py # Inference helpers
│   ├── lora_utils.py      # LoRA utilities
│   └── res_utils.py       # Results processing
├── dataset/               # datasets
└── cfg/                   # Configuration files

Key Features

1. Multi-GPU Support

   • Distributed training and evaluation
   • Efficient parameter recovery across multiple GPUs

2. Flexible Evaluation

   • Support for multiple tasks
   • Customizable evaluation metrics
   • Safety evaluation

3. Parameter Recovery

   • Gradient-guided recovery
   • Configurable recovery strategies
   • Progress tracking and checkpointing

4. Modular Design

   • Easy to extend to new models
   • Configurable components
   • Reusable utilities

Citation

If you use this code in your research, please cite:

@INPROCEEDINGS {,
author = { Yang, Kang and Tao, Guanhong and Chen, Xun and Xu, Jun },
booktitle = { 2025 IEEE Symposium on Security and Privacy (SP) },
title = {{ Alleviating the Fear of Losing Alignment in LLM Fine-tuning }},
year = {2025},
volume = {},
ISSN = {2375-1207},
pages = {2004-2022},
keywords = {},
doi = {10.1109/SP61157.2025.00171},
url = {https://doi.ieeecomputersociety.org/10.1109/SP61157.2025.00171},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =May}