This repository contains the code and released models for our paper SimPO: Simple Preference Optimization with a Reference-Free Reward. We propose a simpler and more effective preference optimization algorithm than DPO (Direct Preference Optimization) without using a reference model. SimPO outperforms DPO and its latest variants across AlpacaEval 2, MT-Bench, and Arena-Hard benchmarks under various settings.

• SimPO: Simple Preference Optimization with a Reference-Free Reward
  • Released Models
  • Install Requirements
  • Training scripts
  • Evaluation
  • Bugs or Questions?
  • Citation

Below is the full list of models that we evaluate in our preprint.

Please refer to the generate.py script for detailed instructions on loading the model with the appropriate chat template.

Our codebase is built upon the alignment-handbook repo. The following steps will guide you through the installation process.

First, create a Python virtual environment using e.g. Conda:

conda create -n handbook python=3.10 && conda activate handbook

Next, install PyTorch v2.2.2. Since this is hardware-dependent, we direct you to the PyTorch Installation Page.

You can then install the remaining package dependencies of alignment-handbook as follows:

git clone https://github.com/huggingface/alignment-handbook.git
cd ./alignment-handbook/
python -m pip install .

You will also need Flash Attention 2 installed, which can be done by running:

python -m pip install flash-attn --no-build-isolation

We provide four training config files for the four training setups reported in our paper. The training config is set for 8xH100 GPUs. You may need to adjust num_processes and per_device_train_batch_size based on your computation environment.

• Mistral-Base:

ACCELERATE_LOG_LEVEL=info accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml scripts/run_simpo.py training_configs/mistral-7b-base-simpo.yaml

• Mistral-Instruct:

ACCELERATE_LOG_LEVEL=info accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml scripts/run_simpo.py training_configs/mistral-7b-instruct-simpo.yaml

• Llama3-Base:

ACCELERATE_LOG_LEVEL=info accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml scripts/run_simpo.py training_configs/llama-3-8b-base-simpo.yaml

• Llama3-Instruct:

ACCELERATE_LOG_LEVEL=info accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml scripts/run_simpo.py training_configs/llama-3-8b-instruct-simpo.yaml

We follow the official implementation for evaluation on AlpacaEval 2, Arena-Hard, and MT-Bench, as follows:

• AlpacaEval 2: Please refer to the AlpacaEval repo for evaluation.

• Arena-Hard: Please refer to to the Arena-Hard-Auto repo for evaluation.

• MT-Bench: Please refer to the FastChat repo for evaluation.

If you have any questions related to the code or the paper, feel free to email Yu ([email protected]). If you encounter any problems when using the code, or want to report a bug, feel free to open an issue! Please try to specify the problem with details so we can help you better and quicker!

Please cite our paper if you find the repo helpful in your work:

@article{meng2024simpo,
  title={{SimPO}: Simple Preference Optimization with a Reference-Free Reward},
  author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
  year={2024}
}