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Yu Meng
89 lines
3.7 KiB
Markdown
89 lines
3.7 KiB
Markdown
# Simple Preference Optimization (SimPO)
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This repository contains the code and released models for our paper [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734). 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.
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<img src="./SimPO.png" width="1000px"></img>
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## 🔗 Quick Links
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- [SimPO: Simple Preference Optimization with a Reference-Free Reward](#simple-preference-optimization-simpo)
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- [Released Models](#released-models)
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- [Install Requirements](#install-requirements)
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- [Training scripts](#training-scripts)
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- [Evaluation](#evaluation)
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- [Bugs or Questions?](#bugs-or-questions)
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- [Citation](#citation)
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## Released Models
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## Install Requirements
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Our codebase is built upon the [alignment-handbook repo](https://github.com/huggingface/alignment-handbook). The following steps will guide you through the installation process.
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First, create a Python virtual environment using e.g. Conda:
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```shell
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conda create -n handbook python=3.10 && conda activate handbook
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```
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Next, install PyTorch `v2.2.2`. Since this is hardware-dependent, we
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direct you to the [PyTorch Installation Page](https://pytorch.org/get-started/locally/).
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You can then install the remaining package dependencies of [alignment-handbook](https://github.com/huggingface/alignment-handbook) as follows:
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```shell
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git clone https://github.com/huggingface/alignment-handbook.git
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cd ./alignment-handbook/
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python -m pip install .
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```
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You will also need Flash Attention 2 installed, which can be done by running:
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```shell
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python -m pip install flash-attn --no-build-isolation
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```
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## Training Scripts
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We provide four training config files for the four training setups reported in our paper:
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* Mistral-Base:
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```shell
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ACCELERATE_LOG_LEVEL=info accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml scripts/run_simpo.py training_configs/mistral-7b-base-simpo.yaml
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```
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* Mistral-Instruct:
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```shell
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ACCELERATE_LOG_LEVEL=info accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml scripts/run_simpo.py training_configs/mistral-7b-instruct-simpo.yaml
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```
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* Llama3-Base:
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```shell
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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
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```
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* Llama3-Instruct:
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```shell
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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
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```
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## Evaluation
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We follow the official implementation for evaluation on AlpacaEval 2, Arena-Hard, and MT-Bench, as follows:
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* AlpacaEval 2: Please refer to the [AlpacaEval repo](https://github.com/tatsu-lab/alpaca_eval) for evaluation.
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* Arena-Hard: Please refer to to the [Arena-Hard-Auto repo](https://github.com/lm-sys/arena-hard-auto) for evaluation.
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* MT-Bench: Please refer to the [FastChat repo](https://github.com/lm-sys/FastChat) for evaluation.
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## Bugs or Questions?
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If you have any questions related to the code or the paper, feel free to email Yu (yumeng5@virginia.edu). 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!
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## Citation
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Please cite our paper if you find the repo helpful in your work:
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```bibtex
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@article{meng2024simpo,
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title={{SimPO}: Simple Preference Optimization with a Reference-Free Reward},
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author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
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year={2024}
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}
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```
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