loramoe's Introduction

LoRAMoE: Revolutionizing Mixture of Experts for Maintaining World Knowledge in Language Model Alignment

This is the repository for LoRAMoE: Revolutionizing Mixture of Experts for Maintaining World Knowledge in Language Model Alignment.

Implementation

You can quickly export the environment by using the follow command:

conda env create -f environment.yml

conda create -n loramoe python=3.10 -y

pip install -r requirements.txt

We do not install the peft to avoid the conflicts with the local peft package.

Usage

Data Format

We construct a tiny dataset to demonstrate the data format during the training and inference phase and evaluate the correct of code.

data/
|--tiny_data/
  |--train/train.json
  |--test.json

Train LoRAMoE on Single Node

bash run_loramoe.sh

Explanations of Hyper-parameters

blc weight	blc alpha	LoRA rank	LoRA alpha	LoRA trainable	LoRA dropout	LoRA num
the strength of localized balance constraints	degree of imbalance	rank of LoRA experts	LoRA scale	where the LoRA layers are added	dropout rate in LoRA	number of experts

Note: Our main changes to `transformers` and `peft`

In transformers, we mainly change modeling_llama.py to introduce new para task_types.

In peft, we replace the original LoRA class with the mixtures of experts architecture.

Citation

If you find this useful in your research, please consider citing

@misc{dou2024loramoe,
      title={LoRAMoE: Revolutionizing Mixture of Experts for Maintaining World Knowledge in Language Model Alignment}, 
      author={Shihan Dou and Enyu Zhou and Yan Liu and Songyang Gao and Jun Zhao and Wei Shen and Yuhao Zhou and Zhiheng Xi and Xiao Wang and Xiaoran Fan and Shiliang Pu and Jiang Zhu and Rui Zheng and Tao Gui and Qi Zhang and Xuanjing Huang},
      year={2023},
      eprint={2312.09979},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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