This is another pytorch implementation of the paper ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation.
The difference of this repo and the original repo is the version of transformers package. While the original implementation is using transformers==4.28.1, here we implement with transformers==4.35.2.
Compared with the original repo, we only modify the finetune.py file. Therefore, as for data preprocessing related staff, please refer to the original repo.
pip install -r requirments.txtpython -u finetune.py \
--lr 0.001 \
--dataset ml-1m \
--train_size 8192 \
--train_type sequential \
--test_type sequential \
--K 30 \
--epochs 5 \
--total_batch_size 256 \
--output_path ml-1m_lora-Vicuna/vicuna-13b-v1.5/lr_0.001_shot_8192_sequential_sequential_K_30_5_bs256 \
--test_range all \
--model_path ./models/vicuna-13b-v1.5/ \
> ml-1m_logs/vicuna-13b-v1.5/lr_0.001_shot_8192_sequential_sequential_K_30_5_bs256.txt
By replacing the finetune.py file in the original repo, you can run the experiment with the scripts.
@article{lin2023rella,
title={ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation},
author={Lin, Jianghao and Shan, Rong and Zhu, Chenxu and Du, Kounianhua and Chen, Bo and Quan, Shigang and Tang, Ruiming and Yu, Yong and Zhang, Weinan},
journal={arXiv preprint arXiv:2308.11131},
year={2023}
}