conda create -n LightLM python=3.9
conda activate LightLM
pip install -r requirements.txt
Experiments are done on a single A5000 GPU with CUDA Version: 11.7.
Here we use the Toys dataset as an example.
CUDA_VISIBLE_DEVICES=0 python \
main.py \
--task toys \
--seed 2022 \
--warmup_prop 0.05 \
--lr 1e-3 \
--clip 1.0 \
--model_type 't5-small' \
--epochs 10 \
--gpu '0' \
--data_dir data \
--logging_step 100 \
--logging_dir 'log/pretrain_dn_t5_small_toys_co_useritem_CF_50.log' \
--model_dir 'model/pretrain_dn_t5_small_toys_co_useritem_CF_50' \
--train_direct_straightforward_batch 64 \
--eval_direct_straightforward_batch 32 \
--ffn_width 16 \
--whole_word_embedding shijie \
--random_initialization_embedding \
--item_representation CID \
--user_representation CID \
--random_initialization_embedding \
--data_order remapped_sequential \
--remapped_data_order original \
--co_indexing \
--user_cluster_num 50 \
--user_cluster_size 100 \
--item_cluster_num 50 \
--item_cluster_size 100
CUDA_VISIBLE_DEVICES=0 python \
main.py \
--task toys \
--seed 2022 \
--warmup_prop 0.05 \
--lr 1e-3 \
--clip 1.0 \
--model_type 't5-small' \
--epochs 10 \
--gpu '0' \
--data_dir data/ \
--logging_step 100 \
--logging_dir 'log/pretrain_dn_t5_small_toys_co_useritem_graph.log' \
--model_dir 'model/pretrain_dn_t5_small_toys_co_useritem_graph' \
--train_direct_straightforward_batch 64 \
--eval_direct_straightforward_batch 32 \
--ffn_width 16 \
--whole_word_embedding shijie \
--random_initialization_embedding \
--item_representation GID \
--user_representation GID \
--random_initialization_embedding \
--data_order remapped_sequential \
--remapped_data_order original \
--co_indexing \
--user_quantized_len 4 \
--item_quantized_len 4
If you find our work useful, please consider citing our paper:
@article{mei2023lightlm,
title={LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation},
author={Kai Mei and Yongfeng Zhang},
journal={arXiv:2310.17488},
year={2023}
}