Open nancheng58 opened 6 months ago
I replaced the PAD token from 0 to '0' and solved this issue. Due to the resourse restricted, I replaced the basemodel to Llama2-13B to train and evaluate the E4SRec, but the gained result is very poor. The finetune script is
torchrun --nproc_per_node=8 --master_port=1907 finetune.py \
--base_model /data06/Llama-2-13b-hf \
--data_path Beauty \
--task_type sequential \
--output_dir ./LLM4Rec \
--batch_size 16 \
--micro_batch_size 1 \
--num_epochs 1 \
--learning_rate 0.0003 \
--cutoff_len 4096 \
--val_set_size 0 \
--lora_r 16 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_target_modules '[gate_proj, down_proj, up_proj]' \
--train_on_inputs False \
--add_eos_token False \
--group_by_length False \
--prompt_template_name alpaca \
--lr_scheduler 'cosine' \
--warmup_steps 100 \
--wandb_project E4SRec
The infer script is
torchrun --nproc_per_node=8 --master_port=1234 inference.py \
--base_model /data06/Llama-2-13b-hf \
--data_path Beauty \
--task_type sequential \
--checkpoint_dir ./LLM4Rec/ \
--cache_dir cache_dir/ \
--output_dir ./LLM4Rec/ \
--batch_size 16 \
--micro_batch_size 1
The reported results is that
Evaluation for User:
Precision@1: 8.9433439162903e-05
Recall@1: 8.9433439162903e-05
MRR@1: 8.9433439162903e-05
MAP@1: 8.9433439162903e-05
NDCG@1: 8.9433439162903e-05
Precision@5: 8.9433439162903e-05
Recall@5: 0.00044716719581451504
MRR@5: 0.0001974988448180775
MAP@5: 0.0001974988448180775
NDCG@5: 0.0002586554819987668
Precision@10: 8.943343916290303e-05
Recall@10: 0.0008943343916290301
MRR@10: 0.00025114116358582666
MAP@10: 0.00025114116358582666
NDCG@10: 0.0003971278706389283
Precision@20: 7.825425926754018e-05
Recall@20: 0.0015650851853508028
MRR@20: 0.0002965062025846168
MAP@20: 0.0002965062025846168
NDCG@20: 0.0005650891080215324
Precision@100: 7.154675133032246e-05
Recall@100: 0.007154675133032241
MRR@100: 0.00040016219874475093
MAP@100: 0.00040016219874475093
NDCG@100: 0.0015212688144377391
The similar issues here.I would like to ask if there are any issues with the evaluation? There are too many decimals,like 0.000.
If you want to use a smaller base model, please use garage-bAInd/Platypus2-13B or garage-bAInd/Platypus2-7B instead. Original LLaMA2 is not instruction-tuned and can not get desirable results.
Dear authors, Thanks for your nice work! I have already cloned this repo and downloaded the 'Platypus2-70B-instruct' model. However, I encounter (meet) with the following error when running the 'fine-turning.sh':
It seems like the token cannot be the number.