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我训练的模型是BELLE-7B-0.2M,使用你们示例提供的school_math作为数据集,使用的训练命令如下: torchrun --nproc_per_node 1 src/entry_point/sft_train.py --ddp_timeout 36000 --model_name_or_path ${model_name_or_path} --use_lora --deepspeed configs/deepspeed_config_stage3.json --lora_config configs/lora_config_bloom.json --train_file ${train_file} --validation_file ${validation_file} --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --gradient_accumulation_steps 1 --num_train_epochs 10 --model_max_length ${cutoff_len} --save_strategy "steps" --save_total_limit 3 --learning_rate 3e-4 --weight_decay 0.00001 --warmup_ratio 0.01 --lr_scheduler_type "cosine" --logging_steps 10 --evaluation_strategy "steps" --bf16 --seed 1234 --gradient_checkpointing --cache_dir ${cache_dir} --output_dir ${output_dir} \
以下为显存占用情况:
请问我该如何加速训练呢?
The text was updated successfully, but these errors were encountered:
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我训练的模型是BELLE-7B-0.2M,使用你们示例提供的school_math作为数据集,使用的训练命令如下:
torchrun --nproc_per_node 1 src/entry_point/sft_train.py
--ddp_timeout 36000
--model_name_or_path ${model_name_or_path}
--use_lora
--deepspeed configs/deepspeed_config_stage3.json
--lora_config configs/lora_config_bloom.json
--train_file ${train_file}
--validation_file ${validation_file}
--per_device_train_batch_size 1
--per_device_eval_batch_size 1
--gradient_accumulation_steps 1
--num_train_epochs 10
--model_max_length ${cutoff_len}
--save_strategy "steps"
--save_total_limit 3
--learning_rate 3e-4
--weight_decay 0.00001
--warmup_ratio 0.01
--lr_scheduler_type "cosine"
--logging_steps 10
--evaluation_strategy "steps"
--bf16
--seed 1234
--gradient_checkpointing
--cache_dir ${cache_dir}
--output_dir ${output_dir} \
以下为显存占用情况:
请问我该如何加速训练呢?
The text was updated successfully, but these errors were encountered: