nero-ysss/train.sh
2025-03-26 10:17:03 +02:00

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#export CUDA_HOME=/usr/lib/nvidia-cuda-toolkit/bin && \
#export PATH=/usr/lib/nvidia-cuda-toolkit/bin:$PATH && \
#export LD_LIBRARY_PATH=/usr/lib/cuda:$LD_LIBRARY_PATH && \
#export CUDA_VISIBLE_DEVICES=0,1,2 && \
#export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True && \
#export TORCH_USE_CUDA_DSA=1
time python3 run_clm.py \
--model_name_or_path sberbank-ai/rugpt3small_based_on_gpt2 \
--train_file corpus.txt \
--per_device_train_batch_size 2 \
--block_size 2048 \
--dataset_config_name plain_text \
--do_train \
--gradient_accumulation_steps 4 \
--gradient_checkpointing True \
--bf16 True \
--optim adamw_torch \
--weight_decay 0.1 \
--num_train_epochs 10 \
--max_steps 50000 \
--save_steps 20 \
--save_total_limit 5 \
--output_dir models/nero-ysss