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# export WANDB_MODE=offline
# openlm-research/open_llama_3b
# --num_train_epochs 1 \
torchrun --nproc_per_node=8 --master_port=20001 fastchat/train/pretrain_streaming_mem.py \
--model_name_or_path NousResearch/Llama-2-7b-hf \
--train_file_dir /workspace/medvicuna/pretrain_data_170G \
--cache_dir /workspace/.cache \
--bf16 True \
--max_steps 12000 \
--output_dir /workspace/medvicuna/output_medllama2_pretrain \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 32 \
--evaluation_strategy "no" \
--eval_steps 4500 \
--save_strategy "steps" \
--save_steps 250 \
--save_total_limit 1000 \
--learning_rate 5e-5 \
--weight_decay 0.1 \
--warmup_ratio 0.02 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 4096 \
--gradient_checkpointing True &>> pretrain_set1.log
torchrun --nproc_per_node=8 --master_port=20001 fastchat/train/pretrain_streaming_mem.py \
--model_name_or_path NousResearch/Llama-2-7b-hf \
--train_file_dir /workspace/medvicuna/pretrain_data_170G \
--cache_dir /workspace/.cache \
--bf16 True \
--max_steps 24000 \
--output_dir /workspace/medvicuna/output_medllama2_pretrain \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 16 \
--evaluation_strategy "no" \
--eval_steps 4500 \
--save_strategy "steps" \
--save_steps 500 \
--save_total_limit 1000 \
--learning_rate 5e-5 \
--weight_decay 0.1 \
--warmup_ratio 0.04 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 4096 \
--gradient_checkpointing True &>> pretrain_set2.log
torchrun --nproc_per_node=8 --master_port=20001 fastchat/train/pretrain_streaming_mem.py \
--model_name_or_path NousResearch/Llama-2-7b-hf \
--train_file_dir /workspace/medvicuna/pretrain_data_170G \
--cache_dir /workspace/.cache \
--bf16 True \
--max_steps 24000 \
--output_dir /workspace/medvicuna/output_medllama2_pretrain \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 32 \
--evaluation_strategy "no" \
--eval_steps 4500 \
--save_strategy "steps" \
--save_steps 500 \
--save_total_limit 1000 \
--learning_rate 5e-5 \
--weight_decay 0.1 \
--warmup_ratio 0.04 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 4096 \
--gradient_checkpointing True &>> pretrain_set3.log
torchrun --nproc_per_node=8 --master_port=20001 fastchat/train/pretrain_streaming_mem.py \
--model_name_or_path NousResearch/Llama-2-7b-hf \
--train_file_dir /workspace/medvicuna/pretrain_data_170G \
--cache_dir /workspace/.cache \
--bf16 True \
--max_steps 12000 \
--output_dir /workspace/medvicuna/output_medllama2_pretrain \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--eval_steps 4500 \
--save_strategy "steps" \
--save_steps 250 \
--save_total_limit 1000 \
--learning_rate 5e-5 \
--weight_decay 0.1 \
--warmup_ratio 0.04 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True &>> pretrain_set4.log
torchrun --nproc_per_node=8 --master_port=20001 fastchat/train/pretrain_streaming_mem.py \
--model_name_or_path yahma/llama-7b-hf \
--train_file_dir /workspace/medvicuna/pretrain_data_170G \
--cache_dir /workspace/.cache \
--bf16 True \
--max_steps 12000 \
--output_dir /workspace/medvicuna/output_medllama_pretrain \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--eval_steps 4500 \
--save_strategy "steps" \
--save_steps 250 \
--save_total_limit 1000 \
--learning_rate 5e-5 \
--weight_decay 0.1 \
--warmup_ratio 0.04 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True &>> pretrain_set5.log