# 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