train_multi_0707.sh

export WANDB_MODE=online

cd /workspace/medvicuna

torchrun --nproc_per_node=8 --master_port=20001 /workspace/medvicuna/fastchat/train/train_mem.py \
    --model_name_or_path lmsys/vicuna-33b-v1.3 \
    --data_path /workspace/medvicuna/33b/medqa_opt4.json \
    --cache_dir /workspace/.cache \
    --push_to_hub False \
    --bf16 True \
    --output_dir output_vicuna_33b_medqa_opt4 \
    --num_train_epochs 8 \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --evaluation_strategy "steps" \
    --eval_steps 250 \
    --save_strategy "steps" \
    --save_steps 300 \
    --save_total_limit 100 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --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 \
    --lazy_preprocess True &>> /workspace/medvicuna/logs/output_vicuna_33b_medqa_opt4.log

sleep 120s

nohup bash /workspace/medvicuna/push_to_hub.sh https://huggingface.co/s1ghhh/vicuna_33b_medqa_opt4_0707 /workspace/medvicuna/output_vicuna_33b_medqa_opt4 &

cd /workspace/medvicuna

torchrun --nproc_per_node=8 --master_port=20001 /workspace/medvicuna/fastchat/train/train_mem.py \
    --model_name_or_path alexl83/LLaMA-33B-HF \
    --data_path /workspace/medvicuna/33b/medqa_opt4.json \
    --cache_dir /workspace/.cache \
    --push_to_hub False \
    --bf16 True \
    --output_dir output_llama_33b_medqa_opt4 \
    --num_train_epochs 8 \
    --lazy_preprocess True \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --evaluation_strategy "steps" \
    --eval_steps 250 \
    --save_strategy "steps" \
    --save_steps 300 \
    --save_total_limit 100 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --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 &>> /workspace/medvicuna/logs/output_llama_33b_medqa_opt4.log

sleep 120s

nohup bash /workspace/medvicuna/push_to_hub.sh https://huggingface.co/s1ghhh/llama_33b_medqa_opt4_0707 /workspace/medvicuna/output_llama_33b_medqa_opt4 &

cd /workspace/medvicuna

torchrun --nproc_per_node=8 --master_port=20001 /workspace/medvicuna/fastchat/train/train_mem.py \
    --model_name_or_path lmsys/vicuna-33b-v1.3 \
    --data_path /workspace/medvicuna/33b/medqa_opt4_aug.json \
    --cache_dir /workspace/.cache \
    --push_to_hub False \
    --lazy_preprocess True \
    --bf16 True \
    --output_dir output_vicuna_33b_medqa_opt4_aug \
    --num_train_epochs 8 \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --evaluation_strategy "steps" \
    --eval_steps 250 \
    --save_strategy "steps" \
    --save_steps 300 \
    --save_total_limit 100 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --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 &>> /workspace/medvicuna/logs/output_vicuna_33b_medqa_opt4_aug.log

sleep 120s

nohup bash /workspace/medvicuna/push_to_hub.sh https://huggingface.co/s1ghhh/vicuna_33b_medqa_opt4_aug_0707 /workspace/medvicuna/output_vicuna_33b_medqa_opt4_aug &

cd /workspace/medvicuna

torchrun --nproc_per_node=8 --master_port=20001 /workspace/medvicuna/fastchat/train/train_mem.py \
    --model_name_or_path alexl83/LLaMA-33B-HF \
    --data_path /workspace/medvicuna/33b/medqa_opt4_aug.json \
    --cache_dir /workspace/.cache \
    --push_to_hub False \
    --bf16 True \
    --output_dir output_llama_33b_medqa_opt4_aug \
    --num_train_epochs 8 \
    --lazy_preprocess True \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --evaluation_strategy "steps" \
    --eval_steps 250 \
    --save_strategy "steps" \
    --save_steps 300 \
    --save_total_limit 100 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --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 &>> /workspace/medvicuna/logs/output_llama_33b_medqa_opt4_aug.log

sleep 120s

nohup bash /workspace/medvicuna/push_to_hub.sh https://huggingface.co/s1ghhh/llama_33b_medqa_opt4_aug_0707 /workspace/medvicuna/output_llama_33b_medqa_opt4_aug &

cd /workspace/medvicuna

torchrun --nproc_per_node=8 --master_port=20001 /workspace/medvicuna/fastchat/train/train_mem.py \
    --model_name_or_path eachadea/vicuna-13b-1.1 \
    --data_path /workspace/medvicuna/medvicuna_v1.1_520k_augAndNoaug.json \
    --cache_dir /workspace/.cache \
    --push_to_hub False \
    --bf16 True \
    --lazy_preprocess True \
    --output_dir output_medvicuna_v1.1_13b \
    --num_train_epochs 8 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 16 \
    --gradient_accumulation_steps 1 \
    --evaluation_strategy "steps" \
    --eval_steps 1150 \
    --save_strategy "steps" \
    --save_steps 1150 \
    --save_total_limit 100 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --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 &>> /workspace/medvicuna/logs/output_medvicuna_v1.1_13b.log


nohup bash /workspace/medvicuna/push_to_hub.sh https://huggingface.co/s1ghhh/medvicuna_v1.1_13b_0707 /workspace/medvicuna/output_medvicuna_v1.1_13b &

cd /workspace/medvicuna

torchrun --nproc_per_node=8 --master_port=20001 fastchat/train/train_mem.py \
    --model_name_or_path eachadea/vicuna-7b-1.1  \
    --data_path /workspace/medvicuna/medvicuna_v1.1_520k_augAndNoaug.json \
    --push_to_hub False \
    --bf16 True \
    --output_dir medvicuna_7b_epoch8_test \
    --num_train_epochs 8 \
    --per_device_train_batch_size 32 \
    --per_device_eval_batch_size 16 \
    --gradient_accumulation_steps 1 \
    --evaluation_strategy "epoch" \
    --eval_steps 1894 \
    --save_strategy "no" \
    --save_steps 3787 \
    --save_total_limit 32 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --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 2048 \
    --gradient_checkpointing True \
    --lazy_preprocess True &>> medvicuna_7b_epoch8_test.log