75 lines
2.5 KiB
Bash
75 lines
2.5 KiB
Bash
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#!/bin/bash
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# Pretraining (2 hours)
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torchrun --nnodes=1 --nproc_per_node=8 --master_port=25001 \
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llava/train/train_mem.py \
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--model_name_or_path mosaicml/mpt-7b-chat \
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--version v1 \
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--data_path /path/to/blip_laion_cc_sbu_558k.json \
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--image_folder /path/to/blip_laion_cc_sbu_558k \
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--vision_tower openai/clip-vit-large-patch14 \
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--tune_mm_mlp_adapter True \
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--mm_vision_select_layer -2 \
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--mm_use_im_start_end \
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--bf16 True \
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--output_dir ./checkpoints/llava-lightning-mpt-7b-pretrain \
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--num_train_epochs 1 \
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--per_device_train_batch_size 16 \
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--per_device_eval_batch_size 4 \
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--gradient_accumulation_steps 1 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 2400 \
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--save_total_limit 1 \
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--learning_rate 2e-3 \
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--weight_decay 0. \
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--warmup_ratio 0.03 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--tf32 True \
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--model_max_length 2048 \
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--gradient_checkpointing True \
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--dataloader_num_workers 4 \
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--lazy_preprocess True \
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--report_to wandb
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# Extract projector features
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python scripts/extract_mm_projector.py \
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--model_name_or_path ./checkpoints/llava-lightning-mpt-7b-pretrain \
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--output ./checkpoints/mm_projector/llava-lightning-mpt-7b-pretrain.bin
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# Visual instruction tuning (1 hour)
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torchrun --nnodes=1 --nproc_per_node=8 --master_port=25001 \
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llava/train/train_mem.py \
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--model_name_or_path mosaicml/mpt-7b-chat \
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--version v1 \
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--data_path /path/to/llava_instruct_80k.json \
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--image_folder /Data/haotian/coco/train2014 \
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--vision_tower openai/clip-vit-large-patch14 \
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--pretrain_mm_mlp_adapter ./checkpoints/mm_projector/llava-lightning-mpt-7b-pretrain.bin \
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--mm_vision_select_layer -2 \
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--mm_use_im_start_end True \
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--bf16 True \
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--output_dir ./checkpoints \
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--num_train_epochs 1 \
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--per_device_train_batch_size 16 \
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--per_device_eval_batch_size 4 \
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--gradient_accumulation_steps 1 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 5000 \
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--save_total_limit 1 \
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--learning_rate 2e-5 \
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--weight_decay 0. \
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--warmup_ratio 0.03 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--tf32 True \
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--fsdp "full_shard auto_wrap" \
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--fsdp_transformer_layer_cls_to_wrap 'MPTBlock' \
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--model_max_length 2048 \
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--gradient_checkpointing True \
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--dataloader_num_workers 4 \
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--lazy_preprocess True \
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--report_to wandb
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