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#!/bin/bash
if [ $# -ne 10 ]; then
echo "Usage: $0 <DATA_PATH> <IMAGE_PATH> <LLM_VERSION> <VT_VERSION> <VT_VERSION2> <CN_VERSION> <CONV_VERSION> <VERSION> <TRAIN_RECIPE> <MODEL_MAX_LENGTH>"
exit 1
fi
# Assign the arguments to variables
DATA_PATH="$1"
IMAGE_PATH="$2"
LLM_VERSION="$3"
VT_VERSION="$4"
VT_VERSION2="$5"
CN_VERSION="$6"
CONV_VERSION="$7"
VERSION="$8"
TRAIN_RECIPE="$9"
MODEL_MAX_LENGTH="${10}"
VT_VARIANT="${VT_VERSION#*/}"
LLM_VARIANT="${LLM_VERSION#*/}"
deepspeed --include localhost:4,5,6,7 --master_port 29501 tinyllava/train/train.py \
--deepspeed ./scripts/zero3.json \
--data_path $DATA_PATH \
--image_folder $IMAGE_PATH \
--is_multimodal True \
--conv_version $CONV_VERSION \
--model_name_or_path $LLM_VERSION \
--vision_tower $VT_VERSION \
--vision_tower2 "$VT_VERSION2" \
--connector_type $CN_VERSION \
--mm_vision_select_layer -2 \
--image_aspect_ratio square \
--attn_implementation flash_attention_2 \
--fp16 True \
--training_recipe $TRAIN_RECIPE \
--tune_type_llm full \
--tune_type_vision_tower frozen\
--tune_vision_tower_from_layer 0 \
--tune_type_connector full \
--group_by_modality_length True \
--pretrained_model_path /mnt/data/sata/yinghu/checkpoints/llava_factory/tiny-llava-${LLM_VARIANT}-${VT_VARIANT}-${VERSION}-pretrain \
--output_dir /mnt/data/sata/yinghu/checkpoints/llava_factory/tiny-llava-${LLM_VARIANT}-${VT_VARIANT}-${VERSION}-finetune \
--num_train_epochs 1 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 16 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 False \
--model_max_length $MODEL_MAX_LENGTH \
--gradient_checkpointing True \
--dataloader_num_workers 8 \
--lazy_preprocess True \
--report_to tensorboard \
--tokenizer_use_fast False \
--run_name tiny-llava-${LLM_VARIANT}-${VT_VARIANT}-${VERSION}-finetune
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