NNODES=8
VISION_MODEL_VERSION="google/siglip-so400m-patch14-384"
PROMPT_VERSION="qwen2"
BASE_RUN_NAME="video-utr-7b"

torchrun --nproc_per_node=8 \
    --master_addr $MASTER_ADDR \
    --master_port ${MASTER_PORT} \
    --nnodes $NNODES \
    --node_rank $NODE_RANK \
    llava/train/train_mem.py \
    --deepspeed scripts/zero3.json \
    --model_name_or_path ${CKPT_PATH} \
    --version ${PROMPT_VERSION} \
    --data_path sft/data.yaml \
    --image_folder data/images \
    --video_folder data/videos \
    --pretrain_mm_mlp_adapter="checkpoints/utr_pretrain/mm_projector.bin" \ # replace with your own pretrain adapter
    --mm_tunable_parts="mm_vision_tower,mm_mlp_adapter,mm_language_model" \
    --mm_vision_tower_lr=2e-6 \
    --vision_tower ${VISION_MODEL_VERSION} \
    --mm_projector_type mlp2x_gelu \
    --mm_vision_select_layer -2 \
    --mm_use_im_start_end False \
    --mm_use_im_patch_token False \
    --group_by_modality_length True \
    --image_aspect_ratio anyres_max_9 \
    --image_grid_pinpoints  "(1x1),...,(6x6)" \
    --mm_patch_merge_type spatial_unpad \
    --bf16 True \
    --run_name $BASE_RUN_NAME \
    --output_dir "checkpoints/$BASE_RUN_NAME" \
    --num_train_epochs 1 \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 8 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 100 \
    --save_total_limit 1 \
    --learning_rate 1e-5 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 True \
    --model_max_length 32768 \
    --gradient_checkpointing True \
    --dataloader_num_workers 4 \
    --lazy_preprocess True \
    --report_to wandb \
    --torch_compile True \
    --torch_compile_backend "inductor" \
    --dataloader_drop_last True \
    --frames_upbound 32

# You can delete the sdpa attn_implementation if you want to use flash attn