scripts/finetune_video.sh

#!/bin/bash

# MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
# MODEL_NAME="Qwen/Qwen2-VL-2B-Instruct"
# MODEL_NAME="Qwen/Qwen2.5-VL-3B-Instruct"
# MODEL_NAME="Qwen/Qwen2.5-VL-7B-Instruct"

MODEL_NAME="Qwen/Qwen3-VL-4B-Instruct"

export PYTHONPATH=src:$PYTHONPATH

GLOBAL_BATCH_SIZE=128
BATCH_PER_DEVICE=4
NUM_DEVICES=8
GRAD_ACCUM_STEPS=$((GLOBAL_BATCH_SIZE / (BATCH_PER_DEVICE * NUM_DEVICES)))

# If your dataset is mixed with images and videos, you need to use zero2.
# If you want to set the min pixels and max pixels for Qwen3-VL, You should set as (N * 32 * 32)

deepspeed src/train/train_sft.py \
    --use_liger_kernel True \
    --deepspeed scripts/zero3_offload.json \
    --model_id $MODEL_NAME \
    --data_path /path/to/your/training/data.json \
    --image_folder /path/to/your/image/folder \
    --remove_unused_columns False \
    --freeze_vision_tower False \
    --freeze_llm False \
    --freeze_merger False \
    --bf16 True \
    --fp16 False \
    --disable_flash_attn2 False \
    --output_dir output/test_train \
    --num_train_epochs 1 \
    --per_device_train_batch_size $BATCH_PER_DEVICE \
    --gradient_accumulation_steps $GRAD_ACCUM_STEPS \
    --video_max_pixels $((360 * 420)) \
    --fps 1.0 \
    --learning_rate 1e-5 \
    --merger_lr 1e-5 \
    --vision_lr 2e-6 \
    --weight_decay 0.1 \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 True \
    --gradient_checkpointing True \
    --report_to tensorboard \
    --lazy_preprocess True \
    --save_strategy "steps" \
    --save_steps 1 \
    --save_total_limit 10 \
    --dataloader_num_workers 4