# Start FSDP2 fine-tuning
# accelerate launch \
#     --config_file examples/accelerate/fsdp2_config.yaml \
#     src/train.py examples/ascend/qwen3vlmoe_full_sft_fsdp2.yaml
# Change `num_processes` in fsdp2_config.yaml to 16 in A3

### model
model_name_or_path: Qwen/Qwen3-VL-30B-A3B-Instruct
image_max_pixels: 262144
video_max_pixels: 16384
trust_remote_code: true
use_v1_kernels: true
flash_attn: fa2

### method
stage: sft
do_train: true
finetuning_type: full
disable_gradient_checkpointing: false

### dataset
dataset: llava_1k_en, llava_1k_zh
template: qwen3_vl
cutoff_len: 1024
overwrite_cache: true
preprocessing_num_workers: 16
dataloader_num_workers: 4

### output
output_dir: saves/Qwen3-VL-30B-A3B-Instruct/full/sft
logging_steps: 1
save_steps: 500
max_steps: 500
plot_loss: true
overwrite_output_dir: true
save_only_model: true
report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]

### train
per_device_train_batch_size: 2
gradient_accumulation_steps: 1
learning_rate: 1.0e-4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
resume_from_checkpoint: null
seed: 1234