# Start FSDP2 full fine-tuning on Ascend NPU
# Usage:
#   accelerate launch \
#       --config_file examples/accelerate/fsdp2_config_qwen35_moe.yaml \
#       src/train.py examples/ascend/qwen3_5moe_lora_sft_fsdp2.yaml
#
# Note: Change `num_processes` in fsdp2_config_qwen35_moe.yaml to match your NPU count

### model
model_name_or_path: Qwen/Qwen3.5-35B-A3B
trust_remote_code: true
use_v1_kernels: false
flash_attn: fa2

### method
stage: sft
do_train: true
finetuning_type: lora
lora_rank: 8
lora_target: all

### dataset
dataset: alpaca_en_demo
template: qwen3_5_nothink
cutoff_len: 2048
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
dataloader_num_workers: 4
packing: false

### output
output_dir: saves/Qwen3.5-35B/lora/sft
logging_steps: 1
save_steps: 2000
max_steps: 2000
plot_loss: true
overwrite_output_dir: true
save_only_model: false
report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]

### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 1
learning_rate: 1.0e-5
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 1800
resume_from_checkpoint: null
disable_gradient_checkpointing: true