### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct  # or use local absolute path
trust_remote_code: true

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

### dataset
dataset: identity,alpaca_en_demo
dataset_dir: REMOTE:llamafactory/demo_data  # or use local absolute path
template: llama3
cutoff_len: 2048
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
dataloader_num_workers: 4

### output
output_dir: tmp_dir
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
save_only_model: false

### ray
ray_run_name: llama3_8b_sft_lora
ray_storage_path: ./saves
ray_num_workers: 4  # number of GPUs to use
resources_per_worker:
  GPU: 1
placement_strategy: PACK

### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
resume_from_checkpoint: null

### eval
# eval_dataset: alpaca_en_demo
# val_size: 0.1
# per_device_eval_batch_size: 1
# eval_strategy: steps
# eval_steps: 500
