llama3_lora_sft.yaml

### model
model_name_or_path: meta-llama/Llama-3.1-8B-Instruct

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

### dataset
dataset: bct_non_cot_sft_500
dataset_dir: data_private
template: llama3
cutoff_len: 1024
# max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16

### output
output_dir: saves/Llama-3.1-8B-Instruct/lora/sft-half
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
save_total_limit: 3
load_best_model_at_end: true
push_to_hub: true
hub_model_id: chchen/Llama-3.1-8B-Instruct-SFT-Half

### train
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
learning_rate: 0.000005
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true

### eval
val_size: 0.1
per_device_eval_batch_size: 2
evaluation_strategy: steps
eval_steps: 500