model_name="llama3"  # model name, e.g. llama3, qwen
model_name_or_path=/path/to/model                #path to the huggingface model
task_type="qa"  # task type, e.g. qa, summary
dataset_name="nq"   # testing dataset name. e.g., nq, nqswap, memotrap, hotpot... Note: NQ and NQSWAP are the same dataset, you only need to modify the dataset_name to "nqswap".
data_path=../eval/data/QA/nqswap.json
learning_rate=1e-5   # Optimal parameters
kl_coeff=0.1
num_train_epochs=10

python ../src/train_and_inference.py  \
    --model_name_or_path ${model_name_or_path} \
    --data_path ${data_path} \
    --bf16 True --num_data 4000 \
    --output_path ../result \
    --output_dir ../log  \
    --per_device_train_batch_size 1  \
    --per_device_eval_batch_size 1   \
    --gradient_accumulation_steps 1 \
    --evaluation_strategy no  \
    --weight_decay 0. \
    --logging_steps 1 \
    --tf32 false  \
    --lr_scheduler_type constant  \
    --report_to none  \
    --remove_unused_columns false  \
    --model_name ${model_name}  \
    --learning_rate ${learning_rate} \
    --kl_coeff ${kl_coeff} \
    --num_train_epochs ${num_train_epochs} \
    --max_new_tokens 100 \
    --dataset_name ${dataset_name} \
    --task_type ${task_type}  \
    --choose_cd False \
    --choose_dola False
