# vllm server
# CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve dyyyyyyyy/Qwen2.5-1.5B-GenRM-QueryOnly --served_model_name genrm-demo

# sglang server
# CUDA_VISIBLE_DEVICES=0,1,2,3 python -m sglang_router.launch_server --model-path dyyyyyyyy/Qwen2.5-1.5B-GenRM-QueryOnly --dp-size 4

set -x

CUDA_VISIBLE_DEVICES=4,5,6,7 python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files=${HOME}/data/gsm8k/train.parquet \
    data.val_files=${HOME}/data/gsm8k/test.parquet \
    data.train_batch_size=1024 \
    data.max_prompt_length=1024 \
    data.max_response_length=2048 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    actor_rollout_ref.model.path=Qwen/Qwen2.5-3B-Instruct \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
    actor_rollout_ref.actor.use_dynamic_bsz=True \
    actor_rollout_ref.actor.use_kl_loss=True \
    actor_rollout_ref.actor.kl_loss_coef=0.001 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
    actor_rollout_ref.rollout.n=8 \
    algorithm.use_kl_in_reward=False \
    reward_model.reward_manager=batch \
    custom_reward_function.path=recipe/genrm_remote/reward_function.py \
    custom_reward_function.name=compute_score_batch \
    trainer.critic_warmup=0 \
    trainer.logger=['console','wandb'] \
    trainer.project_name='verl_func_rm_example_gsm8k' \
    trainer.experiment_name='qwen2_5_3b_gen_rm' \
    trainer.n_gpus_per_node=4 \
    trainer.val_before_train=True \
    trainer.nnodes=1 \
    trainer.save_freq=20 \
    trainer.test_freq=5 \
    trainer.total_epochs=10 \
    trainer.resume_mode='disable'
