accumulative_counts = 2
avg_num_per_pack = 5
batch_size = 1
betas = (
    0.9,
    0.95,
)
custom_hooks = [
    dict(
        by_epoch=False,
        interval=200,
        type='xtuner.engine.hooks.DataResumeHook'),
    dict(type='xtuner.engine.hooks.VarlenAttnArgsToMessageHubHook'),
]
data_num = 510000000
data_path = '/cpfs01/shared/alillm_hs/zouyicheng/rm_pretrain/data/train'
dataloader_num_workers = 0
default_hooks = dict(
    checkpoint=dict(
        by_epoch=False,
        interval=200,
        max_keep_ckpts=10,
        type='mmengine.hooks.CheckpointHook'),
    logger=dict(
        interval=10,
        log_metric_by_epoch=False,
        type='mmengine.hooks.LoggerHook'),
    param_scheduler=dict(type='mmengine.hooks.ParamSchedulerHook'),
    sampler_seed=dict(type='mmengine.hooks.DistSamplerSeedHook'),
    timer=dict(type='mmengine.hooks.IterTimerHook'))
env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'pytorch'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
loss_type = 'ranking'
lr = 1.45e-05
max_epochs = 1
max_length = 16384
max_norm = 1
max_packed_length = 32768
max_response_length = 5120
model = dict(
    llm=dict(
        pretrained_model_name_or_path=
        '/cpfs01/shared/alillm_hs/zouyicheng/xtuner/model/internlm2_5-7b',
        trust_remote_code=True,
        type='transformers.AutoModel.from_pretrained'),
    loss_type='ranking',
    penalty_type='none',
    type='xtuner.model.reward.RewardModel',
    use_varlen_attn=True)
optim_type = 'torch.optim.AdamW'
optim_wrapper = dict(
    optimizer=dict(
        betas=(
            0.9,
            0.95,
        ),
        lr=1.45e-05,
        type='torch.optim.AdamW',
        weight_decay=0),
    type='DeepSpeedOptimWrapper')
param_scheduler = [
    dict(
        begin=0,
        by_epoch=True,
        convert_to_iter_based=True,
        end=0.03,
        start_factor=1.45e-06,
        type='mmengine.optim.LinearLR'),
    dict(
        begin=0.03,
        by_epoch=True,
        convert_to_iter_based=True,
        end=1,
        eta_min=1.45e-06,
        type='mmengine.optim.CosineAnnealingLR'),
]
penalty_type = 'none'
pretrained_model_name_or_path = '/cpfs01/shared/alillm_hs/zouyicheng/xtuner/model/internlm2_5-7b'
randomness = dict(deterministic=False, seed=None)
resume = True
reward_token_id = 92527
runner_type = 'FlexibleRunner'
sampler = 'mmengine.dataset.DefaultSampler'
save_steps = 200
save_total_limit = 10
sequence_parallel_size = 1
strategy = dict(
    config=dict(
        bf16=dict(enabled=True),
        fp16=dict(enabled=False, initial_scale_power=16),
        gradient_accumulation_steps='auto',
        gradient_clipping='auto',
        train_micro_batch_size_per_gpu='auto',
        zero_allow_untested_optimizer=True,
        zero_force_ds_cpu_optimizer=False,
        zero_optimization=dict(overlap_comm=True, stage=1)),
    exclude_frozen_parameters=True,
    gradient_accumulation_steps=2,
    gradient_clipping=1,
    sequence_parallel_size=1,
    train_micro_batch_size_per_gpu=1,
    type='xtuner.engine.DeepSpeedStrategy')
tokenizer = dict(
    padding_side='left',
    pretrained_model_name_or_path=
    '/cpfs01/shared/alillm_hs/zouyicheng/xtuner/model/internlm2_5-7b',
    trust_remote_code=True,
    type='transformers.AutoTokenizer.from_pretrained')
train_cfg = dict(max_epochs=1, type='xtuner.engine.runner.TrainLoop')
train_dataloader = dict(
    batch_size=1,
    collate_fn=dict(
        type=
        'xtuner.dataset.collate_fns.preference_collate_fn.preference_collate_fn',
        use_varlen_attn=True),
    dataset=dict(
        avg_num_per_pack=5,
        data_num=510000000,
        dataset=dict(
            path='/cpfs01/shared/alillm_hs/zouyicheng/rm_pretrain/data/train',
            streaming=True,
            type='datasets.load_dataset'),
        dataset_map_fn=None,
        if_pretrain=True,
        is_dpo=False,
        is_reward=True,
        max_length=16384,
        max_packed_length=32768,
        max_response_length=5120,
        num_proc=32,
        reward_token_id=92527,
        shuffle_before_pack=True,
        tokenizer=dict(
            padding_side='left',
            pretrained_model_name_or_path=
            '/cpfs01/shared/alillm_hs/zouyicheng/xtuner/model/internlm2_5-7b',
            trust_remote_code=True,
            type='transformers.AutoTokenizer.from_pretrained'),
        type=
        'xtuner.dataset.preference_dataset.build_preference_dataset_stream',
        use_varlen_attn=True,
        work_dir_name=
        'RM_PT_internlm2_5_7b_DATA_510m_single_mix_Node_57_LR_1_45e_5'),
    drop_last=True,
    num_workers=0)
train_dataset = dict(
    avg_num_per_pack=5,
    data_num=510000000,
    dataset=dict(
        path='/cpfs01/shared/alillm_hs/zouyicheng/rm_pretrain/data/train',
        streaming=True,
        type='datasets.load_dataset'),
    dataset_map_fn=None,
    if_pretrain=True,
    is_dpo=False,
    is_reward=True,
    max_length=16384,
    max_packed_length=32768,
    max_response_length=5120,
    num_proc=32,
    reward_token_id=92527,
    shuffle_before_pack=True,
    tokenizer=dict(
        padding_side='left',
        pretrained_model_name_or_path=
        '/cpfs01/shared/alillm_hs/zouyicheng/xtuner/model/internlm2_5-7b',
        trust_remote_code=True,
        type='transformers.AutoTokenizer.from_pretrained'),
    type='xtuner.dataset.preference_dataset.build_preference_dataset_stream',
    use_varlen_attn=True,
    work_dir_name='RM_PT_internlm2_5_7b_DATA_510m_single_mix_Node_57_LR_1_45e_5'
)
use_varlen_attn = True
visualizer = dict(
    type='mmengine.visualization.Visualizer',
    vis_backends=[
        dict(type='mmengine.visualization.TensorboardVisBackend'),
    ])
warmup_ratio = 0.03
weight_decay = 0
work_dir = './work_dirs/RM_PT_internlm2_5_7b_DATA_510m_single_mix_Node_57_LR_1_45e_5'