class_docstr: Llama Reward model.
  
  Args:
      pretrained (str): Pretrained model name or path.
      config (LlamaConfig): Model config.
      lora_rank (int): LoRA rank.
      lora_train_bias (str): LoRA bias training mode.
code: def __init__(
      self,
      pretrained: Optional[str] = None,
      config: Optional[LlamaConfig] = None,
      lora_rank: int = 0,
      lora_train_bias: str = "none",
  ) -> None:
      if pretrained is not None:
          model = LlamaModel.from_pretrained(pretrained)
      elif config is not None:
          model = LlamaModel(config)
      else:
          model = LlamaModel(LlamaConfig())
  
      value_head = nn.Linear(model.config.hidden_size, 1)
      value_head.weight.data.normal_(
          mean=0.0, std=1 / (model.config.hidden_size + 1)
      )
  
      super().__init__(model, value_head, lora_rank, lora_train_bias)
  
imports: from typing import Optional
  import torch.nn
  from transformers import LlamaConfig, LlamaModel
  from ..base import RewardModel
label_desc: Use image node_id 1 to create a new LlamaRM object from inherited base classes: RewardModel with example: obj = LlamaRM(pretrained, config, lora_rank, lora_train_bias)