class_docstr: OPT Critic model.
  
  Args:
      pretrained (str): Pretrained model name or path.
      config (OPTConfig): Model config.
      lora_rank (int): Rank of the low-rank approximation.
      lora_train_bias (str): LoRA bias training mode.
code: def __init__(
      self,
      pretrained: Optional[str] = None,
      config: Optional[OPTConfig] = None,
      lora_rank: int = 0,
      lora_train_bias: str = "none",
      **kwargs,
  ) -> None:
      if pretrained is not None:
          model = OPTModel.from_pretrained(pretrained)
      elif config is not None:
          model = OPTModel(config)
      else:
          model = OPTModel(OPTConfig())
  
      value_head = nn.Linear(model.config.word_embed_proj_dim, 1)
      super().__init__(
          model, value_head, lora_rank, lora_train_bias, **kwargs
      )
  
file_path: ColossalAI/applications/Chat/coati/models/opt/opt_critic.py
imports: from typing import Optional
  import torch.nn
  from transformers.models.opt.configuration_opt import OPTConfig
  from transformers.models.opt.modeling_opt import OPTModel
  from ..base import Critic
label_desc: Use image node_id 1 to create a new OPTCritic object from inherited base classes: Critic with example: obj = OPTCritic(pretrained, config, lora_rank, lora_train_bias)