class_docstr: Model for causal language modeling problem type.
class_docstr_tok: ["Model","for","causal","language","modeling","problem","type","."]
docstr: Args:
      cfg: config with all the hyperparameters
docstr_tok: ["Args",":","cfg",":","config","with","all","the","hyperparameters"]
code: def __init__(self, cfg: Any):
      """
      Args:
          cfg: config with all the hyperparameters
      """
  
      super(Model, self).__init__()
  
      self.cfg = cfg
      self.backbone, self.backbone_config = create_nlp_backbone(
          cfg, model_class=AutoModelForCausalLM
      )
  
      if cfg.training.lora:
          self.backbone = prepare_lora(cfg, self.backbone)
  
      self.loss_fn = self.cfg.training.loss_class.get(
          self.cfg.training.loss_function
      )(self.cfg)
  
      if self.cfg.prediction.metric == "Perplexity":
          self.perplexity = Perplexity(self.cfg, reduce=False)
  
file_path: h2o-llmstudio/llm_studio/src/models/text_causal_language_modeling_model.py
imports: import logging
  from typing import Any, Dict
  from torch import nn
  from transformers import AutoModelForCausalLM
  from llm_studio.src.metrics.text_causal_language_modeling_metrics import Perplexity
  from llm_studio.src.utils.data_utils import batch_padding
  from llm_studio.src.utils.modeling_utils import create_nlp_backbone, generate, prepare_lora
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(cfg)