docstr: Updates the model's metrics given targets and predictions for fine-tuning.
docstr_tok: ["Updates","the","model","'s","metrics","given","targets","and","predictions","for","fine-tuning","."]
code: def update_metrics_finetune_llm(self, targets, predictions):
      """Updates the model's metrics given targets and predictions for fine-tuning."""
      _targets, _predictions = targets, predictions
      for of_name, of_obj in self.output_features.items():
          if isinstance(of_obj, TextOutputFeature):
              # Update the target tensor to enable text metric evaluation. This pads the target tensor with -100s
              # to match the prediction length and depends on how much of the target tensor was included in the
              # forward pass.
              _targets = self._update_target_tensor_for_finetuning(
                  _targets, _predictions, of_name
              )
              if isinstance(of_obj, TextOutputFeature):
                  of_obj.update_metrics(
                      _targets[of_name],
                      _predictions[of_name],
                      self.tokenizer,
                  )
              else:
                  of_obj.update_metrics(
                      _targets[of_name], _predictions[of_name]
                  )
              continue
  
          of_obj.update_metrics(
              _targets[of_name], _predictions[of_name]
          )
  
      eval_loss, additional_losses = self.eval_loss(
          _targets, _predictions
      )
      self.eval_loss_metric.update(eval_loss)
      self.eval_additional_losses_metrics.update(additional_losses)
  
imports: import contextlib
  import logging
  import os
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from transformers import AutoConfig, GenerationConfig
  from ludwig.accounting.used_tokens import get_used_tokens_for_llm
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, MODEL_LLM, PREDICTIONS, TEXT, USED_TOKENS
  from ludwig.features.base_feature import ModuleWrapper, OutputFeature
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.features.text_feature import TextOutputFeature
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.modules.training_hooks import NEFTuneHook
  from ludwig.schema.features.base import BaseOutputFeatureConfig, FeatureCollection
  from ludwig.schema.model_types.llm import LLMModelConfig
  from ludwig.utils.augmentation_utils import AugmentationPipelines
  from ludwig.utils.data_utils import clear_data_cache
  from ludwig.utils.llm_quantization_utils import convert_quantized_linear_to_linear
  from ludwig.utils.llm_utils import add_left_padding, generate_merged_ids, get_context_len, get_realigned_target_and_prediction_tensors_for_inference, initialize_adapter, load_pretrained_from_config, pad_target_tensor_for_fine_tuning, remove_left_padding, to_device
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.output_feature_utils import set_output_feature_tensor
  from ludwig.utils.tokenizers import HFTokenizer
  from ludwig.utils.torch_utils import reg_loss
label_desc: Use image node_id 20 for calling the LLM obj's underlying member method code with example usage: obj.update_metrics_finetune_llm(targets, predictions) without return types