docstr: Saves the base LLM model to the given path.
docstr_tok: ["Saves","the","base","LLM","model","to","the","given","path","."]
code: def save_base_model(self, save_path):
      """Saves the base LLM model to the given path."""
      # TODO: see the "TODO" statement from "LLM.save()" in this module.
      if self.config_obj.trainer.type != "none":
          weights_save_path = os.path.join(
              save_path, MODEL_WEIGHTS_FILE_NAME
          )
          self.model.base_model.save_pretrained(weights_save_path)
          # While this class initializes the tokenizer (from the base_model) automatically, and hence does not
          # need to be saved if inference is to be done using LudwigModel.predict(), the rationale for saving the
          # tokenizer to HuggingFace Hub is to provide access to models fine-tuned and persisted to HuggingFace Hub
          # using Ludwig at a later time, with the ability to perform inference, independently of Ludwig itself.
          self.tokenizer.save_pretrained(weights_save_path)
      else:
          logger.info("Skipped saving LLM without weight adjustments.")
  
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 25 for calling the LLM obj's underlying member method code with example usage: obj.save_base_model(save_path) without return types