class_docstr: Configuration for the model.
  
  Args:
      model: Name or path of the huggingface model to use.
      tokenizer: Name or path of the huggingface tokenizer to use.
      tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
          available, and "slow" will always use the slow tokenizer.
      trust_remote_code: Trust remote code (e.g., from HuggingFace) when
          downloading the model and tokenizer.
      download_dir: Directory to download and load the weights, default to the
          default cache directory of huggingface.
      load_format: The format of the model weights to load:
          "auto" will try to load the weights in the safetensors format and
              fall back to the pytorch bin format if safetensors format is
              not available.
          "pt" will load the weights in the pytorch bin format.
          "safetensors" will load the weights in the safetensors format.
          "npcache" will load the weights in pytorch format and store
              a numpy cache to speed up the loading.
          "dummy" will initialize the weights with random values, which is
              mainly for profiling.
      dtype: Data type for model weights and activations. The "auto" option
          will use FP16 precision for FP32 and FP16 models, and BF16 precision
          for BF16 models.
      seed: Random seed for reproducibility.
      revision: The specific model version to use. It can be a branch name,
          a tag name, or a commit id. If unspecified, will use the default
          version.
      tokenizer_revision: The specific tokenizer version to use. It can be a
          branch name, a tag name, or a commit id. If unspecified, will use
          the default version.
      max_model_len: Maximum length of a sequence (including prompt and
          output). If None, will be derived from the model.
      quantization: Quantization method that was used to quantize the model
          weights. If None, we assume the model weights are not quantized.
      device: The device to be used for the model. If None, we will default
          to use CPU as the device.
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code: def _verify_quantization(self) -> None:
      supported_quantization = ["awq"]
      if self.quantization is None:
          return
      quantization = self.quantization.lower()
      if quantization not in supported_quantization:
          invalidInputError(
              False,
              f"Unknown quantization: {self.quantization}. Must be one of "
              f"{supported_quantization}.",
          )
      self.quantization = quantization
  
imports: from typing import Optional
  import torch
  from transformers import AutoConfig, PretrainedConfig
  from bigdl.llm.vllm.logger import init_logger
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 4 for calling the ModelConfig obj's underlying member method code with example usage: obj._verify_quantization() without return types