class_docstr: This is a wrapper class about all possible attributes and features that you can
   play with a model that has been loaded using `auto-awq` library awq quantization
   relying on auto_awq backend.
  
  Args:
      bits (`int`, *optional*, defaults to 4):
          The number of bits to quantize to.
      group_size (`int`, *optional*, defaults to 128):
          The group size to use for quantization.
          Recommended value is 128 and -1 uses per-column quantization.
      zero_point (`bool`, *optional*, defaults to `True`):
          Whether to use zero point quantization.
      version (`AWQLinearVersion`, *optional*, defaults to
      `AWQLinearVersion.GEMM`):
          The version of the quantization algorithm to use.
          GEMM is better for big batch_size (e.g. >= 8) otherwise,
          GEMV is better (e.g. < 8 )
      backend (`AwqBackendPackingMethod`, *optional*, defaults to
      `AwqBackendPackingMethod.AUTOAWQ`):
          The quantization backend. Some models might be quantized using `llm-awq` backend.
          This is useful for users that quantize their own models using `llm-awq` library.
      modules_to_not_convert (`list`, *optional*, defaults to []):
          The modules in qblock while not quantized.
code: def __init__(
      self,
      bits: int = 4,
      group_size: int = 128,
      zero_point: bool = True,
      version: AWQLinearVersion = AWQLinearVersion.GEMM,
      backend: AwqBackendPackingMethod = AwqBackendPackingMethod.AUTOAWQ,
      modules_to_not_convert: list = [],
      **kwargs,
  ):
      self.quant_method = QuantizationMethod.AWQ
  
      self.bits = bits
      self.group_size = group_size
      self.zero_point = zero_point
      self.version = version.lower()
      self.backend = backend
      self.modules_to_not_convert = modules_to_not_convert
  
      self.post_init()
  
imports: from dataclasses import dataclass
  from bigdl.llm.utils.common import invalidInputError
  from transformers.utils.quantization_config import QuantizationConfigMixin
  from transformers.utils.quantization_config import AwqBackendPackingMethod, AWQLinearVersion, QuantizationMethod
label_desc: Use image node_id 1 to create a new AwqConfig object from inherited base classes: QuantizationConfigMixin with example: obj = AwqConfig(bits, group_size, zero_point, version, backend, modules_to_not_convert)