class_docstr: This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
  model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
  defaults will yield a similar configuration to that of the
  [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  
  Args:
      vocab_size (`int`, *optional*, defaults to 65024):
          Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`FalconModel`]
      hidden_size (`int`, *optional*, defaults to 4544):
          Dimension of the hidden representations.
      num_hidden_layers (`int`, *optional*, defaults to 32):
          Number of hidden layers in the Transformer decoder.
      num_attention_heads (`int`, *optional*, defaults to 71):
          Number of attention heads for each attention layer in the Transformer encoder.
      layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
          The epsilon used by the layer normalization layers.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether the model should return the last key/values attentions (not used by all models). Only relevant if
          `config.is_decoder=True`.
      hidden_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for MLP layers.
      attention_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for attention layers.
      num_kv_heads (`int`, *optional*):
          Number of key-value heads to use per attention layer. If unset, defaults to the same value as
          `num_attention_heads`.
      alibi (`bool`, *optional*, defaults to `False`):
          Whether to use ALiBi positional biases during self-attention.
      new_decoder_architecture (`bool`, *optional*, defaults to `False`):
          Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
          arguments are ignored, as the new decoder always uses parallel attention.
      multi_query (`bool`, *optional*, defaults to `True`):
          Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
      parallel_attn (`bool`, *optional*, defaults to `True`):
          Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
          instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
      bias (`bool`, *optional*, defaults to `False`):
          Whether to use bias on Linear layers.
      max_position_embeddings (`int`, *optional*, defaults to 2048):
          The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained
          Falcon models with RoPE support up to 2048 tokens.
      rope_theta (`float`, *optional*, defaults to 10000.0):
          The base period of the RoPE embeddings.
      rope_scaling (`Dict`, *optional*):
          Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
          strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
          `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
          `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
          these scaling strategies behave:
          https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
          experimental feature, subject to breaking API changes in future versions.
      bos_token_id (`int`, *optional*, defaults to 11):
          The id of the "beginning-of-sequence" token.
      eos_token_id (`int`, *optional*, defaults to 11):
          The id of the "end-of-sequence" token.
  
  Example:
  
  ```python
  >>> from transformers import FalconModel, FalconConfig
  
  >>> # Initializing a small (2-layer) Falcon configuration
  >>> configuration = FalconConfig(num_hidden_layers=2)
  
  >>> # Initializing a model from the small configuration
  >>> model = FalconModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      vocab_size=65024,
      hidden_size=4544,
      num_hidden_layers=32,
      num_attention_heads=71,
      layer_norm_epsilon=1e-5,
      initializer_range=0.02,
      use_cache=True,
      hidden_dropout=0.0,
      attention_dropout=0.0,
      num_kv_heads=None,
      alibi=False,
      new_decoder_architecture=False,
      multi_query=True,
      parallel_attn=True,
      bias=False,
      max_position_embeddings=2048,
      rope_theta=10000.0,
      rope_scaling=None,
      bos_token_id=11,
      eos_token_id=11,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      # Backward compatibility with n_embed kwarg
      n_embed = kwargs.pop("n_embed", None)
      self.hidden_size = hidden_size if n_embed is None else n_embed
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.layer_norm_epsilon = layer_norm_epsilon
      self.initializer_range = initializer_range
      self.use_cache = use_cache
      self.hidden_dropout = hidden_dropout
      self.attention_dropout = attention_dropout
  
      self.bos_token_id = bos_token_id
      self.eos_token_id = eos_token_id
      self.num_kv_heads = (
          num_attention_heads if num_kv_heads is None else num_kv_heads
      )
      self.alibi = alibi
      self.new_decoder_architecture = new_decoder_architecture
      self.multi_query = (
          multi_query  # Ignored when new_decoder_architecture is True
      )
      self.parallel_attn = parallel_attn
      self.bias = bias
      self.max_position_embeddings = max_position_embeddings
      self.rope_theta = rope_theta
      self.rope_scaling = rope_scaling
      self._rope_scaling_validation()
  
      super().__init__(
          bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
      )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new FalconConfig object from inherited base classes: PretrainedConfig with example: obj = FalconConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, layer_norm_epsilon, initializer_range, use_cache, hidden_dropout, attention_dropout, num_kv_heads, alibi, new_decoder_architecture, multi_query, parallel_attn, bias, max_position_embeddings, rope_theta, rope_scaling, bos_token_id, eos_token_id)