class_docstr: This is the configuration class to store the configuration of a [`OpenLlamaModel`]. It is used to instantiate an
  Open-Llama 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
  [s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1).
  
  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 32000):
          Vocabulary size of the Open-Llama model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed when calling [`OpenLlamaModel`]
      hidden_size (`int`, *optional*, defaults to 4096):
          Dimension of the hidden representations.
      intermediate_size (`int`, *optional*, defaults to 11008):
          Dimension of the MLP representations.
      num_hidden_layers (`int`, *optional*, defaults to 32):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 32):
          Number of attention heads for each attention layer in the Transformer encoder.
      hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
          The non-linear activation function (function or string) in the decoder.
      max_position_embeddings (`int`, *optional*, defaults to 2048):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      rms_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the rms normalization layers.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models). Only
          relevant if `config.is_decoder=True`.
      tie_word_embeddings(`bool`, *optional*, defaults to `False`):
          Whether to tie weight 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.
  
      Example:
  
  ```python
  >>> from transformers import OpenLlamaModel, OpenLlamaConfig
  
  >>> # Initializing a Open-Llama open_llama-7b style configuration
  >>> configuration = OpenLlamaConfig()
  
  >>> # Initializing a model from the open_llama-7b style configuration
  >>> model = OpenLlamaModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=100000,
      hidden_size=4096,
      intermediate_size=11008,
      num_hidden_layers=32,
      num_attention_heads=32,
      hidden_act="silu",
      max_position_embeddings=2048,
      initializer_range=0.02,
      rms_norm_eps=1e-6,
      use_cache=True,
      pad_token_id=0,
      bos_token_id=1,
      eos_token_id=2,
      tie_word_embeddings=False,
      use_memory_efficient_attention=True,
      hidden_dropout_prob=0.1,
      attention_dropout_prob=0.1,
      use_stable_embedding=True,
      shared_input_output_embedding=True,
      rope_scaling=None,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.hidden_size = hidden_size
      self.intermediate_size = intermediate_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.hidden_act = hidden_act
      self.initializer_range = initializer_range
      self.rms_norm_eps = rms_norm_eps
      self.use_cache = use_cache
      self.use_memory_efficient_attention = kwargs.pop(
          "use_memorry_efficient_attention",
          use_memory_efficient_attention,
      )
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_dropout_prob = attention_dropout_prob
      self.use_stable_embedding = use_stable_embedding
      self.shared_input_output_embedding = shared_input_output_embedding
      self.rope_scaling = rope_scaling
      self._rope_scaling_validation()
  
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          tie_word_embeddings=tie_word_embeddings,
          **kwargs,
      )
  
imports: from ....configuration_utils import PretrainedConfig
  from ....utils import logging
label_desc: Use image node_id 1 to create a new OpenLlamaConfig object from inherited base classes: PretrainedConfig with example: obj = OpenLlamaConfig(vocab_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, hidden_act, max_position_embeddings, initializer_range, rms_norm_eps, use_cache, pad_token_id, bos_token_id, eos_token_id, tie_word_embeddings, use_memory_efficient_attention, hidden_dropout_prob, attention_dropout_prob, use_stable_embedding, shared_input_output_embedding, rope_scaling)