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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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CYBERAI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"CyberCapstone/CyberAI": "https://huggingface.co/CyberCapstone/CyberAI/blob/main/config.json"
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}
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class CyberAIConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CyberAIModel`]. It is used to instantiate a CyberAI
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the CyberCapstone/CyberAI architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 65024):
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Vocabulary size of the CyberAI model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`CyberAIModel`].
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 64):
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Number of attention heads for each attention layer in the Transformer encoder.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether the model should return the last key/values attentions (not used by all models). Only relevant if
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`config.is_decoder=True`.
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hidden_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for MLP layers.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for attention layers.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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attention_bias (`bool`, defaults to `True`, *optional*):
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Whether to use a bias in the query, key, value, and output projection layers during self-attention.
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Example:
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```python
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>>> from transformers import CyberAIModel, CyberAIConfig
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>>> # Initializing a CyberCapstone/CyberAI configuration
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>>> configuration = CyberAIConfig()
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>>> # Initializing a model from the configuration
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>>> model = CyberAIModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "cyberai"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=65024,
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hidden_size=4096,
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num_hidden_layers=32,
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num_attention_heads=64,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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use_cache=True,
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hidden_dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=2048,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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attention_bias=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.max_position_embeddings = max_position_embeddings
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.attention_bias = attention_bias
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs,
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)
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if __name__ == "__main__":
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config = CyberAIConfig()
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print(config) |
