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from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
CYBERAI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"CyberCapstone/CyberAI": "https://huggingface.co/CyberCapstone/CyberAI/blob/main/config.json"
}
class CyberAIConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CyberAIModel`]. It is used to instantiate a CyberAI
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 CyberCapstone/CyberAI 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 CyberAI model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`CyberAIModel`].
hidden_size (`int`, *optional*, defaults to 4096):
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 64):
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 0.1):
The dropout probability for MLP layers.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for attention layers.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
attention_bias (`bool`, defaults to `True`, *optional*):
Whether to use a bias in the query, key, value, and output projection layers during self-attention.
Example:
```python
>>> from transformers import CyberAIModel, CyberAIConfig
>>> # Initializing a CyberCapstone/CyberAI configuration
>>> configuration = CyberAIConfig()
>>> # Initializing a model from the configuration
>>> model = CyberAIModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "cyberai"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=65024,
hidden_size=4096,
num_hidden_layers=32,
num_attention_heads=64,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
hidden_dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=2048,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
attention_bias=True,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
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.max_position_embeddings = max_position_embeddings
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.attention_bias = attention_bias
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
# Example usage
if __name__ == "__main__":
config = CyberAIConfig()
print(config) |