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from transformers.configuration_utils import PretrainedConfig
class GPTPanguConfig(PretrainedConfig):
model_type = "gpt_pangu"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=40000,
max_position_embeddings=1024,
hidden_size=2560,
intermediate_size=None,
num_layers=32,
num_heads=32,
activation_function="gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
scale_attn_weights=True,
initializer_range=0.02,
summary_type="cls_index",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
use_cache=True,
bos_token_id=9,
eos_token_id=9,
**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_layers = num_layers
self.num_heads = num_heads
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.scale_attn_weights = scale_attn_weights
self.initializer_range = initializer_range
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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