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from transformers.configuration_utils import PretrainedConfig
class PhariaConfig(PretrainedConfig):
model_type = "pharia-v1"
def __init__(
self,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
hidden_act="gelu",
hidden_size=512,
initializer_range=0.02,
intermediate_size=2048,
max_position_embeddings=8192,
model_type="pharia-v1",
num_attention_heads=4,
num_hidden_layers=4,
num_key_value_heads=2,
torch_dtype="bfloat16",
transformers_version="4.31.0.dev0",
use_cache=True,
vocab_size=128000,
mlp_bias=True,
attention_bias=True,
tie_word_embeddings=False,
attention_dropout=0.0,
rope_theta=1000000, # rotary_embeddingbase,
rope_scaling=None,
**kwargs,
):
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,
)
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.hidden_act = hidden_act
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.intermediate_size = intermediate_size
self.max_position_embeddings = max_position_embeddings
self.model_type = model_type
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = num_hidden_layers
self.num_key_value_heads = num_key_value_heads
self.torch_dtype = torch_dtype
self.transformers_version = transformers_version
self.use_cache = use_cache
self.vocab_size = vocab_size
self.mlp_bias = mlp_bias
self.attention_bias = attention_bias
self.tie_word_embeddings = tie_word_embeddings
self.attention_dropout = attention_dropout
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
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