File size: 2,526 Bytes
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from transformers import PretrainedConfig
class NeoLMConfig(PretrainedConfig):
model_type = "neolm"
def __init__(
self,
vocab_size: int = 32000,
max_position_embeddings: int = 512,
hidden_size: int = 512,
num_attention_heads: int = 8,
num_key_value_heads: int | None = None,
num_hidden_layers: int = 8,
intermediate_size: int = None,
dropout: float = 0.0,
tie_word_embeddings: bool = True,
rope_base: int = 10_000,
mlp_multiple_of: int = 256,
rms_norm_eps: float = 1e-6,
qk_norm: bool = True,
scale_residual_init: bool = True,
use_cache: bool = False,
pad_token_id: int = None,
bos_token_id: int = None,
eos_token_id: int = None,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
assert hidden_size % num_attention_heads == 0, (
f"hidden_size ({hidden_size}) يجب أن يقسم على num_attention_heads ({num_attention_heads})"
)
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
if not (
1 <= num_key_value_heads <= num_attention_heads
and num_attention_heads % num_key_value_heads == 0
):
raise ValueError(
"num_key_value_heads must be positive "
"and divide num_attention_heads"
)
if intermediate_size is None:
intermediate_size = mlp_multiple_of * math.ceil(
int(8 * hidden_size / 3) / mlp_multiple_of
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.dropout = dropout
self.tie_word_embeddings = tie_word_embeddings
self.rope_base = rope_base
self.mlp_multiple_of = mlp_multiple_of
self.rms_norm_eps = rms_norm_eps
self.qk_norm = qk_norm
self.scale_residual_init = scale_residual_init
self.use_cache = use_cache
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