File size: 1,915 Bytes
df5a117 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | from transformers import PretrainedConfig
class PersadianNanoV4Config(PretrainedConfig):
model_type = "persadian_nano_v4"
def __init__(
self,
hidden_size=512,
intermediate_size=1024,
num_hidden_layers=12,
num_attention_heads=8,
num_key_value_heads=4,
num_experts=4,
num_experts_per_tok=2,
progressive_experts=True,
min_experts=1,
max_experts=2,
use_hyper_connection=True,
use_compressed_attention=True,
compress_ratio=4,
vocab_size=50257,
max_position_embeddings=2048,
bos_token_id=50256,
eos_token_id=50256,
rope_theta=10000.0,
layer_norm_eps=1e-5,
dropout=0.1,
attention_dropout=0.0,
use_flash_attention=True,
**kwargs
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.num_experts = num_experts
self.num_experts_per_tok = num_experts_per_tok
self.progressive_experts = progressive_experts
self.min_experts = min_experts
self.max_experts = max_experts
self.use_hyper_connection = use_hyper_connection
self.use_compressed_attention = use_compressed_attention
self.compress_ratio = compress_ratio
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.rope_theta = rope_theta
self.layer_norm_eps = layer_norm_eps
self.dropout = dropout
self.attention_dropout = attention_dropout
self.use_flash_attention = use_flash_attention
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