File size: 1,846 Bytes
e5c4555 | 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 56 57 58 | from transformers import PretrainedConfig
class RSLMConfig(PretrainedConfig):
model_type = "rslm"
def __init__(
self,
hidden_size=2048,
num_layers=24,
num_q_heads=16,
num_kv_heads=1,
head_dim=128,
intermediate_size=4352,
vocab_size=65536,
max_position_embeddings=262144,
original_max_position_embeddings=8192,
rope_theta=1000000.0,
rope_scaling=None,
window_size=4096,
global_layers_0idx=(5, 11, 17, 23),
evict_local_kv=True,
local_cache_keep=4096,
parallel_block=True,
rms_norm_eps=1e-6,
hidden_act="swiglu",
tie_word_embeddings=True,
bos_token_id=1,
eos_token_id=2,
pad_token_id=0,
**kwargs,
):
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_q_heads = num_q_heads
self.num_kv_heads = num_kv_heads
self.head_dim = head_dim
self.intermediate_size = intermediate_size
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.original_max_position_embeddings = original_max_position_embeddings
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.window_size = window_size
self.global_layers_0idx = list(global_layers_0idx)
self.evict_local_kv = evict_local_kv
self.local_cache_keep = local_cache_keep
self.parallel_block = parallel_block
self.rms_norm_eps = rms_norm_eps
self.hidden_act = hidden_act
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