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93e48a8 | 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 | """ForgePlex-M2 model configuration for Hugging Face Transformers."""
from transformers import PretrainedConfig
class ForgePlexM2Config(PretrainedConfig):
model_type = "forgeplex_m2"
def __init__(
self,
vocab_size: int = 4096,
hidden_size: int = 256,
num_hidden_layers: int = 11,
num_attention_heads: int = 8,
num_key_value_heads: int = 2,
head_dim: int = 32,
intermediate_size: int = 707,
max_position_embeddings: int = 1024,
rope_theta: float = 5000.0,
rms_norm_eps: float = 1e-6,
tie_word_embeddings: bool = True,
use_xsa_projection: bool = False,
use_attn_output_gate: bool = True,
use_refresh_gate: bool = True,
inject_layers: list | tuple | None = None,
refresh_kernel: int = 9,
bos_token_id: int = 0,
eos_token_id: int = 0,
pad_token_id: int = 1,
**kwargs,
):
if inject_layers is None:
inject_layers = [5, 10]
self.vocab_size = vocab_size
self.hidden_size = hidden_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.head_dim = head_dim
self.intermediate_size = intermediate_size
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
self.use_xsa_projection = use_xsa_projection
self.use_attn_output_gate = use_attn_output_gate
self.use_refresh_gate = use_refresh_gate
self.inject_layers = list(int(i) for i in inject_layers)
self.refresh_kernel = refresh_kernel
super().__init__(
tie_word_embeddings=tie_word_embeddings,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
**kwargs,
)
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