File size: 2,268 Bytes
c8de259 | 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 59 | """HuggingFace configuration for the Rose X1 architecture."""
from transformers import PretrainedConfig
class RoseX1Config(PretrainedConfig):
model_type = "rose_x1"
def __init__(
self,
vocab_size=16384,
hidden_size=512,
intermediate_size=1408,
num_hidden_layers=14,
num_attention_heads=8,
num_key_value_heads=2,
head_dim=None,
max_position_embeddings=1024,
hidden_act="silu",
rms_norm_eps=1e-5,
attention_bias=False,
mlp_bias=False,
attention_dropout=0.0,
tie_word_embeddings=True,
rope_theta=100000.0,
rope_scaling=None,
initializer_range=0.02,
use_cache=True,
# ── Rose X1 specifics ──────────────────────────────────────────────
use_qk_norm=True,
refresh_gate_enabled=True,
refresh_gate_inject_layers=None,
refresh_gate_kernel_size=9,
**kwargs,
):
self.vocab_size = vocab_size
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.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.hidden_act = hidden_act
self.rms_norm_eps = rms_norm_eps
self.attention_bias = attention_bias
self.mlp_bias = mlp_bias
self.attention_dropout = attention_dropout
self.tie_word_embeddings = tie_word_embeddings
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.initializer_range = initializer_range
self.use_cache = use_cache
self.use_qk_norm = use_qk_norm
self.refresh_gate_enabled = refresh_gate_enabled
self.refresh_gate_inject_layers = (
list(refresh_gate_inject_layers) if refresh_gate_inject_layers else []
)
self.refresh_gate_kernel_size = refresh_gate_kernel_size
super().__init__(**kwargs) |