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4e45e39 | 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 | """AQ model configuration.
AQ (Academic Quotient) — Zyora Labs' proprietary, from-scratch decoder
architecture: RMSNorm, rotary position embeddings, grouped-query attention,
SwiGLU feed-forward, tied embeddings.
"""
from transformers import PretrainedConfig
class AQConfig(PretrainedConfig):
model_type = "aq"
def __init__(
self,
vocab_size: int = 32000,
hidden_size: int = 1536,
intermediate_size: int = 4096,
num_hidden_layers: int = 48,
num_attention_heads: int = 24,
num_key_value_heads: int = 8,
head_dim: int = 64,
max_position_embeddings: int = 2048,
rope_theta: float = 10000.0,
rms_norm_eps: float = 1e-5,
tie_word_embeddings: bool = True,
bos_token_id: int = 0,
eos_token_id: int = 0,
pad_token_id: int = 0,
**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
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
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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