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5974fd0 | 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 | """HuggingFace Transformers config for Ivme-Conversate-v2."""
from transformers import PretrainedConfig
class IvmeConfig(PretrainedConfig):
model_type = "ivme"
def __init__(
self,
vocab_size: int = 16_000,
hidden_dim: int = 384,
n_layers: int = 10,
n_heads: int = 6,
context_len: int = 1024,
ffn_mult: float = 4.0,
rope_theta: float = 10_000.0,
norm_eps: float = 1e-5,
tie_embeddings: bool = True,
dropout: float = 0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.n_heads = n_heads
self.context_len = context_len
self.ffn_mult = ffn_mult
self.rope_theta = rope_theta
self.norm_eps = norm_eps
self.dropout = dropout
assert hidden_dim % n_heads == 0, "hidden_dim must be divisible by n_heads"
self.max_position_embeddings = context_len
self.num_hidden_layers = n_layers
self.num_attention_heads = n_heads
self.hidden_size = hidden_dim
kwargs.setdefault("tie_word_embeddings", tie_embeddings)
super().__init__(**kwargs)
@property
def head_dim(self) -> int:
return self.hidden_dim // self.n_heads
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