File size: 1,413 Bytes
2b33fba | 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 | from __future__ import annotations
from transformers import PretrainedConfig
class TalkieConfig(PretrainedConfig):
model_type = "talkie"
def __init__(
self,
vocab_size: int = 65536,
n_layer: int = 40,
n_head: int = 40,
n_embd: int = 5120,
head_dim: int = 128,
max_position_embeddings: int = 2048,
rope_base: int = 1_000_000,
logit_scale: float = 1.0,
use_cache: bool = True,
tie_word_embeddings: bool = False,
bos_token_id: int | None = None,
eos_token_id: int | list[int] = 65535,
pad_token_id: int | None = None,
**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.vocab_size = vocab_size
self.n_layer = n_layer
self.n_head = n_head
self.n_embd = n_embd
self.head_dim = head_dim
self.max_position_embeddings = max_position_embeddings
self.rope_base = rope_base
self.logit_scale = logit_scale
self.use_cache = use_cache
# Common Transformers aliases used by generation/cache helpers.
self.hidden_size = n_embd
self.num_hidden_layers = n_layer
self.num_attention_heads = n_head
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