|
|
|
from transformers.configuration_utils import PretrainedConfig |
|
from transformers.utils import logging |
|
|
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
LLAMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
|
"damerajee/Llamoe-test": "https://huggingface.co/damerajee/Llamoe-test/resolve/main/config.json", |
|
} |
|
|
|
|
|
class LlamoeConfig(PretrainedConfig): |
|
model_type = "Llamoe" |
|
keys_to_ignore_at_inference = ["past_key_values"] |
|
def __init__( |
|
self, |
|
vocab_size=32000, |
|
hidden_size=4096, |
|
intermediate_size=11008, |
|
num_hidden_layers=32, |
|
num_attention_heads=32, |
|
num_key_value_heads=32, |
|
head_dim=128, |
|
hidden_act="silu", |
|
max_position_embeddings=2048, |
|
initializer_range=0.02, |
|
rms_norm_eps=1e-06, |
|
use_cache=True, |
|
pad_token_id=0, |
|
eos_token_id=1, |
|
bos_token_id=2, |
|
tie_word_embeddings=False, |
|
rope_theta=10000.0, |
|
attention_bias=False, |
|
attention_dropout=0.0, |
|
num_experts_per_tok=2, |
|
num_local_experts=8, |
|
router_aux_loss_coef=0.02, |
|
output_router_logits=False, |
|
**kwargs, |
|
): |
|
self.vocab_size = vocab_size |
|
self.max_position_embeddings = max_position_embeddings |
|
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.head_dim = head_dim |
|
self.num_key_value_heads = num_key_value_heads |
|
self.hidden_act = hidden_act |
|
self.initializer_range = initializer_range |
|
self.rms_norm_eps = rms_norm_eps |
|
self.use_cache = use_cache |
|
self.rope_theta = rope_theta |
|
self.attention_bias = attention_bias |
|
self.attention_dropout = attention_dropout |
|
self.num_experts_per_tok = num_experts_per_tok |
|
self.num_local_experts = num_local_experts |
|
self.router_aux_loss_coef = router_aux_loss_coef |
|
self.output_router_logits = output_router_logits |
|
|
|
super().__init__( |
|
pad_token_id=pad_token_id, |
|
bos_token_id=bos_token_id, |
|
eos_token_id=eos_token_id, |
|
tie_word_embeddings=tie_word_embeddings, |
|
**kwargs, |
|
) |