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from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
CM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class JiutianConfig(PretrainedConfig):
model_type = "jiutian"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=152064,
hidden_size=8192,
intermediate_size=13312,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=151645,
bos_token_id=None,
eos_token_id=151645,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=500000,
rope_scaling=None,
qkv_bias=True,
attention_dropout=0.0,
**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.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = None
self.qkv_bias = qkv_bias
self.attention_dropout = attention_dropout
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
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,
)
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