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# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
from transformers.configuration_utils import PretrainedConfig
class BaichuanConfig(PretrainedConfig):
model_type = "baichuan"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=64000,
hidden_size=5120,
intermediate_size=13696,
num_hidden_layers=40,
num_attention_heads=40,
hidden_act="silu",
model_max_length=4096,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
gradient_checkpointing=False,
z_loss_weight=0,
**kwargs,
):
self.vocab_size = vocab_size
self.model_max_length = model_max_length
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.use_cache = use_cache
self.z_loss_weight = z_loss_weight
self.gradient_checkpointing = (gradient_checkpointing,)
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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