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""" Nanbeige model configuration""" |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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NANBEIGE_PRETRAINED_CONFIG_ARCHIVE_MAP = {} |
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class NanbeigeConfig(PretrainedConfig): |
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model_type = "nanbeige" |
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def __init__( |
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self, |
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vocab_size=32000, |
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hidden_size=4096, |
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intermediate_size=11008, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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hidden_act="silu", |
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max_position_embeddings=2048, |
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initializer_range=0.02, |
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rms_norm_eps=1e-6, |
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use_cache=True, |
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pad_token_id=0, |
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bos_token_id=1, |
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eos_token_id=2, |
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tie_word_embeddings=False, |
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yarn_scale=1., |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.max_position_embeddings = max_position_embeddings |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.hidden_act = hidden_act |
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self.initializer_range = initializer_range |
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self.rms_norm_eps = rms_norm_eps |
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self.use_cache = use_cache |
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self.yarn_scale = yarn_scale |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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