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from transformers.models.gemma.configuration_gemma import GemmaConfig |
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class NewModelConfig(GemmaConfig): |
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def __init__( |
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self, |
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vocab_size=256030, |
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hidden_size=64, |
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intermediate_size=90, |
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num_hidden_layers=28, |
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num_attention_heads=16, |
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num_key_value_heads=16, |
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head_dim=256, |
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hidden_act="gelu_pytorch_tanh", |
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hidden_activation=None, |
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max_position_embeddings=1500, |
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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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eos_token_id=1, |
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bos_token_id=2, |
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tie_word_embeddings=True, |
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rope_theta=10000.0, |
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attention_bias=False, |
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attention_dropout=0.0, |
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**kwargs, |
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): |
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super().__init__(self, **kwargs) |
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@property |
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def num_heads(self): |
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return self.num_attention_heads |
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