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from transformers import LlamaConfig |
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class CognitivessConfig(LlamaConfig): |
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model_type = "cognitivess" |
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def __init__( |
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self, |
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vocab_size=128256, |
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hidden_size=4096, |
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intermediate_size=14336, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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num_key_value_heads=8, |
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hidden_act="silu", |
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max_position_embeddings=8192, |
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initializer_range=0.02, |
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rms_norm_eps=1e-5, |
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use_cache=True, |
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pad_token_id=0, |
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bos_token_id=128000, |
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eos_token_id=128001, |
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tie_word_embeddings=False, |
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attention_dropout=0.0, |
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pretraining_tp=1, |
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rope_theta=500000.0, |
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**kwargs |
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): |
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super().__init__( |
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vocab_size=vocab_size, |
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hidden_size=hidden_size, |
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intermediate_size=intermediate_size, |
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num_hidden_layers=num_hidden_layers, |
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num_attention_heads=num_attention_heads, |
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num_key_value_heads=num_key_value_heads, |
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hidden_act=hidden_act, |
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max_position_embeddings=max_position_embeddings, |
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initializer_range=initializer_range, |
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rms_norm_eps=rms_norm_eps, |
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use_cache=use_cache, |
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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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pretraining_tp=pretraining_tp, |
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rope_theta=rope_theta, |
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**kwargs |
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) |
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self.attention_dropout = attention_dropout |