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from transformers import PretrainedConfig |
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class RECASTMLP_llama(PretrainedConfig): |
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model_type = "recastmlp_llama" |
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attribute_map = { |
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"hidden_size": "hidden_size", |
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"num_attention_heads": "num_attention_heads", |
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} |
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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=131072, |
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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=None, |
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bos_token_id=128000, |
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eos_token_id=128001, |
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pretraining_tp=1, |
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tie_word_embeddings=False, |
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rope_theta=500000.0, |
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rope_scaling={ |
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"factor": 8.0, |
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"low_freq_factor": 1.0, |
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"high_freq_factor": 4.0, |
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"original_max_position_embeddings": 8192, |
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"rope_type": "llama3", |
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}, |
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attention_bias=False, |
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attention_dropout=0.0, |
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mlp_bias=False, |
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num_templates=4, |
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num_groups=8, |
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num_cf=1, |
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torch_dtype="bfloat16", |
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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.num_key_value_heads = num_key_value_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.pretraining_tp = pretraining_tp |
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self.use_cache = use_cache |
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self.mlp_bias = mlp_bias |
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self.attention_bias = attention_bias |
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self.attention_dropout = attention_dropout |
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self.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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self.torch_dtype = torch_dtype |
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self.num_templates = num_templates |
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self.num_groups = num_groups |
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self.num_cf = num_cf |
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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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