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from transformers.configuration_utils import PretrainedConfig |
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from transformers.modeling_rope_utils import rope_config_validation |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class NeoLLMConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a `NeoLLMModel`. It is used to instantiate a |
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NeoLLM model according to the specified arguments, defining the model architecture. |
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Configuration objects inherit from `PretrainedConfig` and can be used to control the model outputs. |
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""" |
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model_type = "neollm" |
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keys_to_ignore_at_inference = [] |
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def __init__( |
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self, |
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vocab_size=200005, |
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hidden_size=512, |
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intermediate_size=1536, |
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num_hidden_layers=12, |
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num_attention_heads=8, |
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num_key_value_heads=2, |
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hidden_act="xielu", |
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max_position_embeddings=32768, |
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initializer_range=0.02, |
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rms_norm_eps=1e-6, |
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tie_word_embeddings=True, |
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rope_theta=10000.0, |
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rope_scaling=None, |
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partial_rotary_factor=0.25, |
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attention_bias=False, |
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attention_dropout=0.1, |
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head_dim=64, |
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fan_ratio=0.125, |
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fan_ratio_ffn=0.0625, |
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dropout_rate=0.1, |
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use_stack=True, |
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num_stack_heads=4, |
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stack_slots=24, |
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stack_d_model=16, |
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**kwargs, |
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): |
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) |
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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.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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self.partial_rotary_factor = partial_rotary_factor |
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self.attention_bias = attention_bias |
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self.attention_dropout = attention_dropout |
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self.head_dim = head_dim |
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rope_config_validation(self) |
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self.fan_ratio = fan_ratio |
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self.fan_ratio_ffn = fan_ratio_ffn |
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self.dropout_rate = dropout_rate |
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self.use_stack = use_stack |
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self.num_stack_heads = num_stack_heads |
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self.stack_slots = stack_slots |
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self.stack_d_model = stack_d_model |
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self.auto_map = { |
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"AutoConfig": "configuration_neollm.NeoLLMConfig", |
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"AutoModel": "modeling_neollm.NeoLLMModel", |
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"AutoModelForCausalLM": "modeling_neollm.NeoLLMForCausalLM" |
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} |
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__all__ = ["NeoLLMConfig"] |
