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""" NEW 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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class NewConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to |
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instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a |
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configuration with the defaults will yield a similar configuration to that of the NEW |
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[izhx/new-base-en](https://huggingface.co./izhx/new-base-en) architecture. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 30522): |
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Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`]. |
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hidden_size (`int`, *optional*, defaults to 768): |
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Dimensionality of the encoder layers and the pooler layer. |
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num_hidden_layers (`int`, *optional*, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (`int`, *optional*, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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intermediate_size (`int`, *optional*, defaults to 3072): |
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. |
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, |
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`"relu"`, `"silu"` and `"gelu_new"` are supported. |
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): |
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The dropout ratio for the attention probabilities. |
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max_position_embeddings (`int`, *optional*, defaults to 512): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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type_vocab_size (`int`, *optional*, defaults to 2): |
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The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`]. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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layer_norm_eps (`float`, *optional*, defaults to 1e-12): |
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The epsilon used by the layer normalization layers. |
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position_embedding_type (`str`, *optional*, defaults to `"rope"`): |
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Type of position embedding. Choose one of `"absolute"`, `"rope"`. |
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rope_theta (`float`, *optional*, defaults to 10000.0): |
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The base period of the RoPE embeddings. |
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rope_scaling (`Dict`, *optional*): |
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling |
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is |
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update |
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how |
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these scaling strategies behave: |
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an |
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experimental feature, subject to breaking API changes in future versions. |
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classifier_dropout (`float`, *optional*): |
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The dropout ratio for the classification head. |
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Examples: |
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```python |
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>>> from transformers import NewConfig, NewModel |
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>>> # Initializing a NEW izhx/new-base-en style configuration |
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>>> configuration = NewConfig() |
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>>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration |
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>>> model = NewModel(configuration) |
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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```""" |
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model_type = "new" |
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def __init__( |
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self, |
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vocab_size=30528, |
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hidden_size=768, |
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num_hidden_layers=12, |
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num_attention_heads=12, |
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intermediate_size=3072, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.1, |
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attention_probs_dropout_prob=0.0, |
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max_position_embeddings=2048, |
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type_vocab_size=1, |
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initializer_range=0.02, |
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layer_norm_type='layer_norm', |
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layer_norm_eps=1e-12, |
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position_embedding_type="rope", |
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rope_theta=10000.0, |
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rope_scaling=None, |
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classifier_dropout=None, |
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pack_qkv=True, |
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unpad_inputs=False, |
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use_memory_efficient_attention=False, |
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logn_attention_scale=False, |
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logn_attention_clip1=False, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.vocab_size = vocab_size |
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self.hidden_size = hidden_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.intermediate_size = intermediate_size |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.max_position_embeddings = max_position_embeddings |
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self.type_vocab_size = type_vocab_size |
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self.initializer_range = initializer_range |
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self.layer_norm_type = layer_norm_type |
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self.layer_norm_eps = layer_norm_eps |
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self.position_embedding_type = position_embedding_type |
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self.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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self.classifier_dropout = classifier_dropout |
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self.pack_qkv = pack_qkv |
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self.unpad_inputs = unpad_inputs |
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self.use_memory_efficient_attention = use_memory_efficient_attention |
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self.logn_attention_scale = logn_attention_scale |
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self.logn_attention_clip1 = logn_attention_clip1 |
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