fTh`SrSSKJr SSKJr \R "\5r"SS\5rS/r g)zOpenAI GPT configuration)PretrainedConfig)loggingcb^\rSrSrSrSrSSSSS.rS U4S jjrS rU=r $) OpenAIGPTConfiga This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a [`TFOpenAIGPTModel`]. It is used to instantiate a GPT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the GPT [openai-community/openai-gpt](https://huggingface.co/openai-community/openai-gpt) architecture from OpenAI. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 40478): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OpenAIGPTModel`] or [`TFOpenAIGPTModel`]. n_positions (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_embd (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. afn (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. summary_type (`str`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. The dropout ratio to be used after the projection and activation. Examples: ```python >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel >>> # Initializing a GPT configuration >>> configuration = OpenAIGPTConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = OpenAIGPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```z openai-gpt n_positionsn_embdn_headn_layer)max_position_embeddings hidden_sizenum_attention_headsnum_hidden_layersc >XlX lX0lX@lXPlX`lXplXlXlXl Xl Xl Xl Xl UUlXl[ TU]D"S0UD6 g)N) vocab_sizerr r r afn resid_pdrop embd_pdrop attn_pdroplayer_norm_epsiloninitializer_range summary_typesummary_use_projsummary_activationsummary_first_dropoutsummary_proj_to_labelssuper__init__)selfrrr r r rrrrrrrrrrrkwargs __class__s g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/openai/configuration_openai.pyrOpenAIGPTConfig.__init__usp(%&   &$$"4!2( 0"4%:"&<# "6")rrrrrr r r rrrrrrrr)iii r&gelu皙?r(r(gh㈵>g{Gz? cls_indexTNTr() __name__ __module__ __qualname____firstlineno____doc__ model_type attribute_mapr__static_attributes__ __classcell__)r"s@r#rrs`Q fJ#0'& M  #!#$#$#r%rN) r.configuration_utilsrutilsr get_loggerr*loggerr__all__rr%r#r8s< 3   H %@#&@#F  r%