ZjdSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) zOpenAI GPT-2 configuration)strict)PreTrainedConfig)auto_docstringzopenai-community/gpt2) checkpointc\rSrSr%SrSrS/rSSSSS .rS r\ \ S 'S r \ \ S'S r \ \ S'Sr \ \ S'Sr\ \ S'Sr\ S-\ S'Sr\\ S'Sr\\ -\ S'Sr\\ -\ S'Sr\\ -\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\S-\ S'Sr\\ S 'Sr\\ -\ S!'Sr\\ S"'Sr\\ S#'S$r \ S-\ S%'S$r!\ \"\ -S-\ S&'Sr#\ S-\ S''S(r$\\ S)'S(r%\\ S*'S(r&\\ S+'Sr'\\ S,'S-r(g). GPT2Configa summary_type (`string`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`]. 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 [`GPT2DoubleHeadsModel`]. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in for the multiple choice head in [`GPT2DoubleHeadsModel`]. 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 [`GPT2DoubleHeadsModel`]. 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 [`GPT2DoubleHeadsModel`]. The dropout ratio to be used after the projection and activation. scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): Whether to additionally scale attention weights by `1 / layer_idx + 1`. reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision. Example: ```python >>> from transformers import GPT2Config, GPT2Model >>> # Initializing a GPT2 configuration >>> configuration = GPT2Config() >>> # Initializing a model (with random weights) from the configuration >>> model = GPT2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```gpt2past_key_valuesn_embd n_positionsn_headn_layer) hidden_sizemax_position_embeddingsnum_attention_headsnum_hidden_layersiQ vocab_sizeii Nn_innergelu_newactivation_functiong? resid_pdrop embd_pdrop attn_pdropgh㈵>layer_norm_epsilong{Gz?initializer_range cls_index summary_typeTsummary_use_projsummary_activationsummary_proj_to_labelssummary_first_dropoutscale_attn_weights use_cacheiP bos_token_id eos_token_id pad_token_idFscale_attn_by_inverse_layer_idxreorder_and_upcast_attnadd_cross_attentiontie_word_embeddings))__name__ __module__ __qualname____firstlineno____doc__ model_typekeys_to_ignore_at_inference attribute_maprint__annotations__rr rrrrstrrfloatrrrrr r!boolr"r#r$r%r&r'r(listr)r*r+r,r-__static_attributes__r.|/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/gpt2/configuration_gpt2.pyr r sj) VJ#4"5#0'& MJKFCGSFCGS4Z))"K"!J !!J ! $$#u##L##!d!%)d )#'D'),53;,##It$L#*$+0L#S /D(0#L#*#,1#T1$)T) %% $$r>r N) r3huggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__r.r>r?rDsJ!.3#23N%!N%4N%b .r>