o h @sDdZddlmZddlmZeeZdddZGdddeZ d S) z Phi model configuration)PretrainedConfig)loggingzAhttps://huggingface.co/susnato/phi-1_dev/resolve/main/config.jsonzChttps://huggingface.co/susnato/phi-1_5_dev/resolve/main/config.json)zsusnato/phi-1_devzsusnato/phi-1_5_devcs\eZdZdZdZdgZ             dfdd ZddZZS) PhiConfiga This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi 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 Phi [susnato/phi-1_dev](https://huggingface.co/susnato/phi-1_dev). 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 51200): Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PhiModel`]. hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 8192): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. resid_pdrop (`float`, *optional*, defaults to 0.0): Dropout probability for mlp outputs. embd_pdrop (`int`, *optional*, defaults to 0.0): The dropout ratio for the embeddings. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after computing the attention scores. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048 tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding. qk_layernorm (`bool`, *optional*, defaults to `False`): Whether or not to normalize the Queries and Keys after projecting the hidden states bos_token_id (`int`, *optional*, defaults to 1): Denotes beginning of sequences token id. eos_token_id (`int`, *optional*, defaults to 2): Denotes end of sequences token id. Example: ```python >>> from transformers import PhiModel, PhiConfig >>> # Initializing a Phi-1 style configuration >>> configuration = PhiConfig.from_pretrained("susnato/phi-1_dev") >>> # Initializing a model from the configuration >>> model = PhiModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```phipast_key_values  gelu_new{Gz?h㈵>TF@N?c s||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_||_|tjd|||d|dS)N) bos_token_id eos_token_idtie_word_embeddings) vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads resid_pdrop embd_pdropattention_dropout hidden_actmax_position_embeddingsinitializer_rangelayer_norm_eps use_cache rope_theta rope_scalingpartial_rotary_factor qk_layernorm_rope_scaling_validationsuper__init__)selfrrrrrrrrr r!r"r#r$rr%r&r'r(rrkwargs __class__r_/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/models/phi/configuration_phi.pyr+ns2 zPhiConfig.__init__cCs|jdurdSt|jtrt|jdkrtd|j|jdd}|jdd}|dus2|dvr9td||dusFt|trF|dkrMtd |dS) z< Validate the `rope_scaling` configuration. NrzS`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, got typefactor)lineardynamiczF`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got g?z7`rope_scaling`'s factor field must be a float > 1, got )r& isinstancedictlen ValueErrorgetfloat)r,rope_scaling_typerope_scaling_factorrrr0r)s" z"PhiConfig._rope_scaling_validation)rrr r r r r r r rrrTFrNrFrr) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencer+r) __classcell__rrr.r0rs4K3rN) r@configuration_utilsrutilsr get_loggerr=logger!PHI_PRETRAINED_CONFIG_ARCHIVE_MAPrrrrr0s