U ,-e;@sBdZddlmZddlmZeeZddiZGdddeZ dS) z Persimmon model configuration)PretrainedConfig)loggingzadept/persimmon-8b-basezGhttps://huggingface.co/adept/persimmon-8b-base/resolve/main/config.jsoncs4eZdZdZdZdgZdfdd ZddZZS)PersimmonConfigau This is the configuration class to store the configuration of a [`PersimmonModel`]. It is used to instantiate an Persimmon 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 [adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base). 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 262144): Vocabulary size of the Persimmon model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PersimmonModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 16384): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 36): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 64): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 16384): The maximum sequence length that this model might ever be used with. 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-5): 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`. tie_word_embeddings(`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 25000.0): 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. qk_layernorm (`bool`, *optional*, default to `True`): Whether or not to normalize the Queries and Keys after projecting the hidden states hidden_dropout (`float`, *optional*, default to 0.0): The dropout ratio after applying the MLP to the hidden states. attention_dropout (`float`, *optional*, default to 0.0): The dropout ratio after computing the attention scores. partial_rotary_factor (`float`, *optional*, default to 0.5): Percentage of the query and keys which will have rotary embedding. Example: ```python >>> from transformers import PersimmonModel, PersimmonConfig >>> # Initializing a Persimmon persimmon-7b style configuration >>> configuration = PersimmonConfig() ```Z persimmonZpast_key_values@$@relu2{Gz?h㈵>TFj@N?c s||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ ||_ ||_ ||_||_|tjf|||| d|dS)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads hidden_actinitializer_rangelayer_norm_eps use_cache rope_theta rope_scaling qk_layernormhidden_dropoutattention_dropoutpartial_rotary_factor_rope_scaling_validationsuper__init__)selfrrrrrrrrrrrr r!r"r#r$r%rrrkwargs __class__v/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/persimmon/configuration_persimmon.pyr(^s2zPersimmonConfig.__init__cCs|jdkrdSt|jtr(t|jdkr8td|j|jdd}|jdd}|dksd|dkrrtd||dkst|tr|dkrtd |dS) z< Validate the `rope_scaling` configuration. NrzS`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, got typefactor)ZlinearZdynamiczF`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got g?z8`rope_scaling`'s factor field must be an float > 1, got )r! isinstancedictlen ValueErrorgetfloat)r)Zrope_scaling_typeZrope_scaling_factorr-r-r.r&s  z(PersimmonConfig._rope_scaling_validation)rrrrr r rr r TFr NTrrrNrr) __name__ __module__ __qualname____doc__Z model_typeZkeys_to_ignore_at_inferencer(r& __classcell__r-r-r+r.rs2>2rN) r:Zconfiguration_utilsrutilsrZ get_loggerr7loggerZ'PERSIMMON_PRETRAINED_CONFIG_ARCHIVE_MAPrr-r-r-r.s