qildZddlmZddlmZddlmZejeZ GddeZ dgZ y)zPersimmon model configuration)PreTrainedConfig)RopeParameters)loggingc&eZdZdZdZdgZ ddedzdedzdedzdedzd edzd edzd edzd edzd edzde dzde dzde e ee fzdzde dzdedzdedzdedzdedzdedzf$fd Z xZ S)PersimmonConfiga 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_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. 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. Example: ```python >>> from transformers import PersimmonModel, PersimmonConfig >>> # Initializing a Persimmon persimmon-7b style configuration >>> configuration = PersimmonConfig() ``` persimmonpast_key_valuesN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads hidden_actmax_position_embeddingsinitializer_rangelayer_norm_eps use_cachetie_word_embeddingsrope_parameters qk_layernormhidden_dropoutattention_dropout pad_token_id bos_token_id eos_token_idc D||_||_||_||_||_||_||_||_| |_| |_ | |_ ||_ ||_ | |_ |jdd| |_||_||_||_t'|Pdi|y)Npartial_rotary_factorg?)r rr r r rrrrrrrrr setdefaultrrrrsuper__init__)selfr r r r rrrrrrrrrrrrrrkwargs __class__s g/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/persimmon/configuration_persimmon.pyr!zPersimmonConfig.__init__Ss,%'>$&!2!2#6 $!2,"(,!2.137#6 ((( "6")ii@$@relu2r'g{Gz?gh㈵>TFNTr+N)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferenceintstrfloatboolrdictr! __classcell__)r$s@r%rrse5 nJ#4"5"("&(-(**,!(.3*.%)!%+0MQ$('**-#'#$#$'*#$J*#4Z*#: *# : *# !4Z *#$J*#"%t*#!4<*#d *#$;*#"D[*#($sN/B*CCdJ*#Tk*# *# !4r@sA$31   H %e#&e#P  r&