qi"8ddlmZddlmZGddeZdgZy))PreTrainedConfig)RopeParametersc*jeZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Z d"d ed zded zded zded zded zded zded zde d zded zde d zded zde d zded zded zded zde d zde e e e fzd zde d zde d zd e d zf(fd! ZxZS)# GemmaConfiga This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma 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 Gemma-7B. e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b) 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 256000): Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GemmaModel`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The legacy activation function. It is overwritten by the `hidden_activation`. max_position_embeddings (`int`, *optional*, defaults to 8192): 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. rms_norm_eps (`float`, *optional*, defaults to 1e-06): 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`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): 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`. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. use_bidirectional_attention (`bool`, *optional*): If True, the model will attend to all text tokens instead of using a causal mask. ```python >>> from transformers import GemmaModel, GemmaConfig >>> # Initializing a Gemma gemma-7b style configuration >>> configuration = GemmaConfig() >>> # Initializing a model from the gemma-7b style configuration >>> model = GemmaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```gemmapast_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_headshead_dim hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id eos_token_id bos_token_idtie_word_embeddingsrope_parametersattention_biasattention_dropoutuse_bidirectional_attentionc <||_| |_||_||_||_||_||_||_||_| |_ | |_ | |_ ||_ ||_ ||_| |_||_||_||_||_t)|Tdi|y)N)rrrrrrrrrrrrr#r$r%rr rr!r"super__init__)selfrrrrrrrrrrrrrrr r!r"r#r$r%kwargs __class__s _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/gemma/configuration_gemma.pyr)zGemmaConfig.__init__qs0%'>$&!2!2#6   #6 $!2(",!2+F((((#6 . "6")ii i`r0gelu_pytorch_tanhi g{Gz?gư>TTNFgN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintstrfloatboolrdictr) __classcell__)r,s@r-rrsD LJ#4"5%.%.%.%."+ )"+&(9:#%568IJ!"_$56"("&(-(**,*,"!4.2*.#'!%#$#$#$+/MQ&+*-37+-#$J-#4Z-#: -# : -# !4Z -#!4Z-#*-#$J-#"%t-#!4<-#Dj-#$;-#Dj-#Dj-# Dj!-#""D[#-#$($sN/B*CCdJ%-#&t '-#(!4<)-#*&*D[+-#-#r.rN)configuration_utilsrmodeling_rope_utilsrr__all__r'r.r-rGs&*41E#"E#P /r.