-iO(<ddlmZmZddlmZGddeZdgZy))PreTrainedConfiglayer_type_validation)RopeParametersc2eZdZdZdZdgZddddddddZdgdgfd 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 ed zd!ed zd"ee d zd#e d zd$e d zf0fd% ZxZS)'VaultGemmaConfiga This is the configuration class to store the configuration of a [`VaultGemmaModel`]. It is used to instantiate an VaultGemma 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 VaultGemma-7B. e.g. [google/vaultgemma-7b](https://huggingface.co/google/vaultgemma-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 VaultGemma model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`VaultGemmaModel`] hidden_size (`int`, *optional*, defaults to 2304): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 9216): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 26): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 4): 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_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. 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. query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores sliding_window (`int`, *optional*, defaults to 4096): in VaultGemma, every other layer uses sliding window attention. This is the size of the sliding window. layer_types (`list`, *optional*): Attention pattern for each layer. final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits. attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores. ```python >>> from transformers import VaultGemmaModel, VaultGemmaConfig >>> # Initializing a VaultGemma vaultgemma-7b style configuration >>> configuration = VaultGemmaConfig() >>> # Initializing a model from the vaultgemma-7b style configuration >>> model = VaultGemmaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` vaultgemmapast_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_dimhidden_activationmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id eos_token_id bos_token_idtie_word_embeddingsrope_parametersattention_biasattention_dropoutquery_pre_attn_scalarsliding_window layer_typesfinal_logit_softcappingattn_logit_softcappingc L| |_||_||_||_||_| |_||_||_||_||_ ||_ ||_ | |_ | |_ | |_||_||_||_||_||_||_||_||_|j,;t/|jDcgc]}t1|dzdzrdndc}|_t3|j,|j||_t7|pdi|ycc}w)Nsliding_attentionfull_attention)rr!r r"rrrrrrrrrrrr$r%rr&r'r)r*r(rangeboolrr#super__init__)selfrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*kwargsi __class__s s/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/vaultgemma/configuration_vaultgemma.pyr4zVaultGemmaConfig.__init__{s58)((#6 $'>$&!2!2#6   #6 !2(",!2!2%:",'>$&<#&    #X]^b^t^tXu STtQUaK'8#>NN D  d..0F0FG. "6" sD!)ii i$gelu_pytorch_tanhi g{Gz?gư>Tr,r-TNFgr=iNg>@gI@)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintstrfloatr2rdictlistr4 __classcell__)r8s@r9rrs$M ^J#4"5%.%.%.%."+ )"+&(9:#%568IJ!"_$56"("&(,(**+*+"(;.2*.#'!%#$#$#$+/MQ&+*-,/%)(,04/33<#$J<#4Z<#: <# : <# !4Z <#!4Z<#*<#:<#"%t<#!4<<#Dj<#$;<#Dj<#Dj<# Dj!<#""D[#<#$($sN/B*CCdJ%<#&t '<#(!4<)<#* #Tz+<#,d -<#.#Y%/<#0"'1<#2!& 3<#<#rN)configuration_utilsrrmodeling_rope_utilsrr__all__r0rNr9rRs(,K1]#']#@  rN