꬜i8"8ddlmZddlmZGddeZdgZy))PreTrainedConfig)RopeParametersc&ReZdZdZdZdgZdZdddddddZdgd gfd d gd gfd gd gfd Zd ddddddddddddddddddddd dd!fd"e dzd#e dzd$e dzd%e dzd&e dzd'e dzd(e dzd)e dzd*e dzd+e dzd,e dzd-e dzd.e dzd/e dzd0e dzd1e dzd2e dzd3e dzf$fd4 ZxZS)5 ApertusConfiga  This is the configuration class to store the configuration of a [`ApertusModel`]. It is used to instantiate a Apertus 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 Apertus-8B. e.g. [swiss-ai/Apertus-8B](https://huggingface.co/swiss-ai/Apertus-8B) 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 131072): Vocabulary size of the Apertus model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ApertusModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): 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. num_key_value_heads (`int`, *optional*): 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`. hidden_act (`str` or `function`, *optional*, defaults to `"xielu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 65536): The maximum sequence length that this model might ever be used with. Apertus supports up to 65536 tokens. 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-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`. pad_token_id (`int`, *optional*, defaults to 3): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. 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`. attention_bias (`bool`, *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. ```python >>> from transformers import ApertusModel, ApertusConfig >>> # Initializing a Apertus-8B style configuration >>> configuration = ApertusConfig() >>> # Initializing a model from the Apertus-8B style configuration >>> model = ApertusModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```apertuspast_key_valuesg`fAcolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormiii8 Nxieluig{Gz?gh㈵>TrFllama3g @i g?g@) rope_type rope_thetafactor original_max_position_embeddingslow_freq_factorhigh_freq_factorg vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parametersattention_biasattention_dropoutc (||_||_||_||_||_||_||}||_||_| |_| |_ | |_ ||_ ||_ ||_ ||_| |_| |_||_t%|Ldi|y)N)rr$rrr r!r"r#r%r&r'r-r.r,r+r(r)r*super__init__)selfrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.kwargs __class__s m/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/apertus/configuration_apertus.pyr2zApertusConfig.__init__rs:%'>$&!2!2#6   &"5 #6 $!2(",!2.#6 ((( "6")__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference default_thetabase_model_tp_planbase_model_pp_planintstrfloatboolrr2 __classcell__)r5s@r6rrsE NJ#4"5M%.%.%.%. )"+ &(9:#%568IJ!"_$56"("&(-(**,*.!(.3*.%)!%#$#$#$+0!$04" # 2 ',*-55#$J5#4Z5#: 5# : 5# !4Z 5#!4Z5#$J5#"%t5#!4<5#dl5#$;5#Dj5#Dj5#Dj5# "D[!5#"($.#5#2t 35#4!4<55#5#r7rN)configuration_utilsrmodeling_rope_utilsrr__all__r0r7r6rIs'*41N#$N#b  r7