o ¾e¦i8"ã@s2ddlmZddlmZGdd„deƒZdgZdS)é)ÚPreTrainedConfig)ÚRopeParametersc&s,eZdZdZdZdgZdZdddddddœZdgd gfd d gd gfd gd gfd œZd ddddddddddddddddddddd œdd!fd"e dBd#e dBd$e dBd%e dBd&e dBd'e dBd(e dBd)e dBd*e dBd+e dBd,e dBd-e dBd.e dBd/e dBd0e dBd1e dBd2e dBd3e dBf$‡fd4d5„ Z‡ZS)6Ú 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 ```ÚapertusÚpast_key_valuesg`ãfAÚcolwiseÚrowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.up_projzlayers.*.mlp.down_projÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnormiii8é NÚxieluig{®Gáz”?gñh㈵øä>TrééFÚllama3g @i gð?g@)Ú rope_typeÚ rope_thetaÚfactorÚ original_max_position_embeddingsÚlow_freq_factorÚhigh_freq_factorgÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutc sŽ||_||_||_||_||_||_|dur|}||_||_| |_| |_ | |_ ||_ ||_ ||_ ||_| |_| |_||_tƒjdi|¤ŽdS)N©)rr"rrrrr r!r#r$r%r+r,r*r)r&r'r(ÚsuperÚ__init__)Úselfrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,Úkwargs©Ú __class__r-úo/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/apertus/configuration_apertus.pyr/rs*zApertusConfig.__init__)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚ default_thetaÚbase_model_tp_planÚbase_model_pp_planÚintÚstrÚfloatÚboolrr/Ú __classcell__r-r-r2r4rsšGú  ýúæþýüûúùø ÷ ö õ ô óòñðïçærN)Úconfiguration_utilsrÚmodeling_rope_utilsrrÚ__all__r-r-r-r4Ús