qi#8ddlmZddlmZGddeZdgZy))PreTrainedConfig)RopeParametersc*heZdZdZdZdgZdddddddZdgdgfd d gd gfd gd gfd Z d"d ed 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 zde d zd ed zf(fd! ZxZS)# ArceeConfiga This is the configuration class to store the configuration of a [`ArceeModel`]. It is used to instantiate an Arcee 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 AFM-4.5B-Base. Pre-trained weights are available at [arcee-ai/AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) and were used to build the examples below. 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 32000): Vocabulary size of the Arcee model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ArceeModel`] hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 18432): 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 checkout [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 `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. AFM-4.5B-Base supports up to 16384 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*): Padding token id. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): 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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads ```python >>> from transformers import ArceeModel, ArceeConfig >>> # Initializing an Arcee AFM-4.5B-Base style configuration >>> configuration = ArceeConfig() >>> # Initializing a model from the AFM-4.5B-Base style configuration >>> model = ArceeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```arceepast_key_valuescolwiserowwise)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_tokenslayersnormN 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_dropoutmlp_biashead_dimc z||_||_||_||_||_||_||}||_||_| |_| |_ | |_ ||_ ||_ ||_ ||n|j|j z|_||_||_| |_| |_||_t)|Tdi|y)N)rrrrrrrrrrrr"r#r$r%r!r rrrsuper__init__)selfrrrrrrrrrrrrrrr r!r"r#r$r%kwargs __class__s _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/arcee/configuration_arcee.pyr)zArceeConfig.__init__ws0%'>$&!2!2#6   &"5 #6 $!2(",!2  $,$8d>N>NRVRjRj>j .#6 ((( "6")i}i iH r/Nrelu2ig{Gz?gh㈵>TNiiFNFgFN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintstrfloatboolrdictr) __classcell__)r,s@r-rrsK ZJ#4"5%.%.%.%. )"+ &(9:#%568IJ!"_$56"'"&(-(**,*.!(.2*.#'!%#'#)#)+0MQ&+*- %#+2#$J2#4Z2#: 2# : 2# !4Z 2#!4Z2#$J2#"%t2#!4<2#Dj2#$;2#Dj2#Dj2#Dj2# "D[!2#"($sN/B*CCdJ#2#$t %2#&!4<'2#(+)2#**+2#2#r.rN)configuration_utilsrmodeling_rope_utilsrr__all__r'r.r-rBs&*41P#"P#f /r.