qih"ldZddlmZddlmZddlmZejeZ GddeZ dgZ y)zFalcon configuration)PreTrainedConfig)RopeParameters)loggingc2eZdZdZdZdgZ d dedzdedzdedzdedzd edzd edzd edzd edzd edzdedzdedzdedzdedzdedzdedzdedzdedzde e e e fzdzdedzdedzdedzdedzde dzdedzf0fd Z e dZe dZxZS)! FalconConfiga This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon 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 [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture. 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 65024): Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FalconModel`] hidden_size (`int`, *optional*, defaults to 4544): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 71): Number of attention heads for each attention layer in the Transformer encoder. num_ln_in_parallel_attn (`int`, *optional*): Set to 2 if separate layer norms are to be used for the MLP and the attention output when using parallel attention, otherwise, 1. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for MLP layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for attention layers. num_kv_heads (`int`, *optional*): Number of key-value heads to use per attention layer. If unset, defaults to the same value as `num_attention_heads`. alibi (`bool`, *optional*, defaults to `False`): Whether to use ALiBi positional biases during self-attention. new_decoder_architecture (`bool`, *optional*, defaults to `False`): Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn` arguments are ignored, as the new decoder always uses parallel attention. multi_query (`bool`, *optional*, defaults to `True`): Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`. parallel_attn (`bool`, *optional*, defaults to `True`): Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`. bias (`bool`, *optional*, defaults to `False`): Whether to use bias on Linear layers. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained Falcon models with RoPE support up to 2048 tokens. 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`. bos_token_id (`int`, *optional*, defaults to 11): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 11): The id of the "end-of-sequence" token. pad_token_id (`int`, *optional*): Padding token id. ffn_hidden_size (`int`, *optional*): The hidden size of the feedforward layer in the Transformer decoder. defaults to 4x hidden dim activation (`str`, *optional*, defaults to `"gelu"`): The activation function used in the feedforward layer. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings Example: ```python >>> from transformers import FalconModel, FalconConfig >>> # Initializing a small (2-layer) Falcon configuration >>> configuration = FalconConfig(num_hidden_layers=2) >>> # Initializing a model from the small configuration >>> model = FalconModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```falconpast_key_valuesN vocab_size hidden_sizenum_hidden_layersnum_attention_headsnum_ln_in_parallel_attnlayer_norm_epsiloninitializer_range use_cachehidden_dropoutattention_dropout num_kv_headsalibinew_decoder_architecture multi_query parallel_attnbiasmax_position_embeddingsrope_parameters bos_token_id eos_token_id pad_token_idffn_hidden_size activationtie_word_embeddingsc ||_|jdd}||n||_||_||_||_||_||_| |_| |_ ||_ ||_ ||_ | |n| |_ | |_| |_||_||_||_||_||_||_||_| |dz|_n||_||_t3|hdi|y)Nn_embed)r popr r r rrrrrrrrrrrrrrrrr r!rrsuper__init__)selfr r r r rrrrrrrrrrrrrrrrrrr r!kwargsr# __class__s a/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/falcon/configuration_falcon.pyr(zFalconConfig.__init__qs8%**Y-*1/;w!2#6 "4!2",!2(((3?3G/\ (@%&* '>$'>$$#6  "#.?D #2D . "6"c4|j|jzSN)r r r)s r,head_dimzFalconConfig.head_dims4#;#;;;r-c|j Sr/)rr0s r,rotaryzFalconConfig.rotarys::~r-)ii GNgh㈵>g{Gz?Tr6NFFTTFiN r7NNgeluT)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferenceintfloatboolrdictstrr(propertyr1r3 __classcell__)r+s@r,rrsS jJ#4"5"'"&(**,.2)-*.!%'**-#'"05#'%)!.2MQ#%#%#'&*!'+/3;#$J;#4Z;#: ;# !4Z ;# "%t ;# $J;#!4<;#$;;# ;#!4<;#Dj;#d{;##'+;#D[;# d{!;#"Tk#;#$"%t%;#&($sN/B*CCdJ';#(Dj);#*Dj+;#,Dj-;#.t/;#0$J1;#2"D[3;#z<<r-rN) r<configuration_utilsrmodeling_rope_utilsrutilsr get_loggerr9loggerr__all__r%r-r,rLsA31   H %\#\~  r-