qi'8ddlmZddlmZGddeZdgZy))PreTrainedConfig)RopeParametersc4eZdZdZdZdgZddddZdgdgfd d gd gfd gd gfd ZiZ d'd e d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zde d zd e d zd!e d zd"e ee e fzd#e d zd$e d zd%e d zf2fd& ZxZS)( YoutuConfiga This is the configuration class to store the configuration of a [`YoutuModel`]. It is used to instantiate an Youtu 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 Youtu-LLM-2B. e.g. [tencent/Youtu-LLM-2B](https://huggingface.co/tencent/Youtu-LLM-2B) 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 128256): Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`YoutuModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 6144): 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 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): In MLA, num_key_value_heads=num_attention_heads. kv_lora_rank (`int`, *optional*, defaults to 512): Rank of the LoRA matrices for key and value projections. q_lora_rank (`int`, *optional*, defaults to 1536): Rank of the LoRA matrices for query projections. qk_rope_head_dim (`int`, *optional*, defaults to 64): Dimension of the query/key heads that use rotary position embeddings. v_head_dim (`int`, *optional*, defaults to 128): Dimension of the value heads. qk_nope_head_dim (`int`, *optional*, defaults to 128): Dimension of the query/key heads that don't use rotary position embeddings. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 131072): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*): The standard deviation of the truncated_normal_initializer for initializing all weight matrices, except embedding matrices. embedding_initializer_range (`float`, *optional*): The standard deviation of the truncated_normal_initializer for initializing all embedding 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*): 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 `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`. rope_interleave (`bool`, *optional*, defaults to `True`): Whether to interleave the rotary 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. ```python >>> from transformers import YoutuModel, YoutuConfig >>> # Initializing a Youtu-LLM-2B style configuration >>> configuration = YoutuConfig() >>> # Accessing the model configuration >>> configuration = model.config ```youtupast_key_valuescolwiserowwise)zlayers.*.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_heads kv_lora_rank q_lora_rankqk_rope_head_dim v_head_dimqk_nope_head_dim hidden_actmax_position_embeddingsinitializer_rangeembedding_initializer_range rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parametersrope_interleaveattention_biasattention_dropoutc T||_| |_||_||_||_||_||_||_| |_| |_ | |_ | | z|_ | |_ ||_ ||}||_| |_||_||_||_||_||_||_||_||_||_||_t5|ldi||j 1|jdk7rdd|jzdzz |_nd|_|d|j z|_y||_y)Ng@g@g?g{Gz?)rrrrrrrrrrr qk_head_dimhead_dimr(rrrr!r"r)r*r'r&r#r$r%super__init__r )selfrrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*kwargs __class__s _/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/youtu/configuration_youtu.pyr1zYoutuConfig.__init__usX:%'>$&!2!2#6 (& 0$ 0+.>>( .  &"5 #6 $!2(",!2.#6 ((( "6"  ! ! )1$),d6F6F0F3/N)N&)-& ' ./2T5K5K/KD ,/JD ,)iii r8ii@r:siluiNNgư>TNiiTNTFg)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_plan attribute_mapintstrfloatboolrdictr1 __classcell__)r4s@r5rrs\F PJ#4"5"+ )"+ &(9:#%568IJ!"_$56 M"("&(,(**,*,#&"&')!$'*!'.4*.48#'!%#'#)#)+/FJ'+&+*-5JK$JJK4ZJK: JK : JK !4Z JK!4ZJKDjJK4ZJK*JK$JJK*JK$JJK"%tJK!4<JK &+T\!JK"Dj#JK$$;%JK&Dj'JK(Dj)JK*Dj+JK,"D[-JK.($sN/B*CC/JK01JK2t 3JK4!4<5JKJKr6rN)configuration_utilsrmodeling_rope_utilsrr__all__r-r6r5rNs(441aK"aKH /r6