o ei'@s2ddlmZddlmZGdddeZdgZdS))PreTrainedConfig)RopeParametersc4sfeZdZdZdZdgZddddZdgdgfd d gd gfd gd gfd ZiZ                       d9de dBde 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 dBd-e dBd.e dBd/e dBd0e dBd1e dBd2e dBd3e ee e fBd4e dBd5e dBd6e dBf2fd7d8 ZZS): 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_tokenslayersnorm @siluNư>TF 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 s||_| |_||_||_||_||_||_||_| |_| |_ | |_ | | |_ | |_ ||_ |dur2|}||_| |_||_||_||_||_||_||_||_||_||_||_tjdi||jdurw|jdkrtdd|jd|_nd|_|durd|j|_dS||_dS)Ng@g@g?g{Gz?)rr+r r!r"r#r%r&r'r(r) qk_head_dimhead_dimr5r$r*r,r.r/r6r7r4r3r0r1r2super__init__r-)selfrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7kwargs __class__r9k/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/youtu/configuration_youtu.pyr=usH    zYoutuConfig.__init__)rrrrrrrrrrrrrNNrTNrrTNTFr)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_plan attribute_mapintstrfloatboolrdictr= __classcell__r9r9r@rBrsH        rN)configuration_utilsrmodeling_rope_utilsrr__all__r9r9r9rBs   %