o ¾e¦i:ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zBitNet model configurationé)ÚPreTrainedConfig)ÚRopeParameters)Úloggingc(sòeZdZdZdZdgZdZ             d)dedBdedBdedBdedBdedBdedBdedBdedBde dBdedBde dBd edBd!edBd"edBd#e dBd$e dBd%edBd&e e ee fBdBf$‡fd'd(„ Z ‡ZS)*Ú BitNetConfigaï This is the configuration class to store the configuration of a [`BitNetModel`]. It is used to instantiate an BitNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of BitNet b1.58 2B4T [microsoft/bitnet-b1.58-2B-4T](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T). 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 BitNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BitNetModel`] hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 6912): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 20): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 5): 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 `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. 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 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. 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`. ```python >>> from transformers import BitNetModel, BitNetConfig >>> # Initializing a BitNet style configuration >>> configuration = BitNetConfig() >>> # Initializing a model from the BitNet style configuration >>> model = BitNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ÚbitnetÚpast_key_valuesg€„Aéõé ééééÚrelu2éç{®Gáz”?çñh㈵øä>TNéôéôFçÚ 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_embeddingsÚattention_biasÚattention_dropoutÚrope_parametersc sŽ||_||_||_||_||_||_|dur|}||_||_| |_| |_ | |_ ||_ ||_ ||_ ||_| |_| |_||_tƒjdi|¤ŽdS)N©)rrrrrrrrrrrr$r%r&r#r r!r"ÚsuperÚ__init__)Úselfrrrrrrrrrrrr r!r"r#r$r%r&Úkwargs©Ú __class__r'úm/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/bitnet/configuration_bitnet.pyr)cs*zBitNetConfig.__init__)rr r r r r rrrrTNrrFFrN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚ default_thetaÚintÚstrÚfloatÚboolrÚdictr)Ú __classcell__r'r'r,r.rsxGíþýüûúùø ÷ ö õ ô óòñðïîírN) r2Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerr/ÚloggerrÚ__all__r'r'r'r.Ús     }