a ½ Àhçã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zNemotron model configurationé)ÚPretrainedConfig)Úrope_config_validation)Úloggingcs,eZdZdZdZdgZd‡fdd„ Z‡ZS)ÚNemotronConfigaØ This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate an Nemotron 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 Nemotron-8B. e.g. [nvidia/nemotron-3-8b-base-4k-hf](https://huggingface.co/nvidia/nemotron-3-8b-base-4k-hf). 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 256000): Vocabulary size of the Nemotron model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`NemotronModel`] hidden_size (`int`, *optional*, defaults to 6144): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): 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 48): Number of attention heads for each attention layer in the Transformer decoder. head_dim (`int`, *optional*): Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if None 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, 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 4096): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.0134): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the 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 2): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 3): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding. 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 and down_proj layers in the MLP layers. ```python >>> from transformers import NemotronModel, NemotronConfig >>> # Initializing a Nemotron nemotron-15b style configuration >>> configuration = NemotronConfig() >>> # Initializing a model from the nemotron-15b style configuration >>> model = NemotronModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZnemotronZpast_key_valueséèéé`é é0NÚrelu2éçS–!Žuq‹?çñh㈵øä>TérFçˆÃ@çà?çc sž||_| |_||_||_||_||_|dur0|n|||_||_||_| |_ | |_ | |_ ||_ ||_ t|ƒ||_||_||_tƒjf| |||dœ|¤ŽdS)N)Ú pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddings)Ú vocab_sizeÚmax_position_embeddingsÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚhead_dimÚnum_key_value_headsÚ hidden_actÚinitializer_rangeÚnorm_epsÚ use_cacheÚ rope_thetaÚpartial_rotary_factorrÚattention_biasÚattention_dropoutÚmlp_biasÚsuperÚ__init__)Úselfrrrrrrrrrr r!r"rrrrr#r$r%r&r'Úkwargs©Ú __class__©úo/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/nemotron/configuration_nemotron.pyr)gs4üûzNemotronConfig.__init__)rrrr r NNr r r rTNrrFrrFrF)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZkeys_to_ignore_at_inferencer)Ú __classcell__r.r.r,r/rs2IêrN) r3Zconfiguration_utilsrZmodeling_rope_utilsrÚutilsrZ get_loggerr0ÚloggerrÚ__all__r.r.r.r/Ús