o ¾e¦i4ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zNemotron model configurationé)ÚPreTrainedConfig)ÚRopeParameters)Úloggingc*seZdZdZdZdgZ            d)ded Bded Bded Bded Bded Bded Bded Bded Bded Bded Bded Bde d Bded Bd ed Bd!ed Bd"e d Bd#e e ee fBd Bd$e d Bd%ed Bd&e d Bf(‡fd'd(„ Z ‡Z S)*ÚNemotronConfiga* 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_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`. 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 ```ÚnemotronÚpast_key_valueséèéé`é é0NÚrelu2éçS–!Žuq‹?çñh㈵øä>TérFçÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚhead_dimÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚnorm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚmlp_biasc sª||_| |_||_||_||_||_|dur|n|||_||_||_| |_ | |_ | |_ ||_ ||_ ||_||_| dd¡||_| |_||_||_tƒjdi|¤ŽdS)NÚpartial_rotary_factorgà?©)rrrrrrrrrrrrr$r%r&r#Ú setdefaultr"rr r!ÚsuperÚ__init__)Úselfrrrrrrrrrrrrrr r!r"r#r$r%r&Úkwargs©Ú __class__r(úq/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/nemotron/configuration_nemotron.pyr+gs, zNemotronConfig.__init__)rr r r r NNr rrrTNrrFNFrF)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚintÚstrÚfloatÚboolrÚdictr+Ú __classcell__r(r(r.r0rs‚Jëþýüûúùø ÷ ö õ ô óòñðïîíìërN) r4Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerr1ÚloggerrÚ__all__r(r(r(r0Ús