o ¾e¦i&'ã@s6ddlmZmZddlmZGdd„deƒZdgZdS)é)ÚPreTrainedConfigÚlayer_type_validation)ÚRopeParametersc,sJeZdZdZdZdgZddddddddœZdgdgfd d gd gfd gd gfd œZ                  d4dedBdedBdedBd e dBd!edBd"edBd#edBd$e dBd%edBd&e dBd'edBd(edBd)edBd*edBd+edBd,e dBd-e e e e fBdBd.e dBd/e dBd0edBd1ee dBf*‡fd2d3„ Z‡ZS)5Ú Cohere2Configac This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Instantiating a configuration with the defaults will yield a similar configuration to that of the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) model. Args: vocab_size (`int`, *optional*, defaults to 256000): Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CohereModel`] hidden_size (`int`, *optional*, defaults to 8192): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22528): Dimension of the MLP representations. logit_scale (`float`, *optional*, defaults to 0.0625): The scaling factor for the output logits. num_hidden_layers (`int`, *optional*, defaults to 40): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 64): Number of attention heads for each attention layer in the Transformer decoder. 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 `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 8192): 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. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization. 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*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 5): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 255001): 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`. 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. sliding_window (`int`, *optional*, defaults to 4096): Size of the sliding window attention context. layer_types (`list`, *optional*): Attention pattern for each layer. ```python >>> from transformers import Cohere2Model, Cohere2Config >>> # Initializing a Cohere Nextmodel configuration >>> configuration = Cohere2Config() >>> # Initializing a model from the Cohere2 configuration >>> model = Cohere2Model(configuration) # doctest: +SKIP >>> # Accessing the model configuration >>> configuration = model.config # doctest: +SKIP ``` Úcohere2Úpast_key_valuesÚcolwiseÚrowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_projÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnorméèé éXç°?é(é@NÚsiluç{®Gáz”?çñh㈵øä>TéééäFçéÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚ logit_scaleÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚlayer_norm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚsliding_windowÚ layer_typesc sø|ˆ_| ˆ_|ˆ_|ˆ_|ˆ_|ˆ_|ˆ_|dur|}|ˆ_|ˆ_| ˆ_ | ˆ_ | ˆ_ |ˆ_ |ˆ_ |ˆ_|ˆ_||ˆ_| ˆ_|ˆ_|ˆ_|ˆ_| dd¡ˆ_ˆjdurgtˆddƒˆ_‡fdd„tˆjƒDƒˆ_tˆjˆjƒ|ˆ_tƒjdi|¤ŽdS)NÚsliding_window_patternécs&g|]}t|dˆjƒrdnd‘qS)éÚsliding_attentionÚfull_attention)ÚboolÚ_sliding_window_pattern)Ú.0Úi©Úself©úo/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/cohere2/configuration_cohere2.pyÚ µsÿÿz*Cohere2Config.__init__..r?)rr'r r"r!r#r$r%r&r(r)r*r0r1r2r3Úhead_dimr+r,r-r.Úgetr:ÚgetattrÚrangerr/ÚsuperÚ__init__)r>rr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3Úkwargs©Ú __class__r=r@rGxs@   þzCohere2Config.__init__)rrrrrrNrrrrTrrrTNFrrN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚbase_model_tp_planÚbase_model_pp_planÚintÚfloatÚstrr9rÚdictÚlistrGÚ __classcell__r?r?rIr@rs Mù  ýêþýüûúùø ÷ ö õ ô óòñðïîíìë êrN)Úconfiguration_utilsrrÚmodeling_rope_utilsrrÚ__all__r?r?r?r@Ús   '