o ¾e¦ia!ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zCohere model configurationé)ÚPreTrainedConfig)ÚRopeParameters)Úloggingc*s@eZdZdZdZdgZdZddddddddœZdgd gfd d gd gfd gd gfd œZ                   d3de dBde 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 ee e fBdBd.e dBd/e dBd0e dBf(‡fd1d2„ Z‡ZS)4Ú CohereConfiga 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. use_qk_norm (`bool`, *optional*, defaults to `False`): Whether to use query-key normalization in the attention ```python >>> from transformers import CohereModel, CohereConfig >>> # Initializing a Cohere model configuration >>> configuration = CohereConfig() >>> # Initializing a model from the Cohere configuration >>> model = CohereModel(configuration) # doctest: +SKIP >>> # Accessing the model configuration >>> configuration = model.config # doctest: +SKIP ```ÚcohereÚpast_key_valuesg€„AÚcolwiseÚrowwise)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>TéééäFçÚ 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_dropoutÚ use_qk_normc sš||_| |_||_||_||_||_||_|dur|}||_||_| |_ | |_ | |_ ||_ ||_ ||_||_||_| |_||_||_tƒjdi|¤ŽdS)N©)rr&rr!r r"r#r$r%r'r(r)r/r0r1r.r-r*r+r,ÚsuperÚ__init__)Úselfrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1Úkwargs©Ú __class__r2úm/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/cohere/configuration_cohere.pyr4zs.zCohereConfig.__init__)rrrrrrNrrrrTrrrTNFrF)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚ default_thetaÚbase_model_tp_planÚbase_model_pp_planÚintÚfloatÚstrÚboolrÚdictr4Ú __classcell__r2r2r7r9rsœJù  ýëþýüûúùø ÷ ö õ ô óòñðïîíìërN) r=Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerr:ÚloggerrÚ__all__r2r2r2r9Ús