o ¾e¦iÕ%ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zMixtral model configurationé)ÚPreTrainedConfig)ÚRopeParameters)Úloggingc4speZdZdZdZdgZdZdddddddd œZd gd gfd d gd gfd gd gfdœZddiZ                       d:d!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 dBd/e dBd0e dBd1e dBd2e dBd3e dBde dBd4e dBd5e dBd6e dBd7eee efBdBf0‡fd8d9„ Z‡ZS);Ú MixtralConfigau This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an Mixtral 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 Mixtral-7B-v0.1 or Mixtral-7B-Instruct-v0.1. [mixtralai/Mixtral-8x7B](https://huggingface.co/mixtralai/Mixtral-8x7B) [mixtralai/Mixtral-7B-Instruct-v0.1](https://huggingface.co/mixtralai/Mixtral-7B-Instruct-v0.1) 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 32000): Vocabulary size of the Mixtral model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MixtralModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 8): 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 `8`. head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`): The attention head dimension. 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 `4096*32`): The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention allows sequence of up to 4096*32 tokens. 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*): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `4096`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_experts_per_tok (`int`, *optional*, defaults to 2): The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter num_local_experts (`int`, *optional*, defaults to 8): Number of experts per Sparse MLP layer. output_router_logits (`bool`, *optional*, defaults to `False`): Whether or not the router logits should be returned by the model. Enabling this will also allow the model to output the auxiliary loss. See [here]() for more details router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. router_jitter_noise (`float`, *optional*, defaults to 0.0): Amount of noise to add to the router. 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 MixtralModel, MixtralConfig >>> # Initializing a Mixtral 7B style configuration >>> configuration = MixtralConfig() >>> # Initializing a model from the Mixtral 7B style configuration >>> model = MixtralModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ÚmixtralÚpast_key_valuesg€„.AÚcolwiseÚrowwiseÚpacked_colwiseÚmoe_tp_experts)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projz!layers.*.mlp.experts.gate_up_projzlayers.*.mlp.experts.down_projzlayers.*.mlp.expertsÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnormÚ num_expertsÚnum_local_expertsé}éé8é éNÚsiluéç{®Gáz”?çñh㈵øä>TééFççü©ñÒMbP?Ú vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚhead_dimÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚ pad_token_idÚ bos_token_idÚ eos_token_idÚtie_word_embeddingsÚsliding_windowÚattention_dropoutÚnum_experts_per_tokÚoutput_router_logitsÚrouter_aux_loss_coefÚrouter_jitter_noiseÚrope_parametersc s²||_| |_||_||_||_||_||_|dur|}||_||_| |_ | |_ | |_ ||_ ||_ ||_||_||_||_||_||_| |_||_||_||_tƒjdi|¤ŽdS)N©)r"r*r#r$r%r&r2r'r)r+r,r-r3r(r4rr5r6r7r1r.r/r0r8ÚsuperÚ__init__)Úselfr"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4rr5r6r7r8Úkwargs©Ú __class__r9úo/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/mixtral/configuration_mixtral.pyr;…s6zMixtralConfig.__init__)rrrrrrNrrrrTNrrFNr rrFr!r N)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚ default_thetaÚbase_model_tp_planÚbase_model_pp_planÚ attribute_mapÚintÚstrÚfloatÚboolrÚdictr;Ú __classcell__r9r9r>r@rs¸Wù  ýÿçþýüûúùø ÷ ö õ ô óòñðïîíìëêéèçrN) rDÚconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerrAÚloggerrÚ__all__r9r9r9r@Ús     ,