T^(h#ZdZddlmZddlmZej eZGddeZy)zMixtral model configuration)PretrainedConfig)loggingc eZdZdZdZdgZdddddddddZdgd gfd d gd gfd gd gfd Z dfd ZxZ S) MixtralConfiga 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). 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. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. 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. Enabeling 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. ```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 ```mixtralpast_key_valuescolwiserowwise colwise_rep)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.block_sparse_moe.gatez&layers.*.block_sparse_moe.experts.*.w1z&layers.*.block_sparse_moe.experts.*.w2z&layers.*.block_sparse_moe.experts.*.w3 input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormc ||_| |_||_||_||_||_||_||}||_||_| |_ | |_ | |_ ||_ ||_ ||n|j|j z|_||_||_||_||_||_t)|Td| |||d|y)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headssliding_windownum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cache rope_thetaattention_dropouthead_dimnum_experts_per_toknum_local_expertsoutput_router_logitsrouter_aux_loss_coefrouter_jitter_noisesuper__init__)selfrrrrrr r'r!rr"r#r$rrrrr%rr&r(r)r*r+r,kwargs __class__s o/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/mixtral/configuration_mixtral.pyr.zMixtralConfig.__init__s8%'>$&!2!2#6 ,  &"5 #6 $!2("$!2$,$8d>N>NRVRjRj>j #6 !2$8!$8!#6   %%% 3    )i}ii8 r4Nsiluig{Gz?gh㈵>TNFg.ANr8r5FgMbP?r9) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planr. __classcell__)r1s@r2rrsS jJ#4"5%.%.%.%.*72;2;2; &(9:#%568IJ!"_$56 )!""3< < r3rN) r=configuration_utilsrutilsr get_loggerr:loggerrrr3r2rGs3"3   H %d $d r3