rh".0ddlmZmZGddeZdgZy))PretrainedConfiglayer_type_validationc eZdZdZdZdgZdddddddddZdgd gfd d gd gfd gd gfd Z dfd ZxZ S) MiniMaxConfiga This is the configuration class to store the configuration of a [`MiniMaxModel`]. It is used to instantiate an MiniMax 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 MiniMax. [MiniMaxAI/MiniMax-Text-01-hf](https://huggingface.co/MiniMaxAI/MiniMax-Text-01-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 32000): Vocabulary size of the MiniMax model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MiniMaxModel`] 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. MiniMax'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. layer_types (`list`, *optional*): Attention pattern for each layer. block_size (`int`, *optional*, defaults to 256): The length of each attention block, determining how queries, keys, and values are grouped and processed for intra- and inter-block attention. full_attn_alpha_factor (`float`, *optional*, defaults to 1): Weight for residual value in residual connection after normal attention. full_attn_beta_factor (`float`, *optional*, defaults to 1): Weight for hidden state value in residual connection after normal attention. linear_attn_alpha_factor (`float`, *optional*, defaults to 1): Weight for residual value in residual connection after lightning attention. linear_attn_beta_factor (`float`, *optional*, defaults to 1): Weight for hidden state value in residual connection after lightning attention. mlp_alpha_factor (`float`, *optional*, defaults to 1): Weight for residual value in residual connection after MLP. mlp_beta_factor (`float`, *optional*, defaults to 1): Weight for hidden state value in residual connection after MLP. ```python >>> from transformers import MiniMaxModel, MiniMaxConfig >>> # Initializing a MiniMax style configuration >>> configuration = MiniMaxConfig() >>> # Initializing a model from the MiniMax style configuration >>> model = MiniMaxModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```minimaxpast_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! t#|d| |||d|!||_| |_||_||_||_||_||_||}||_ ||_ | |_ | |_ | |_ ||_||_||_||_||_||_||_||_||_||_||_||_||_||_||_| |_|j,;t=|j D"cgc]}"t?|"dzdzrdndc}"|_tA|j,ycc}"w)N) pad_token_id bos_token_id eos_token_idtie_word_embeddingsfull_attentionlinear_attention)!super__init__ 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_noise layer_types block_sizefull_attn_alpha_factorfull_attn_beta_factorlinear_attn_alpha_factorlinear_attn_beta_factormlp_alpha_factormlp_beta_factorrangeboolr)$selfrr!r"r#r$r&r-r'r r(r)r*rrrrr+r%r,r.r/r0r1r2r3r4r5r6r7r8r9r:kwargsi __class__s$ /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/minimax/configuration_minimax.pyrzMiniMaxConfig.__init__ssH  %%% 3    %'>$&!2!2#6 ,  &"5 #6 $!2("$!2  #6 !2$8!$8!#6 &$&<#%:"(@%'>$ 0.    #W\]a]s]sWt RSD!a%1$5 ;MM D  d../ sD;) i}ii8 rBNsiluig{Gz?gh㈵>TNrrFg.ANrrCFgMbP?rENrrrrrr) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planr __classcell__)r@s@rArrsc JJ#4"5%.%.%.%.*72;2;2; &(9:#%568IJ!"_$56 )!"" !" !CR0R0rN)configuration_utilsrrr__all__rrPrArSs%,KJ0$J0Z  rP