# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/minimax/modular_minimax.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_minimax.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved. # # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PretrainedConfig, layer_type_validation class MiniMaxConfig(PretrainedConfig): r""" 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 ```""" model_type = "minimax" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.block_sparse_moe.gate": "colwise_rep", # we need to replicate here to correctly route experts "layers.*.block_sparse_moe.experts.*.w1": "colwise", "layers.*.block_sparse_moe.experts.*.w2": "rowwise", "layers.*.block_sparse_moe.experts.*.w3": "colwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=14336, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=8, head_dim=None, hidden_act="silu", max_position_embeddings=4096 * 32, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=1e6, sliding_window=None, attention_dropout=0.0, num_experts_per_tok=2, num_local_experts=8, output_router_logits=False, router_aux_loss_coef=0.001, router_jitter_noise=0.0, layer_types=None, block_size=256, full_attn_alpha_factor=1, full_attn_beta_factor=1, linear_attn_alpha_factor=1, linear_attn_beta_factor=1, mlp_alpha_factor=1, mlp_beta_factor=1, **kwargs, ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.sliding_window = sliding_window # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.head_dim = head_dim self.num_experts_per_tok = num_experts_per_tok self.num_local_experts = num_local_experts self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef self.router_jitter_noise = router_jitter_noise self.layer_types = layer_types self.block_size = block_size self.full_attn_alpha_factor = full_attn_alpha_factor self.full_attn_beta_factor = full_attn_beta_factor self.linear_attn_alpha_factor = linear_attn_alpha_factor self.linear_attn_beta_factor = linear_attn_beta_factor self.mlp_alpha_factor = mlp_alpha_factor self.mlp_beta_factor = mlp_beta_factor if self.layer_types is None: self.layer_types = [ "full_attention" if bool((i + 1) % 2) else "linear_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types) __all__ = ["MiniMaxConfig"]