# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ MiniMaxText01 model configuration""" from transformers.configuration_utils import PretrainedConfig class MiniMaxText01Config(PretrainedConfig): model_type = "MiniMaxText01" keys_to_ignore_at_inference = ["past_key_values"] 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, 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=None, eos_token_id=None, 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, **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.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 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, )