# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.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_qwen3_5_moe.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 The Qwen Team and The HuggingFace Inc. team. 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 from ...modeling_rope_utils import RopeParameters class Qwen3_5MoeTextConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen3_5MoeTextModel`]. It is used to instantiate a Qwen3.5-MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen3.5-35B-A3B-Instruct [Qwen/Qwen3.5-35B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Instruct). 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 248320): Vocabulary size of the model. Defines the number of different tokens that can be represented by the `inputs_ids`. hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 40): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 2): 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 `32`. hidden_act (`str`, *optional*, defaults to `"silu"`): The non-linear activation function in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. 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-06): 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. 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`. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. head_dim (`int`, *optional*, defaults to 256): Projection weights dimension in multi-head attention. linear_conv_kernel_dim (`int`, *optional*, defaults to 4): Kernel size of the convolution used in linear attention layers. linear_key_head_dim (`int`, *optional*, defaults to 128): Dimension of each key head in linear attention. linear_value_head_dim (`int`, *optional*, defaults to 128): Dimension of each value head in linear attention. linear_num_key_heads (`int`, *optional*, defaults to 16): Number of key heads used in linear attention layers. linear_num_value_heads (`int`, *optional*, defaults to 32): Number of value heads used in linear attention layers. moe_intermediate_size (`int`, *optional*, defaults to 512): Intermediate size of the routed expert. shared_expert_intermediate_size (`int`, *optional*, defaults to 512): Intermediate size of the shared expert. num_experts_per_tok (`int`, *optional*, defaults to 8): Number of selected experts. num_experts (`int`, *optional*, defaults to 256): Number of routed experts. 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, including load balancing loss and router z-loss. router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. layer_types (`list[str]`, *optional*): Types of each layer (attention or linear). pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*): End of stream token id. ```python >>> from transformers import Qwen3_5MoeTextModel, Qwen3_5MoeTextConfig >>> # Initializing a Qwen3.5-MoE style configuration >>> configuration = Qwen3_5MoeTextConfig() >>> # Initializing a model from the Qwen3.5-35B-A3B style configuration >>> model = Qwen3_5MoeTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "qwen3_5_moe_text" 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.*.mlp.experts.gate_up_proj": "packed_colwise", "layers.*.mlp.experts.down_proj": "rowwise", "layers.*.mlp.shared_expert.gate_proj": "colwise", "layers.*.mlp.shared_expert.up_proj": "colwise", "layers.*.mlp.shared_expert.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } base_config_key = "text_config" def __init__( self, vocab_size=248320, hidden_size=2048, num_hidden_layers=40, num_attention_heads=16, num_key_value_heads=2, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, tie_word_embeddings=False, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, attention_bias=False, attention_dropout=0.0, head_dim=256, linear_conv_kernel_dim=4, linear_key_head_dim=128, linear_value_head_dim=128, linear_num_key_heads=16, linear_num_value_heads=32, moe_intermediate_size=512, shared_expert_intermediate_size=512, num_experts_per_tok=8, num_experts=256, output_router_logits=False, router_aux_loss_coef=0.001, layer_types=None, pad_token_id: int | None = None, bos_token_id: int | None = None, eos_token_id: int | None = None, **kwargs, ): kwargs["ignore_keys_at_rope_validation"] = {"mrope_section", "mrope_interleaved"} self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.tie_word_embeddings = tie_word_embeddings self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_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.attention_bias = attention_bias self.attention_dropout = attention_dropout self.head_dim = head_dim self.rope_parameters = rope_parameters kwargs.setdefault("partial_rotary_factor", 0.25) # assign default for BC self.layer_types = layer_types if self.layer_types is None: interval_pattern = kwargs.get("full_attention_interval", 4) self.layer_types = [ "linear_attention" if bool((i + 1) % interval_pattern) else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types, self.num_hidden_layers) # linear attention part self.linear_conv_kernel_dim = linear_conv_kernel_dim self.linear_key_head_dim = linear_key_head_dim self.linear_value_head_dim = linear_value_head_dim self.linear_num_key_heads = linear_num_key_heads self.linear_num_value_heads = linear_num_value_heads self.moe_intermediate_size = moe_intermediate_size self.shared_expert_intermediate_size = shared_expert_intermediate_size self.num_experts_per_tok = num_experts_per_tok self.num_experts = num_experts self.output_router_logits = output_router_logits self.router_aux_loss_coef = router_aux_loss_coef super().__init__(**kwargs) class Qwen3_5MoeVisionConfig(PreTrainedConfig): model_type = "qwen3_5_moe" base_config_key = "vision_config" def __init__( self, depth=27, hidden_size=1152, hidden_act="gelu_pytorch_tanh", intermediate_size=4304, num_heads=16, in_channels=3, patch_size=16, spatial_merge_size=2, temporal_patch_size=2, out_hidden_size=3584, num_position_embeddings=2304, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.out_hidden_size = out_hidden_size self.num_position_embeddings = num_position_embeddings self.initializer_range = initializer_range class Qwen3_5MoeConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen3_5MoeModel`]. It is used to instantiate a Qwen3.5-MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen3.5-35B-A3B-Instruct [Qwen/Qwen3.5-35B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Instruct). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3_5TextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3_5VisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 248056): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 248057): The video token index to encode the image prompt. vision_start_token_id (`int`, *optional*, defaults to 248053): The start token index to encode the image prompt. vision_end_token_id (`int`, *optional*, defaults to 248054): The end token index to encode the image prompt. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie the word embeddings. ```python >>> from transformers import Qwen3_5MoeForConditionalGeneration, Qwen3_5MoeConfig >>> # Initializing a Qwen3.5-MoE style configuration >>> configuration = Qwen3_5MoeConfig() >>> # Initializing a model from the Qwen3.5-35B-A3B style configuration >>> model = Qwen3_5MoeForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen3_5_moe" sub_configs = {"vision_config": Qwen3_5MoeVisionConfig, "text_config": Qwen3_5MoeTextConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, image_token_id=248056, video_token_id=248057, vision_start_token_id=248053, vision_end_token_id=248054, tie_word_embeddings=False, **kwargs, ): if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() if isinstance(text_config, dict): self.text_config = self.sub_configs["text_config"](**text_config) elif text_config is None: self.text_config = self.sub_configs["text_config"]() self.image_token_id = image_token_id self.video_token_id = video_token_id self.vision_start_token_id = vision_start_token_id self.vision_end_token_id = vision_end_token_id self.tie_word_embeddings = tie_word_embeddings super().__init__(**kwargs) __all__ = ["Qwen3_5MoeConfig", "Qwen3_5MoeTextConfig"]