# coding=utf-8 # Copyright 2024 The Qwen team, Alibaba Group 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. """RWKV079Qwen3 model configuration""" #Never gonna give you up from typing import Optional from transformers.configuration_utils import PretrainedConfig#, layer_type_validation from transformers.modeling_rope_utils import rope_config_validation from transformers.utils import logging logger = logging.get_logger(__name__) # class RWKV07BMoEConfig(PretrainedConfig): # r""" # This is the configuration class to store the configuration of a [`RWKV07BMoEModel`]. It is used to instantiate a # RWKV079Qwen3 model according to the specified arguments, defining the model architecture. Instantiating a configuration # with the defaults will yield a similar configuration to that of # Qwen3-7B-beta [Qwen/Qwen3-7B-beta](https://huggingface.co/Qwen/Qwen3-7B-beta). # 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 151936): # Vocabulary size of the RWKV079Qwen3 model. Defines the number of different tokens that can be represented by the # `inputs_ids` passed when calling [`RWKV07BMoEModel`] # hidden_size (`int`, *optional*, defaults to 4096): # Dimension of the hidden representations. # intermediate_size (`int`, *optional*, defaults to 22016): # 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 32): # 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`. # lora_rank_decay (`int`, *optional*): # The rank of the lora used to generate decay. # lora_rank_iclr (`int`, *optional*): # The rank of the lora used to generate the in-context learning rate. # lora_rank_value_residual_mix (`int`, *optional*): # The rank of the lora used to generate the value residual mix amount. # lora_rank_value_gate (`int`, *optional*): # The rank of the lora used to generate the gate. # 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 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_theta (`float`, *optional*, defaults to 10000.0): # The base period of the RoPE embeddings. # rope_scaling (`Dict`, *optional*): # Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type # and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value # accordingly. # Expected contents: # `rope_type` (`str`): # The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', # 'llama3'], with 'default' being the original RoPE implementation. # `factor` (`float`, *optional*): # Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In # most scaling types, a `factor` of x will enable the model to handle sequences of length x * # original maximum pre-trained length. # `original_max_position_embeddings` (`int`, *optional*): # Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during # pretraining. # `attention_factor` (`float`, *optional*): # Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention # computation. If unspecified, it defaults to value recommended by the implementation, using the # `factor` field to infer the suggested value. # `beta_fast` (`float`, *optional*): # Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear # ramp function. If unspecified, it defaults to 32. # `beta_slow` (`float`, *optional*): # Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear # ramp function. If unspecified, it defaults to 1. # `short_factor` (`List[float]`, *optional*): # Only used with 'longrope'. The scaling factor to be applied to short contexts (< # `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden # size divided by the number of attention heads divided by 2 # `long_factor` (`List[float]`, *optional*): # Only used with 'longrope'. The scaling factor to be applied to long contexts (< # `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden # size divided by the number of attention heads divided by 2 # `low_freq_factor` (`float`, *optional*): # Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE # `high_freq_factor` (`float`, *optional*): # Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE # use_sliding_window (`bool`, *optional*, defaults to `False`): # Whether to use sliding window attention. # sliding_window (`int`, *optional*, defaults to 4096): # Sliding window attention (SWA) window size. If not specified, will default to `4096`. # max_window_layers (`int`, *optional*, defaults to 28): # The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. # attention_dropout (`float`, *optional*, defaults to 0.0): # The dropout ratio for the attention probabilities. # ```python # >>> from transformers import RWKV07BMoEModel, RWKV079Qwen3Config # >>> # Initializing a RWKV079Qwen3 style configuration # >>> configuration = RWKV079Qwen3Config() # >>> # Initializing a model from the RWKV079Qwen3-7B style configuration # >>> model = RWKV07BMoEModel(configuration) # >>> # Accessing the model configuration # >>> configuration = model.config # ```""" # model_type = "rwkv07b_moe" # keys_to_ignore_at_inference = ["past_key_values"] # # Default tensor parallel plan for base model `Qwen3Moe` # base_model_tp_plan = { # #NoPE-GQA # "layers.*.self_attn.q_proj": "colwise", # "layers.*.self_attn.k_proj": "colwise", # "layers.*.self_attn.v_proj": "colwise", # "layers.*.self_attn.o_proj": "rowwise", # #RoPE-RWKV # "layers.*.self_attn.receptance": "colwise", # "layers.*.self_attn.key": "colwise", # "layers.*.self_attn.value": "colwise", # "layers.*.self_attn.output": "rowwise", # "layers.*.mlp.experts.*.gate_proj": "colwise", # "layers.*.mlp.experts.*.up_proj": "colwise", # "layers.*.mlp.experts.*.down_proj": "rowwise", # "layers.*.mlp.gate_proj": "colwise", # "layers.*.mlp.up_proj": "colwise", # "layers.*.mlp.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"]), # } # def __init__( # self, # vocab_size=151936, # hidden_size=4096, # intermediate_size=22016, # num_hidden_layers=32, # num_attention_heads=32, # num_key_value_heads=32, # lora_rank_tokenshift=None, # lora_rank_decay=None, # lora_rank_iclr=None, # lora_rank_value_residual_mix=None, # lora_rank_value_key_mix=None, # lora_rank_gate=None, # hidden_act="silu", # max_position_embeddings=32768, # initializer_range=0.02, # rms_norm_eps=1e-6, # use_cache=True, # tie_word_embeddings=False, # use_rope=True, # rope_theta=10000.0, # rope_scaling=None, # use_sliding_window=False, # sliding_window=4096, # max_window_layers=28, # first_attention_layer=9999, # first_post_attention_layer=9999, # attention_striping=1, # last_striping_layer=99999, # layer_types=None, # attention_dropout=0.0, # attention_bias=True, # attention_output_bias=False, # gate_rank_type=2, # balance_state=True, # groupnorm_att=False, # use_tokenshift=False, # decoder_sparse_step=1, # moe_intermediate_size=768, # num_experts_per_tok=8, # num_experts=128, # norm_topk_prob=False, # output_router_logits=False, # router_aux_loss_coef=0.001, # mlp_only_layers=None, # **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.use_sliding_window = use_sliding_window # self.sliding_window = sliding_window if use_sliding_window else None # self.max_window_layers = max_window_layers # self.first_attention_layer = first_attention_layer # self.first_post_attention_layer = first_post_attention_layer # self.attention_striping = attention_striping # self.last_striping_layer = last_striping_layer # # 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.lora_rank_tokenshift = lora_rank_tokenshift # self.lora_rank_decay = lora_rank_decay # self.lora_rank_iclr = lora_rank_iclr # self.lora_rank_value_residual_mix = lora_rank_value_residual_mix # self.lora_rank_gate = lora_rank_gate # self.hidden_act = hidden_act # self.initializer_range = initializer_range # self.rms_norm_eps = rms_norm_eps # self.use_cache = use_cache # self.use_rope = use_rope # self.rope_theta = rope_theta # self.rope_scaling = rope_scaling # self.attention_dropout = attention_dropout # # Validate the correctness of rotary position embeddings parameters # # BC: if there is a 'type' field, move it to 'rope_type'. # if self.rope_scaling is not None and "type" in self.rope_scaling: # self.rope_scaling["rope_type"] = self.rope_scaling["type"] # self.rope_parameters = rope_scaling # #rope_config_validation(self) # self.layer_types = layer_types # if self.layer_types is None: # self.layer_types = [ # "sliding_attention" # if self.sliding_window is not None and i >= self.max_window_layers # else "full_attention" # for i in range(self.num_hidden_layers) # ] # #layer_type_validation(self.layer_types) # self.attention_bias = attention_bias # self.attention_output_bias = attention_output_bias # self.gate_rank_type = gate_rank_type # self.balance_state = balance_state # self.groupnorm_att = groupnorm_att # self.use_tokenshift = use_tokenshift # # MoE arguments # self.decoder_sparse_step = decoder_sparse_step # self.moe_intermediate_size = moe_intermediate_size # self.num_experts_per_tok = num_experts_per_tok # self.num_experts = num_experts # self.norm_topk_prob = norm_topk_prob # self.output_router_logits = output_router_logits # self.router_aux_loss_coef = router_aux_loss_coef # self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers # super().__init__( # tie_word_embeddings=tie_word_embeddings, # **kwargs, # ) from typing import Optional, TypedDict class RopeParameters(TypedDict): """ Args: rope_theta (`float`): The base period of the RoPE embeddings. rope_type (`str`, *optional*, defaults to "default"): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. factor (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. original_max_position_embeddings (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. attention_factor (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. beta_fast (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. beta_slow (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. short_factor (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 long_factor (`list[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 low_freq_factor (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE high_freq_factor (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE """ rope_theta: float rope_type: Optional[str] factor: Optional[float] original_max_position_embeddings: Optional[int] attention_factor: Optional[float] beta_fast: Optional[float] beta_slow: Optional[float] short_factor: Optional[list[float]] long_factor: Optional[list[float]] low_freq_factor: Optional[float] high_freq_factor: Optional[float] class Qwen3VLMoeTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen3VLMoeTextModel`]. It is used to instantiate a Qwen3-VL-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-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-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 151936): Vocabulary size of the Qwen2MoE model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Qwen2MoeModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 5632): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 24): 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 16): 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` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 128000): 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. attention_bias (`bool`, defaults to `False`, *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. decoder_sparse_step (`int`, *optional*, defaults to 1): The frequency of the MoE layer. moe_intermediate_size (`int`, *optional*, defaults to 1408): Intermediate size of the routed expert. num_experts_per_tok (`int`, *optional*, defaults to 4): Number of selected experts. num_experts (`int`, *optional*, defaults to 60): Number of routed experts. mlp_only_layers (`List[int]`, *optional*, defaults to `[]`): Indicate which layers use Qwen3VLMoeMLP rather than Qwen3VLMoeSparseMoeBlock The list contains layer index, from 0 to num_layers-1 if we have num_layers layers If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty 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`. head_dim (`int`, *optional*): The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`. ```python >>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig >>> # Initializing a Qwen3VLMoe style configuration >>> configuration = Qwen3VLMoeConfig() >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration >>> model = Qwen3VLMoeForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen3_vl_moe_text" base_config_key = "text_config" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `Qwen3VLMoe` 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.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.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"]), } def __init__( self, vocab_size: Optional[int] = 151936, hidden_size: Optional[int] = 2048, intermediate_size: Optional[int] = 5632, num_hidden_layers: Optional[int] = 24, num_attention_heads: Optional[int] = 16, num_key_value_heads: Optional[int] = 16, hidden_act: Optional[str] = "silu", max_position_embeddings: Optional[int] = 128000, initializer_range: Optional[float] = 0.02, rms_norm_eps: Optional[float] = 1e-6, use_cache: Optional[bool] = True, tie_word_embeddings: Optional[bool] = False, attention_bias: Optional[bool] = False, attention_dropout: Optional[float] = 0.0, decoder_sparse_step: Optional[int] = 1, moe_intermediate_size: Optional[int] = 1408, num_experts_per_tok: Optional[int] = 4, num_experts: Optional[int] = 60, mlp_only_layers: Optional[list[int]] = None, rope_parameters: Optional[RopeParameters] = None, head_dim: Optional[int] = None, **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.layer_types = None # 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.attention_bias = attention_bias self.attention_dropout = attention_dropout self.head_dim = head_dim or hidden_size // num_attention_heads # Try to set `rope_scaling` if available, otherwise use `rope_parameters` rope_scaling = kwargs.pop("rope_scaling", None) self.rope_parameters = rope_scaling or rope_parameters self.sliding_window = None self.max_window_layers = 0 if self.layer_types is None: self.layer_types = [ "sliding_attention" if self.sliding_window is not None and i >= self.max_window_layers else "full_attention" for i in range(self.num_hidden_layers) ] # Validate the correctness of rotary position embeddings parameters rope_theta = kwargs.get("rope_theta", 5000000.0) # standardize_rope_params(self, rope_theta=rope_theta) rope_config_validation(self, ignore_keys={"mrope_section", "mrope_interleaved"}) # MoE arguments self.decoder_sparse_step = decoder_sparse_step self.moe_intermediate_size = moe_intermediate_size self.num_experts_per_tok = num_experts_per_tok self.num_experts = num_experts self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class Qwen3VLMoeVisionConfig(PretrainedConfig): model_type = "qwen3_vl_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, deepstack_visual_indexes=[8, 16, 24], 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 self.deepstack_visual_indexes = deepstack_visual_indexes class RWKV07BMoEConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen3VLMoeModel`]. It is used to instantiate a Qwen3-VL-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-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-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 `Qwen3VLMoeTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLMoeVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151655): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151656): The video token index to encode the image prompt. vision_start_token_id (`int`, *optional*, defaults to 151652): The start token index to encode the image prompt. vision_end_token_id (`int`, *optional*, defaults to 151653): 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 Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig >>> # Initializing a Qwen3-VL-MOE style configuration >>> configuration = Qwen3VLMoeConfig() >>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration >>> model = Qwen3VLMoeForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "rwkv07b_moe" sub_configs = {"vision_config": Qwen3VLMoeVisionConfig, "text_config": Qwen3VLMoeTextConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, image_token_id=151655, video_token_id=151656, vision_start_token_id=151652, vision_end_token_id=151653, 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 super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings) __all__ = ["Qwen3VLMoeConfig", "Qwen3VLMoeTextConfig"]