# Qwen3-VL-Moe [Qwen3-VL](https://huggingface.co/papers/2502.13923) is a multimodal vision-language model series, encompassing both dense and MoE variants, as well as Instruct and Thinking versions. Building upon its predecessors, Qwen3-VL delivers significant improvements in visual understanding while maintaining strong pure text capabilities. Key architectural advancements include: enhanced MRope with interleaved layout for better spatial-temporal modeling, DeepStack integration to effectively leverage multi-level features from the Vision Transformer (ViT), and improved video understanding through text-based time alignment—evolving from T-RoPE to text timestamp alignment for more precise temporal grounding. These innovations collectively enable Qwen3-VL to achieve superior performance in complex multimodal tasks. Model usage ```py import torch from transformers import Qwen3VLMoeForConditionalGeneration, AutoProcessor model = Qwen3VLMoeForConditionalGeneration.from_pretrained( "Qwen/Qwen3-VL-Moe", dtype=torch.float16, device_map="auto", attn_implementation="sdpa" ) processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-Moe") messages = [ { "role":"user", "content":[ { "type":"image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" }, { "type":"text", "text":"Describe this image." } ] } ] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", ) inputs.pop("token_type_ids", None) generated_ids = model.generate(**inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` ## Qwen3VLMoeConfig[[transformers.Qwen3VLMoeConfig]] #### transformers.Qwen3VLMoeConfig[[transformers.Qwen3VLMoeConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/configuration_qwen3_vl_moe.py#L220) This is the configuration class to store the configuration of a [Qwen3VLMoeModel](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.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](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. ```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 ``` **Parameters:** 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 the model's input and output word embeddings should be tied. ## Qwen3VLMoeTextConfig[[transformers.Qwen3VLMoeTextConfig]] #### transformers.Qwen3VLMoeTextConfig[[transformers.Qwen3VLMoeTextConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/configuration_qwen3_vl_moe.py#L24) This is the configuration class to store the configuration of a [Qwen3VLMoeTextModel](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.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](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. ```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 ``` **Parameters:** 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](/docs/transformers/v5.3.0/en/model_doc/qwen2_moe#transformers.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`. 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 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`. head_dim (`int`, *optional*) : The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`. pad_token_id (`int`, *optional*) : The id of the padding token. ## Qwen3VLMoeVisionModel[[transformers.Qwen3VLMoeVisionModel]] #### transformers.Qwen3VLMoeVisionModel[[transformers.Qwen3VLMoeVisionModel]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L597) forwardtransformers.Qwen3VLMoeVisionModel.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L747[{"name": "hidden_states", "val": ": Tensor"}, {"name": "grid_thw", "val": ": Tensor"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **hidden_states** (`torch.Tensor` of shape `(seq_len, hidden_size)`) -- The final hidden states of the model. - **grid_thw** (`torch.Tensor` of shape `(num_images_or_videos, 3)`) -- The temporal, height and width of feature shape of each image in LLM.0`torch.Tensor`hidden_states. **Parameters:** hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`) : The final hidden states of the model. grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`) : The temporal, height and width of feature shape of each image in LLM. **Returns:** ``torch.Tensor`` hidden_states. ## Qwen3VLMoeTextModel[[transformers.Qwen3VLMoeTextModel]] #### transformers.Qwen3VLMoeTextModel[[transformers.Qwen3VLMoeTextModel]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L903) Text part of Qwen3VLMoe, not a pure text-only model, as DeepStack integrates visual features into the early hidden states. This model inherits from [PreTrainedModel](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. forwardtransformers.Qwen3VLMoeTextModel.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L924[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | None = None"}, {"name": "visual_pos_masks", "val": ": torch.Tensor | None = None"}, {"name": "deepstack_visual_embeds", "val": ": list[torch.Tensor] | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.modeling_flash_attention_utils.FlashAttentionKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.3.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - **use_cache** (`bool`, *optional*) -- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) -- Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. - **visual_pos_masks** (`torch.Tensor` of shape `(batch_size, seqlen)`, *optional*) -- The mask of the visual positions. - **deepstack_visual_embeds** (`list[torch.Tensor]`, *optional*) -- The deepstack visual embeddings. The shape is (num_layers, visual_seqlen, embed_dim). The feature is extracted from the different visual encoder layers, and fed to the decoder hidden states. It's from the paper DeepStack(https://arxiv.org/abs/2406.04334).0`MoeModelOutputWithPast` or `tuple(torch.FloatTensor)`A `MoeModelOutputWithPast` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Qwen3VLMoeConfig](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeConfig)) and inputs. The [Qwen3VLMoeTextModel](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeTextModel) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. - **router_logits** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary loss for Mixture of Experts models. **Parameters:** config ([Qwen3VLMoeTextConfig](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeTextConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** ``MoeModelOutputWithPast` or `tuple(torch.FloatTensor)`` A `MoeModelOutputWithPast` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Qwen3VLMoeConfig](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeConfig)) and inputs. ## Qwen3VLMoeModel[[transformers.Qwen3VLMoeModel]] #### transformers.Qwen3VLMoeModel[[transformers.Qwen3VLMoeModel]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1087) The bare Qwen3 Vl Moe Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. forwardtransformers.Qwen3VLMoeModel.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1396[{"name": "input_ids", "val": ": LongTensor = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "pixel_values_videos", "val": ": torch.FloatTensor | None = None"}, {"name": "image_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "video_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "mm_token_type_ids", "val": ": torch.IntTensor | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.3.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using `image_processor_class`. See `image_processor_class.__call__` for details (`processor_class` uses `image_processor_class` for processing images). - **pixel_values_videos** (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, frame_size, frame_size)`, *optional*) -- The tensors corresponding to the input video. Pixel values for videos can be obtained using `video_processor_class`. See `video_processor_class.__call__` for details (`processor_class` uses `video_processor_class` for processing videos). - **image_grid_thw** (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) -- The temporal, height and width of feature shape of each image in LLM. - **video_grid_thw** (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*) -- The temporal, height and width of feature shape of each video in LLM. - **mm_token_type_ids** (`torch.IntTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens matching each modality. For example text (0), image (1), video (2). Multimodal token type ids can be obtained using [AutoProcessor](/docs/transformers/v5.3.0/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/v5.3.0/en/model_doc/vilt#transformers.ViltProcessor.__call__) for details. - **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) -- Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.0`Qwen3VLMoeModelOutputWithPast` or `tuple(torch.FloatTensor)`A `Qwen3VLMoeModelOutputWithPast` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`None`) and inputs. The [Qwen3VLMoeModel](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeModel) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **hidden_states** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. - **rope_deltas** (`torch.LongTensor` of shape `(batch_size, )`, *optional*) -- The rope index difference between sequence length and multimodal rope. - **router_logits** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the model, useful to compute the auxiliary loss for Mixture of Experts models. **Parameters:** config ([Qwen3VLMoeModel](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeModel)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.3.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** ``Qwen3VLMoeModelOutputWithPast` or `tuple(torch.FloatTensor)`` A `Qwen3VLMoeModelOutputWithPast` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`None`) and inputs. #### get_video_features[[transformers.Qwen3VLMoeModel.get_video_features]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1270) - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*) -- Last layer hidden-state after a pooling operation on the spatial dimensions. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the model at the output of each stage. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. - **deepstack_features** (`List[torch.FloatTensor]`, *optional*) -- List of hidden-states (feature maps) from deepstack layers. **Parameters:** pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input videos. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*) : The temporal, height and width of feature shape of each video in LLM. **Returns:** ``BaseModelOutputWithDeepstackFeatures` or `tuple(torch.FloatTensor)`` A `BaseModelOutputWithDeepstackFeatures` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Qwen3VLMoeConfig](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeConfig)) and inputs. #### get_image_features[[transformers.Qwen3VLMoeModel.get_image_features]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1287) - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*) -- Last layer hidden-state after a pooling operation on the spatial dimensions. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the model at the output of each stage. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. - **deepstack_features** (`List[torch.FloatTensor]`, *optional*) -- List of hidden-states (feature maps) from deepstack layers. **Parameters:** pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) : The temporal, height and width of feature shape of each image in LLM. **Returns:** ``BaseModelOutputWithDeepstackFeatures` or `tuple(torch.FloatTensor)`` A `BaseModelOutputWithDeepstackFeatures` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Qwen3VLMoeConfig](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeConfig)) and inputs. ## Qwen3VLMoeForConditionalGeneration[[transformers.Qwen3VLMoeForConditionalGeneration]] #### transformers.Qwen3VLMoeForConditionalGeneration[[transformers.Qwen3VLMoeForConditionalGeneration]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1587) forwardtransformers.Qwen3VLMoeForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1639[{"name": "input_ids", "val": ": LongTensor = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "pixel_values_videos", "val": ": torch.FloatTensor | None = None"}, {"name": "image_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "video_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "mm_token_type_ids", "val": ": torch.IntTensor | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | None = None"}, {"name": "logits_to_keep", "val": ": int | torch.Tensor = 0"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}] labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. Example: ```python >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration >>> model = Qwen3VLMoeForConditionalGeneration.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct", dtype="auto", device_map="auto") >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct") >>> messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image in short."}, ], } ] >>> # Preparation for inference >>> inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt" ) >>> inputs = inputs.to(model.device) >>> # Generate >>> generated_ids = model.generate(**inputs, max_new_tokens=128) >>> generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] >>> processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "A woman in a plaid shirt sits on a sandy beach at sunset, smiling as she gives a high-five to a yellow Labrador Retriever wearing a harness. The ocean waves roll in the background." ``` #### get_video_features[[transformers.Qwen3VLMoeForConditionalGeneration.get_video_features]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1607) - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*) -- Last layer hidden-state after a pooling operation on the spatial dimensions. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the model at the output of each stage. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. - **deepstack_features** (`List[torch.FloatTensor]`, *optional*) -- List of hidden-states (feature maps) from deepstack layers. Example: ```python >>> from PIL import Image >>> from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration >>> model = Qwen3VLMoeForConditionalGeneration.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct") >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct") >>> messages = [ ... { ... "role": "user", "content": [ ... {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"}, ... {"type": "text", "text": "Where is the cat standing?"}, ... ] ... }, ... ] >>> inputs = processor.apply_chat_template( ... messages, ... tokenize=True, ... return_dict=True, ... return_tensors="pt", ... add_generation_prompt=True ... ) >>> # Generate >>> generate_ids = model.generate(**inputs) >>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0] ``` **Parameters:** pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input videos. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*) : The temporal, height and width of feature shape of each video in LLM. **Returns:** ``BaseModelOutputWithDeepstackFeatures` or `tuple(torch.FloatTensor)`` A `BaseModelOutputWithDeepstackFeatures` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Qwen3VLMoeConfig](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeConfig)) and inputs. #### get_image_features[[transformers.Qwen3VLMoeForConditionalGeneration.get_image_features]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py#L1624) - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*) -- Last layer hidden-state after a pooling operation on the spatial dimensions. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the model at the output of each stage. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. - **deepstack_features** (`List[torch.FloatTensor]`, *optional*) -- List of hidden-states (feature maps) from deepstack layers. Example: ```python >>> from PIL import Image >>> from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration >>> model = Qwen3VLMoeForConditionalGeneration.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct") >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct") >>> messages = [ ... { ... "role": "user", "content": [ ... {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"}, ... {"type": "text", "text": "Where is the cat standing?"}, ... ] ... }, ... ] >>> inputs = processor.apply_chat_template( ... messages, ... tokenize=True, ... return_dict=True, ... return_tensors="pt", ... add_generation_prompt=True ... ) >>> # Generate >>> generate_ids = model.generate(**inputs) >>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0] ``` **Parameters:** pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) : The temporal, height and width of feature shape of each image in LLM. **Returns:** ``BaseModelOutputWithDeepstackFeatures` or `tuple(torch.FloatTensor)`` A `BaseModelOutputWithDeepstackFeatures` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Qwen3VLMoeConfig](/docs/transformers/v5.3.0/en/model_doc/qwen3_vl_moe#transformers.Qwen3VLMoeConfig)) and inputs.