Qwen3-VL

The Qwen3-VL model is the latest vision–language generation in the Qwen series. It improves text understanding and generation, visual perception and reasoning, spatial and temporal (video) understanding, and capabilities for visual agents—for example GUI understanding and tool use. It supports a long multimodal context (native 256K tokens, extendable up to 1M), expanded OCR (32 languages), and strong performance on multimodal reasoning including STEM-oriented tasks. The family is offered in dense and MoE sizes, with Instruct and reasoning-focused variants for different deployment needs. RBLN NPUs can accelerate Qwen3-VL inference using Optimum RBLN.

API Reference

Classes

RBLNQwen3VLVisionModel

Bases: RBLNModel

RBLN optimized Qwen3-VL vision transformer model.

This class provides hardware-accelerated inference for Qwen3-VL vision transformers on RBLN devices, supporting image and video encoding for multimodal vision-language tasks.

Functions

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

RBLNQwen3VLForConditionalGeneration

Bases: RBLNQwen3VLModel, RBLNDecoderOnlyModelForCausalLM

RBLNQwen3VLForConditionalGeneration is a multi-modal model that integrates vision and language processing capabilities, optimized for RBLN NPUs. It is designed for conditional generation tasks that involve both image and text inputs.

This model inherits from [RBLNDecoderOnlyModelForCausalLM]. Check the superclass documentation for the generic methods the library implements for all its models.

Important Note

This model includes a Large Language Model (LLM). For optimal performance, it is highly recommended to use tensor parallelism for the language model. This can be achieved by using the rbln_config parameter in the from_pretrained method. Refer to the from_pretrained documentation and the RBLNQwen3VLForConditionalGenerationConfig class for details.

Examples:

from optimum.rbln import RBLNQwen3VLForConditionalGeneration

model = RBLNQwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-4B-Instruct",
    export=True,
    rbln_config={
        "visual": {
            "max_seq_lens": 6400,
            "device": 0,
        },
        "tensor_parallel_size": 8,
        "kvcache_partition_len": 16_384,
        "max_seq_len": 262_144,
        "device": [0, 1, 2, 3, 4, 5, 6, 7],
    },
)

model.save_pretrained("compiled-qwen3-vl-4b-instruct")

Functions

generate(input_ids, attention_mask=None, generation_config=None, **kwargs)

The generate function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to generate text from the model. Check the HuggingFace transformers documentation for more details.

Parameters:

Name	Type	Description	Default
input_ids	LongTensor	The input ids to the model.	required
attention_mask	LongTensor	The attention mask to the model.	None
generation_config	GenerationConfig	The generation configuration to be used as base parametrization for the generation call. **kwargs passed to generate matching the attributes of generation_config will override them. If generation_config is not provided, the default will be used, which had the following loading priority: 1) from the generation_config.json model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit GenerationConfig’s default values.	None
kwargs	dict[str, Any]	Additional arguments passed to the generate function. See the HuggingFace transformers documentation for more details.	{}

Returns:

Type	Description
Union[ModelOutput, LongTensor]	A ModelOutput (if return_dict_in_generate=True or when config.return_dict_in_generate=True) or a torch.LongTensor.

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

set_adapter(adapter_name)

Sets the active adapter(s) for the model using adapter name(s).

Parameters:

Name	Type	Description	Default
adapter_name	Union[str, List[str]]	The name(s) of the adapter(s) to be activated. Can be a single adapter name or a list of adapter names.	required

Raises:

Type	Description
ValueError	If the model is not configured with LoRA or if the adapter name is not found.

Functions

Classes

RBLNQwen3VLForConditionalGenerationConfig

Bases: RBLNDecoderOnlyModelForCausalLMConfig

Configuration class for RBLNQwen3VLForConditionalGeneration.

This configuration class stores the configuration parameters specific to RBLN-optimized Qwen3-VL models for multimodal conditional generation tasks that combine vision and language processing capabilities.

Functions

__init__(use_inputs_embeds=True, visual=None, **kwargs)

Parameters:

Name	Type	Description	Default
use_inputs_embeds	bool	Whether or not to use inputs_embeds as input. Defaults to True.	True
visual	Optional[RBLNModelConfig]	Configuration for the vision encoder component.	None
kwargs	Any	Additional arguments passed to the parent RBLNDecoderOnlyModelForCausalLMConfig.	{}

Raises:

Type	Description
ValueError	If use_inputs_embeds is False.

RBLNQwen3VLVisionModelConfig

Bases: RBLNModelConfig

Configuration class for RBLNQwen3VLVisionModel.

This configuration class stores the configuration parameters specific to RBLN-optimized Qwen3-VL vision transformer models for processing images and videos.

Functions

__init__(max_seq_lens=None, **kwargs)

Parameters:

Name	Type	Description	Default
max_seq_lens	Optional[Union[int, List[int]]]	Maximum sequence lengths for Vision Transformer attention. Can be an integer or list of integers, each indicating the number of patches in a sequence for an image or video. For example, an image of 224x224 pixels with patch size 16 and spatial_merge_size 2 yields (224/16/2) * (224/16/2) = 49 merged patches. RBLN optimization runs inference per image or video frame, so set max_seq_len to match the maximum expected resolution to reduce computation. If not provided, a ValueError is raised.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If max_seq_lens is None or not provided.

from_pretrained(path, rbln_config=None, return_unused_kwargs=False, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
rbln_config	Optional[Dict[str, Any]]	Additional configuration to override.	None
return_unused_kwargs	bool	Whether to return unused kwargs.	False
kwargs	Optional[Dict[str, Any]]	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	Union[RBLNModelConfig, Tuple[RBLNModelConfig, Dict[str, Any]]]	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

Examples:

config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")

load(path, rbln_config=None, return_unused_kwargs=False, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Deprecated

This method is deprecated and will be removed in version 0.11.0. Use from_pretrained instead.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
rbln_config	Optional[Dict[str, Any]]	Additional configuration to override.	None
return_unused_kwargs	bool	Whether to return unused kwargs.	False
kwargs	Optional[Dict[str, Any]]	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	Union[RBLNModelConfig, Tuple[RBLNModelConfig, Dict[str, Any]]]	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

Examples:

# Deprecated usage:
config = RBLNResNetForImageClassificationConfig.load("/path/to/model")
# Recommended usage:
config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")