# Qwen2-VL

## Overview

The [Qwen2-VL](https://huggingface.co/papers/2409.12191) ([blog post](https://qwenlm.github.io/blog/qwen2-vl/)) model is a major update to [Qwen-VL](https://huggingface.co/papers/2308.12966) from the Qwen team at Alibaba Research.

The abstract from the blog is the following:

*This blog introduces Qwen2-VL, an advanced version of the Qwen-VL model that has undergone significant enhancements over the past year. Key improvements include enhanced image comprehension, advanced video understanding, integrated visual agent functionality, and expanded multilingual support. The model architecture has been optimized for handling arbitrary image resolutions through Naive Dynamic Resolution support and utilizes Multimodal Rotary Position Embedding (M-ROPE) to effectively process both 1D textual and multi-dimensional visual data. This updated model demonstrates competitive performance against leading AI systems like GPT-4o and Claude 3.5 Sonnet in vision-related tasks and ranks highly among open-source models in text capabilities. These advancements make Qwen2-VL a versatile tool for various applications requiring robust multimodal processing and reasoning abilities.*

 Qwen2-VL architecture. Taken from the blog post. 

This model was contributed by [simonJJJ](https://huggingface.co/simonJJJ).

## Usage example

### Single Media inference

The model can accept both images and videos as input. Here's an example code for inference.

```python

import torch
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor

# Load the model in half-precision on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", device_map="auto")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

conversation = [
    {
        "role":"user",
        "content":[
            {
                "type":"image",
                "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
            },
            {
                "type":"text",
                "text":"Describe this image."
            }
        ]
    }
]

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

# Inference: Generation of the output
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)

# Video
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "video", "path": "/path/to/video.mp4"},
            {"type": "text", "text": "What happened in the video?"},
        ],
    }
]

inputs = processor.apply_chat_template(
    conversation,
    fps=1,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

# Inference: Generation of the output
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)
```

### Batch Mixed Media Inference

The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.

```python

# Conversation for the first image
conversation1 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "path": "/path/to/image1.jpg"},
            {"type": "text", "text": "Describe this image."}
        ]
    }
]

# Conversation with two images
conversation2 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "path": "/path/to/image2.jpg"},
            {"type": "image", "path": "/path/to/image3.jpg"},
            {"type": "text", "text": "What is written in the pictures?"}
        ]
    }
]

# Conversation with pure text
conversation3 = [
    {
        "role": "user",
        "content": "who are you?"
    }
]

# Conversation with mixed midia
conversation4 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "path": "/path/to/image3.jpg"},
            {"type": "image", "path": "/path/to/image4.jpg"},
            {"type": "video", "path": "/path/to/video.jpg"},
            {"type": "text", "text": "What are the common elements in these medias?"},
        ],
    }
]

conversations = [conversation1, conversation2, conversation3, conversation4]
# Preparation for batch inference
ipnuts = processor.apply_chat_template(
    conversations,
    fps=1,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

# Batch Inference
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)
```

### Usage Tips

#### Image Resolution trade-off

The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.

```python
min_pixels = 224*224
max_pixels = 2048*2048
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
```

In case of limited GPU RAM, one can reduce the resolution as follows:

```python
min_pixels = 256*28*28
max_pixels = 1024*28*28 
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
```

This ensures each image gets encoded using a number between 256-1024 tokens. The 28 comes from the fact that the model uses a patch size of 14 and a temporal patch size of 2 (14 x 2 = 28).

#### Multiple Image Inputs

By default, images and video content are directly included in the conversation. When handling multiple images, it's helpful to add labels to the images and videos for better reference. Users can control this behavior with the following settings:

```python
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image"}, 
            {"type": "text", "text": "Hello, how are you?"}
        ]
    },
    {
        "role": "assistant",
        "content": "I'm doing well, thank you for asking. How can I assist you today?"
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Can you describe these images and video?"}, 
            {"type": "image"}, 
            {"type": "image"}, 
            {"type": "video"}, 
            {"type": "text", "text": "These are from my vacation."}
        ]
    },
    {
        "role": "assistant",
        "content": "I'd be happy to describe the images and video for you. Could you please provide more context about your vacation?"
    },
    {
        "role": "user",
        "content": "It was a trip to the mountains. Can you see the details in the images and video?"
    }
]

# default:
prompt_without_id = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Excepted output: 'system\nYou are a helpful assistant.\nuser\nHello, how are you?\nassistant\nI'm doing well, thank you for asking. How can I assist you today?\nuser\nCan you describe these images and video?These are from my vacation.\nassistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?\nuser\nIt was a trip to the mountains. Can you see the details in the images and video?\nassistant\n'

# add ids
prompt_with_id = processor.apply_chat_template(conversation, add_generation_prompt=True, add_vision_id=True)
# Excepted output: 'system\nYou are a helpful assistant.\nuser\nPicture 1: Hello, how are you?\nassistant\nI'm doing well, thank you for asking. How can I assist you today?\nuser\nCan you describe these images and video?Picture 2: Picture 3: Video 1: These are from my vacation.\nassistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?\nuser\nIt was a trip to the mountains. Can you see the details in the images and video?\nassistant\n'

```

#### Flash-Attention 2 to speed up generation

First, make sure to install the latest version of Flash Attention 2:

```bash
pip install -U flash-attn --no-build-isolation
```

Also, you should have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.

To load and run a model using Flash Attention-2, simply add `attn_implementation="flash_attention_2"` when loading the model as follows:

```python
from transformers import Qwen2VLForConditionalGeneration

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-VL-7B-Instruct", 
    dtype=torch.bfloat16, 
    attn_implementation="flash_attention_2",
)
```

## Qwen2VLConfig[[transformers.Qwen2VLConfig]]

#### transformers.Qwen2VLConfig[[transformers.Qwen2VLConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/configuration_qwen2_vl.py#L136)

This is the configuration class to store the configuration of a Qwen2VLModel. It is used to instantiate a Qwen2 Vl
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.5.1/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.5.1/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import Qwen2VLForConditionalGeneration, Qwen2VLConfig

>>> # Initializing a Qwen2VL style configuration
>>> configuration = Qwen2VLConfig()

>>> # Initializing a model from the Qwen2-VL-7B style configuration
>>> model = Qwen2VLForConditionalGeneration(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

text_config (`Union[dict, ~configuration_utils.PreTrainedConfig]`, *optional*) : The config object or dictionary of the text backbone.

vision_config (`Union[dict, ~configuration_utils.PreTrainedConfig]`, *optional*) : The config object or dictionary of the vision backbone.

image_token_id (`int`, *optional*, defaults to `151655`) : The image token index used as a placeholder for input images.

video_token_id (`int`, *optional*, defaults to `151656`) : The video token index used as a placeholder for input videos.

vision_start_token_id (`int`, *optional*, defaults to `151652`) : Token ID that marks the start of a visual segment in the multimodal input sequence.

vision_end_token_id (`int`, *optional*, defaults to `151653`) : Token ID that marks the end of a visual segment in the multimodal input sequence.

tie_word_embeddings (`bool`, *optional*, defaults to `False`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.

## Qwen2VLTextConfig[[transformers.Qwen2VLTextConfig]]

#### transformers.Qwen2VLTextConfig[[transformers.Qwen2VLTextConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/configuration_qwen2_vl.py#L46)

This is the configuration class to store the configuration of a Qwen2VLModel. It is used to instantiate a Qwen2 Vl
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.5.1/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.5.1/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.

```python
>>> from transformers import Qwen2VLTextModel, Qwen2VLConfig

>>> # Initializing a Qwen2VL style configuration
>>> configuration = Qwen2VLConfig()

>>> # Initializing a model from the Qwen2-VL-7B style configuration
>>> model = Qwen2VLTextModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

vocab_size (`int`, *optional*, defaults to `152064`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.

hidden_size (`int`, *optional*, defaults to `8192`) : Dimension of the hidden representations.

intermediate_size (`int`, *optional*, defaults to `29568`) : Dimension of the MLP representations.

num_hidden_layers (`int`, *optional*, defaults to `80`) : Number of hidden layers in the Transformer decoder.

num_attention_heads (`int`, *optional*, defaults to `64`) : Number of attention heads for each attention layer in the Transformer decoder.

num_key_value_heads (`int`, *optional*, defaults to `8`) : 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`.

hidden_act (`str`, *optional*, defaults to `silu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

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-05`) : 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` or when the model is a decoder-only generative model.

use_sliding_window (`bool`, *optional*, defaults to `False`) : Whether to use sliding window attention.

sliding_window (`int`, *optional*, defaults to `4096`) : Sliding window attention window size. If `None`, no sliding window is applied.

max_window_layers (`int`, *optional*, defaults to `80`) : The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any additional layer afterwards will use SWA (Sliding Window Attention).

layer_types (`list[str]`, *optional*) : A list that explicitly maps each layer index with its layer type. If not provided, it will be automatically generated based on config values.

attention_dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities.

rope_parameters (`Union[~modeling_rope_utils.RopeParameters, dict]`, *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`.

bos_token_id (`int`, *optional*, defaults to `151643`) : Token id used for beginning-of-stream in the vocabulary.

eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `151645`) : Token id used for end-of-stream in the vocabulary.

pad_token_id (`int`, *optional*) : Token id used for padding in the vocabulary.

## Qwen2VLImageProcessor[[transformers.Qwen2VLImageProcessor]]

#### transformers.Qwen2VLImageProcessor[[transformers.Qwen2VLImageProcessor]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L92)

Constructs a Qwen2VLImageProcessor image processor.

preprocesstransformers.Qwen2VLImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L140[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.qwen2_vl.image_processing_qwen2_vl.Qwen2VLImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) --
  Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
  passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **min_pixels** (`int`, *kwargs*, *optional*, defaults to `56 * 56`) --
  The min pixels of the image to resize the image.
- **max_pixels** (`int`, *kwargs*, *optional*, defaults to `28 * 28 * 1280`) --
  The max pixels of the image to resize the image.
- **patch_size** (`int`, *kwargs*, *optional*, defaults to 14) --
  The spatial patch size of the vision encoder.
- **temporal_patch_size** (`int`, *kwargs*, *optional*, defaults to 2) --
  The temporal patch size of the vision encoder.
- **merge_size** (`int`, *kwargs*, *optional*, defaults to 2) --
  The merge size of the vision encoder to llm encoder.
- **return_tensors** (`str` or [TensorType](/docs/transformers/v5.5.1/en/internal/file_utils#transformers.TensorType), *optional*) --
  Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.
- ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.1/en/main_classes/processors#transformers.ImagesKwargs), *optional*) --
  Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
  for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.

**Parameters:**

min_pixels (`int`, *kwargs*, *optional*, defaults to `56 * 56`) : The min pixels of the image to resize the image.

max_pixels (`int`, *kwargs*, *optional*, defaults to `28 * 28 * 1280`) : The max pixels of the image to resize the image.

patch_size (`int`, *kwargs*, *optional*, defaults to 14) : The spatial patch size of the vision encoder.

temporal_patch_size (`int`, *kwargs*, *optional*, defaults to 2) : The temporal patch size of the vision encoder.

merge_size (`int`, *kwargs*, *optional*, defaults to 2) : The merge size of the vision encoder to llm encoder.

- ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.1/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

**Returns:**

``~image_processing_base.BatchFeature``

- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.

## Qwen2VLVideoProcessor[[transformers.Qwen2VLVideoProcessor]]

#### transformers.Qwen2VLVideoProcessor[[transformers.Qwen2VLVideoProcessor]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/video_processing_qwen2_vl.py#L72)

Constructs a fast Qwen2-VL image processor that dynamically resizes videos based on the original videos.

preprocesstransformers.Qwen2VLVideoProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/video_processing_utils.py#L327[{"name": "videos", "val": ": typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]]]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.VideosKwargs]"}]- **do_resize** (`bool`, *optional*, defaults to `self.do_resize`) --
  Whether to resize the video's (height, width) dimensions to the specified `size`. Can be overridden by the
  `do_resize` parameter in the `preprocess` method.
- **size** (`dict`, *optional*, defaults to `self.size`) --
  Size of the output video after resizing. Can be overridden by the `size` parameter in the `preprocess`
  method.
- **size_divisor** (`int`, *optional*, defaults to `self.size_divisor`) --
  The size by which to make sure both the height and width can be divided.
- **default_to_square** (`bool`, *optional*, defaults to `self.default_to_square`) --
  Whether to default to a square video when resizing, if size is an int.
- **resample** (`PILImageResampling`, *optional*, defaults to `self.resample`) --
  Resampling filter to use if resizing the video. Only has an effect if `do_resize` is set to `True`. Can be
  overridden by the `resample` parameter in the `preprocess` method.
- **do_center_crop** (`bool`, *optional*, defaults to `self.do_center_crop`) --
  Whether to center crop the video to the specified `crop_size`. Can be overridden by `do_center_crop` in the
  `preprocess` method.
- **crop_size** (`dict[str, int]` *optional*, defaults to `self.crop_size`) --
  Size of the output video after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
  method.
- **do_rescale** (`bool`, *optional*, defaults to `self.do_rescale`) --
  Whether to rescale the video by the specified scale `rescale_factor`. Can be overridden by the
  `do_rescale` parameter in the `preprocess` method.
- **rescale_factor** (`int` or `float`, *optional*, defaults to `self.rescale_factor`) --
  Scale factor to use if rescaling the video. Only has an effect if `do_rescale` is set to `True`. Can be
  overridden by the `rescale_factor` parameter in the `preprocess` method.
- **do_normalize** (`bool`, *optional*, defaults to `self.do_normalize`) --
  Whether to normalize the video. Can be overridden by the `do_normalize` parameter in the `preprocess`
  method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
- **image_mean** (`float` or `list[float]`, *optional*, defaults to `self.image_mean`) --
  Mean to use if normalizing the video. This is a float or list of floats the length of the number of
  channels in the video. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
  overridden by the `image_mean` parameter in the `preprocess` method.
- **image_std** (`float` or `list[float]`, *optional*, defaults to `self.image_std`) --
  Standard deviation to use if normalizing the video. This is a float or list of floats the length of the
  number of channels in the video. Can be overridden by the `image_std` parameter in the `preprocess` method.
  Can be overridden by the `image_std` parameter in the `preprocess` method.
- **do_convert_rgb** (`bool`, *optional*, defaults to `self.image_std`) --
  Whether to convert the video to RGB.
- **video_metadata** (`VideoMetadata`, *optional*) --
  Metadata of the video containing information about total duration, fps and total number of frames.
- **do_sample_frames** (`int`, *optional*, defaults to `self.do_sample_frames`) --
  Whether to sample frames from the video before processing or to process the whole video.
- **num_frames** (`int`, *optional*, defaults to `self.num_frames`) --
  Maximum number of frames to sample when `do_sample_frames=True`.
- **fps** (`int` or `float`, *optional*, defaults to `self.fps`) --
  Target frames to sample per second when `do_sample_frames=True`.
- **return_tensors** (`str` or `TensorType`, *optional*) --
  Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
- **data_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) --
  The channel dimension format for the output video. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
  - Unset: Use the channel dimension format of the input video.
- **input_data_format** (`ChannelDimension` or `str`, *optional*) --
  The channel dimension format for the input video. If unset, the channel dimension format is inferred
  from the input video. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
  - `"none"` or `ChannelDimension.NONE`: video in (height, width) format.
- **device** (`torch.device`, *optional*) --
  The device to process the videos on. If unset, the device is inferred from the input videos.
- **return_metadata** (`bool`, *optional*) --
  Whether to return video metadata or not.0

**Parameters:**

do_resize (`bool`, *optional*, defaults to `self.do_resize`) : Whether to resize the video's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in the `preprocess` method.

size (`dict`, *optional*, defaults to `self.size`) : Size of the output video after resizing. Can be overridden by the `size` parameter in the `preprocess` method.

size_divisor (`int`, *optional*, defaults to `self.size_divisor`) : The size by which to make sure both the height and width can be divided.

default_to_square (`bool`, *optional*, defaults to `self.default_to_square`) : Whether to default to a square video when resizing, if size is an int.

resample (`PILImageResampling`, *optional*, defaults to `self.resample`) : Resampling filter to use if resizing the video. Only has an effect if `do_resize` is set to `True`. Can be overridden by the `resample` parameter in the `preprocess` method.

do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`) : Whether to center crop the video to the specified `crop_size`. Can be overridden by `do_center_crop` in the `preprocess` method.

crop_size (`dict[str, int]` *optional*, defaults to `self.crop_size`) : Size of the output video after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess` method.

do_rescale (`bool`, *optional*, defaults to `self.do_rescale`) : Whether to rescale the video by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method.

rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`) : Scale factor to use if rescaling the video. Only has an effect if `do_rescale` is set to `True`. Can be overridden by the `rescale_factor` parameter in the `preprocess` method.

do_normalize (`bool`, *optional*, defaults to `self.do_normalize`) : Whether to normalize the video. Can be overridden by the `do_normalize` parameter in the `preprocess` method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.

image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`) : Mean to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be overridden by the `image_mean` parameter in the `preprocess` method.

image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`) : Standard deviation to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method.

do_convert_rgb (`bool`, *optional*, defaults to `self.image_std`) : Whether to convert the video to RGB.

video_metadata (`VideoMetadata`, *optional*) : Metadata of the video containing information about total duration, fps and total number of frames.

do_sample_frames (`int`, *optional*, defaults to `self.do_sample_frames`) : Whether to sample frames from the video before processing or to process the whole video.

num_frames (`int`, *optional*, defaults to `self.num_frames`) : Maximum number of frames to sample when `do_sample_frames=True`.

fps (`int` or `float`, *optional*, defaults to `self.fps`) : Target frames to sample per second when `do_sample_frames=True`.

return_tensors (`str` or `TensorType`, *optional*) : Returns stacked tensors if set to `pt, otherwise returns a list of tensors.

data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) : The channel dimension format for the output video. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input video.

input_data_format (`ChannelDimension` or `str`, *optional*) : The channel dimension format for the input video. If unset, the channel dimension format is inferred from the input video. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: video in (height, width) format.

device (`torch.device`, *optional*) : The device to process the videos on. If unset, the device is inferred from the input videos.

return_metadata (`bool`, *optional*) : Whether to return video metadata or not. 

min_pixels (`int`, *optional*, defaults to `56 * 56`) : The min pixels of the image to resize the image.

max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`) : The max pixels of the image to resize the image.

patch_size (`int`, *optional*, defaults to 14) : The spacial patch size of the vision encoder.

temporal_patch_size (`int`, *optional*, defaults to 2) : The temporal patch size of the vision encoder.

merge_size (`int`, *optional*, defaults to 2) : The merge size of the vision encoder to llm encoder.

min_frames (`int`, *optional*, defaults to 4) : The minimum number of frames that can be sampled.

max_frames (`int`, *optional*, defaults to 768) : The maximum number of frames that can be sampled.

## Qwen2VLImageProcessorPil[[transformers.Qwen2VLImageProcessorPil]]

#### transformers.Qwen2VLImageProcessorPil[[transformers.Qwen2VLImageProcessorPil]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/image_processing_pil_qwen2_vl.py#L87)

Constructs a Qwen2VLImageProcessor image processor.

preprocesstransformers.Qwen2VLImageProcessorPil.preprocesshttps://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/image_processing_pil_qwen2_vl.py#L135[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.qwen2_vl.image_processing_pil_qwen2_vl.Qwen2VLImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) --
  Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
  passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **min_pixels** (`int`, *kwargs*, *optional*, defaults to `56 * 56`) --
  The min pixels of the image to resize the image.
- **max_pixels** (`int`, *kwargs*, *optional*, defaults to `28 * 28 * 1280`) --
  The max pixels of the image to resize the image.
- **patch_size** (`int`, *kwargs*, *optional*, defaults to 14) --
  The spatial patch size of the vision encoder.
- **temporal_patch_size** (`int`, *kwargs*, *optional*, defaults to 2) --
  The temporal patch size of the vision encoder.
- **merge_size** (`int`, *kwargs*, *optional*, defaults to 2) --
  The merge size of the vision encoder to llm encoder.
- **return_tensors** (`str` or [TensorType](/docs/transformers/v5.5.1/en/internal/file_utils#transformers.TensorType), *optional*) --
  Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.
- ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.1/en/main_classes/processors#transformers.ImagesKwargs), *optional*) --
  Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
  for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.

**Parameters:**

min_pixels (`int`, *kwargs*, *optional*, defaults to `56 * 56`) : The min pixels of the image to resize the image.

max_pixels (`int`, *kwargs*, *optional*, defaults to `28 * 28 * 1280`) : The max pixels of the image to resize the image.

patch_size (`int`, *kwargs*, *optional*, defaults to 14) : The spatial patch size of the vision encoder.

temporal_patch_size (`int`, *kwargs*, *optional*, defaults to 2) : The temporal patch size of the vision encoder.

merge_size (`int`, *kwargs*, *optional*, defaults to 2) : The merge size of the vision encoder to llm encoder.

- ****kwargs** ([ImagesKwargs](/docs/transformers/v5.5.1/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

**Returns:**

``~image_processing_base.BatchFeature``

- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.

## Qwen2VLProcessor[[transformers.Qwen2VLProcessor]]

#### transformers.Qwen2VLProcessor[[transformers.Qwen2VLProcessor]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/processing_qwen2_vl.py#L44)

Constructs a Qwen2VLProcessor which wraps a image processor, a tokenizer, and a video processor into a single processor.

[Qwen2VLProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLProcessor) offers all the functionalities of [Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor), [Qwen2Tokenizer](/docs/transformers/v5.5.1/en/model_doc/qwen2#transformers.Qwen2Tokenizer), and [Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor). See the
[~Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor), [~Qwen2Tokenizer](/docs/transformers/v5.5.1/en/model_doc/qwen2#transformers.Qwen2Tokenizer), and [~Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor) for more information.

__call__transformers.Qwen2VLProcessor.__call__https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/processing_qwen2_vl.py#L60[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None"}, {"name": "text", "val": ": str | list[str] | list[list[str]] = None"}, {"name": "videos", "val": ": typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.qwen2_vl.processing_qwen2_vl.Qwen2VLProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`, *optional*) --
  Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
  passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **text** (`Union[str, list[str], list[list[str]]]`, *optional*) --
  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
  (pretokenized string). If you pass a pretokenized input, set `is_split_into_words=True` to avoid ambiguity with batched inputs.
- **videos** (`Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]]`, *optional*) --
  Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
  passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
- **return_tensors** (`str` or [TensorType](/docs/transformers/v5.5.1/en/internal/file_utils#transformers.TensorType), *optional*) --
  If set, will return tensors of a particular framework. Acceptable values are:

  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return NumPy `np.ndarray` objects.
- ****kwargs** ([ProcessingKwargs](/docs/transformers/v5.5.1/en/main_classes/processors#transformers.ProcessingKwargs), *optional*) --
  Additional processing options for each modality (text, images, videos, audio). Model-specific parameters
  are listed above; see the TypedDict class for the complete list of supported arguments.0[BatchFeature](/docs/transformers/v5.5.1/en/main_classes/feature_extractor#transformers.BatchFeature)A [BatchFeature](/docs/transformers/v5.5.1/en/main_classes/feature_extractor#transformers.BatchFeature) with the following fields:

- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
  `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
  `None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.

**Parameters:**

image_processor (`Qwen2VLImageProcessor`) : The image processor is a required input.

tokenizer (`Qwen2Tokenizer`) : The tokenizer is a required input.

video_processor (`Qwen2VLVideoProcessor`) : The video processor is a required input.

chat_template (`str`) : A Jinja template to convert lists of messages in a chat into a tokenizable string.

**Returns:**

`[BatchFeature](/docs/transformers/v5.5.1/en/main_classes/feature_extractor#transformers.BatchFeature)`

A [BatchFeature](/docs/transformers/v5.5.1/en/main_classes/feature_extractor#transformers.BatchFeature) with the following fields:

- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
  `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
  `None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.

## Qwen2VLTextModel[[transformers.Qwen2VLTextModel]]

#### transformers.Qwen2VLTextModel[[transformers.Qwen2VLTextModel]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L799)

The bare Qwen2 Vl Text Model outputting raw hidden-states without any specific head on to.

This model inherits from [PreTrainedModel](/docs/transformers/v5.5.1/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.Qwen2VLTextModel.forwardhttps://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L825[{"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": "**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/v5.5.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.5.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.5.1/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/v5.5.1/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/v5.5.1/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`).0[BaseModelOutputWithPast](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPast](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.
The [Qwen2VLTextModel](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLTextModel) 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.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.5.1/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.

**Parameters:**

config ([Qwen2VLTextConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLTextConfig)) : 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/v5.5.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[BaseModelOutputWithPast](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPast](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.

## Qwen2VLModel[[transformers.Qwen2VLModel]]

#### transformers.Qwen2VLModel[[transformers.Qwen2VLModel]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L914)

The bare Qwen2 Vl Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v5.5.1/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.Qwen2VLModel.forwardhttps://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1228[{"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": "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": "rope_deltas", "val": ": torch.LongTensor | None = None"}, {"name": "mm_token_type_ids", "val": ": torch.IntTensor | 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/v5.5.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.5.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.5.1/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/v5.5.1/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/v5.5.1/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`).
- **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
  [Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor). See `Qwen2VLImageProcessor.__call__()` for details ([Qwen2VLProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLProcessor) uses
  [Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor) 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
  [Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor). See [Qwen2VLVideoProcessor.__call__()](/docs/transformers/v5.5.1/en/model_doc/pe_video#transformers.PeVideoVideoProcessor.__call__) for details ([Qwen2VLProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLProcessor) uses
  [Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor) 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.
- **rope_deltas** (`torch.LongTensor` of shape `(batch_size, )`, *optional*) --
  The rope index difference between sequence length and multimodal rope.
- **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/v5.5.1/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/v5.5.1/en/model_doc/align#transformers.AlignProcessor.__call__) for details.0`Qwen2VLModelOutputWithPast` or `tuple(torch.FloatTensor)`A `Qwen2VLModelOutputWithPast` 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.
The [Qwen2VLModel](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLModel) 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/v5.5.1/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.

**Parameters:**

config ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) : 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/v5.5.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Qwen2VLModelOutputWithPast` or `tuple(torch.FloatTensor)``

A `Qwen2VLModelOutputWithPast` 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.
#### get_video_features[[transformers.Qwen2VLModel.get_video_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1094)

- **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.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **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.

**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:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.
#### get_image_features[[transformers.Qwen2VLModel.get_image_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1116)

- **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.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **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.

**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:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.

## Qwen2VLForConditionalGeneration[[transformers.Qwen2VLForConditionalGeneration]]

#### transformers.Qwen2VLForConditionalGeneration[[transformers.Qwen2VLForConditionalGeneration]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1303)

forwardtransformers.Qwen2VLForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1351[{"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": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "use_cache", "val": ": bool | 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": "rope_deltas", "val": ": torch.LongTensor | None = None"}, {"name": "mm_token_type_ids", "val": ": torch.IntTensor | None = None"}, {"name": "logits_to_keep", "val": ": int | torch.Tensor = 0"}, {"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/v5.5.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.5.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.5.1/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/v5.5.1/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/v5.5.1/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.
- **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]`.
- **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`).
- **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
  [Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor). See `Qwen2VLImageProcessor.__call__()` for details ([Qwen2VLProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLProcessor) uses
  [Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor) 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
  [Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor). See [Qwen2VLVideoProcessor.__call__()](/docs/transformers/v5.5.1/en/model_doc/pe_video#transformers.PeVideoVideoProcessor.__call__) for details ([Qwen2VLProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLProcessor) uses
  [Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor) 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.
- **rope_deltas** (`torch.LongTensor` of shape `(batch_size, )`, *optional*) --
  The rope index difference between sequence length and multimodal rope.
- **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/v5.5.1/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/v5.5.1/en/model_doc/align#transformers.AlignProcessor.__call__) for details.

- **logits_to_keep** (`Union[int, torch.Tensor]`, *optional*, defaults to `0`) --
  If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
  `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
  token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
  If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
  This is useful when using packed tensor format (single dimension for batch and sequence length).0`Qwen2VLCausalLMOutputWithPast` or `tuple(torch.FloatTensor)`A `Qwen2VLCausalLMOutputWithPast` 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.
The [Qwen2VLForConditionalGeneration](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLForConditionalGeneration) 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.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.5.1/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.

Example:

```python
>>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration

>>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

>>> messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
            {"type": "text", "text": "Describe the image."},
        ],
    }
]

>>> inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)

>>> # Generate
>>> generated_ids = model.generate(**inputs, max_new_tokens=1024)
>>> 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)[0]
>>> print(output_text)
```

**Parameters:**

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/v5.5.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.5.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.5.1/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/v5.5.1/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/v5.5.1/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.

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]`.

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`).

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 [Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor). See `Qwen2VLImageProcessor.__call__()` for details ([Qwen2VLProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLProcessor) uses [Qwen2VLImageProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLImageProcessor) 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 [Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor). See [Qwen2VLVideoProcessor.__call__()](/docs/transformers/v5.5.1/en/model_doc/pe_video#transformers.PeVideoVideoProcessor.__call__) for details ([Qwen2VLProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLProcessor) uses [Qwen2VLVideoProcessor](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLVideoProcessor) 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.

rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*) : The rope index difference between sequence length and multimodal rope.

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/v5.5.1/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/v5.5.1/en/model_doc/align#transformers.AlignProcessor.__call__) for details. 

logits_to_keep (`Union[int, torch.Tensor]`, *optional*, defaults to `0`) : If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

**Returns:**

``Qwen2VLCausalLMOutputWithPast` or `tuple(torch.FloatTensor)``

A `Qwen2VLCausalLMOutputWithPast` 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.
#### get_video_features[[transformers.Qwen2VLForConditionalGeneration.get_video_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1319)

- **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.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **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.

Example:

```python
>>> from PIL import Image
>>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration

>>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-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:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.
#### get_image_features[[transformers.Qwen2VLForConditionalGeneration.get_image_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.1/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1336)

- **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.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **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.

Example:

```python
>>> from PIL import Image
>>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration

>>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-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:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.5.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([Qwen2VLConfig](/docs/transformers/v5.5.1/en/model_doc/qwen2_vl#transformers.Qwen2VLConfig)) and inputs.