# MuseGlimmer

## Overview

The MuseGlimmer model was proposed in []() by .

The abstract from the paper is the following:

Tips:

This model was contributed by [INSERT YOUR HF USERNAME HERE](https://huggingface.co/).
The original code can be found [here]().

## Usage examples

## MuseGlimmerConfig[[transformers.MuseGlimmerConfig]]

#### transformers.MuseGlimmerConfig[[transformers.MuseGlimmerConfig]]

```python
transformers.MuseGlimmerConfig(transformers_version: str | None = None, architectures: list[str] | None = None, output_hidden_states: bool | None = False, return_dict: bool | None = True, dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None, chunk_size_feed_forward: int = 0, is_encoder_decoder: bool = False, id2label: dict[int, str] | dict[str, str] | None = None, label2id: dict[str, int] | dict[str, str] | None = None, problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None, text_config: dict | transformers.configuration_utils.PreTrainedConfig | None = None, vision_config: dict | transformers.configuration_utils.PreTrainedConfig | None = None, image_token_id: int = 200092, video_token_id: int = 200091, out_hidden_size: int = 6144, projector_hidden_size: int = 4096, projector_hidden_act: str = 'gelu')
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/configuration_muse_glimmer.py#L167)

**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 `200092`) : The image token index used as a placeholder for input images.

video_token_id (`int`, *optional*, defaults to `200091`) : The video token index used as a placeholder for input videos.

out_hidden_size (`int`, *optional*, defaults to 6144) : Output dimension of the vision encoder after patch merging (input width of the multimodal projection).

projector_hidden_size (`int`, *optional*, defaults to 4096) : Intermediate dimension of the multimodal projection.

projector_hidden_act (`str`, *optional*, defaults to `gelu`) : The activation function used by the multimodal projector.

This is the configuration class to store the configuration of a MuseGlimmerModel. It is used to instantiate a Muse Glimmer
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 [meta-models/Muse-Glimmer-30B](https://huggingface.co/meta-models/Muse-Glimmer-30B)

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

Example:

```python
>>> from transformers import MuseGlimmerForConditionalGeneration, MuseGlimmerConfig

>>> # Initializing an MuseGlimmer style configuration
>>> configuration = MuseGlimmerConfig()

>>> # Initializing a model from the configuration
>>> model = MuseGlimmerForConditionalGeneration(configuration)

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

## MuseGlimmerTextConfig[[transformers.MuseGlimmerTextConfig]]

#### transformers.MuseGlimmerTextConfig[[transformers.MuseGlimmerTextConfig]]

```python
transformers.MuseGlimmerTextConfig(transformers_version: str | None = None, architectures: list[str] | None = None, output_hidden_states: bool | None = False, return_dict: bool | None = True, dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None, chunk_size_feed_forward: int = 0, is_encoder_decoder: bool = False, id2label: dict[int, str] | dict[str, str] | None = None, label2id: dict[str, int] | dict[str, str] | None = None, problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None, vocab_size: int = 202048, hidden_size: int = 6656, intermediate_size: int = 19968, num_hidden_layers: int = 52, num_attention_heads: int = 32, num_key_value_heads: int = 2, head_dim: int = 128, hidden_activation: str = 'silu', max_position_embeddings: int = 131072, initializer_range: float = 0.02, rms_norm_eps: float = 1e-05, use_cache: bool = True, pad_token_id: int | None = None, eos_token_id: int | list[int] | None = 200001, bos_token_id: int | None = 200000, tie_word_embeddings: bool = False, rope_parameters: transformers.modeling_rope_utils.RopeParameters | dict | None = None, attention_bias: bool = False, attention_dropout: int | float | None = 0.0, sliding_window: int | None = 2048, layer_types: list[str] | None = None, final_logit_softcapping: float = 20.0, qk_scale_factor: float = 3.87, output_multiplier: float = 0.19611613513818404, post_norm_eps: float = 1e-08, layer_rope_theta: list[float | int] | None = None)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/configuration_muse_glimmer.py#L73)

**Parameters:**

vocab_size (`int`, *optional*, defaults to `202048`) : 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 `6656`) : Dimension of the hidden representations.

intermediate_size (`int`, *optional*, defaults to `19968`) : Dimension of the MLP representations.

num_hidden_layers (`int`, *optional*, defaults to `52`) : Number of hidden layers in the Transformer decoder.

num_attention_heads (`int`, *optional*, defaults to `32`) : Number of attention heads for each attention layer in the Transformer decoder.

num_key_value_heads (`int`, *optional*, defaults to `2`) : This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`.

head_dim (`int`, *optional*, defaults to `128`) : The attention head dimension. If None, it will default to hidden_size // num_attention_heads

hidden_activation (`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 `131072`) : 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.

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

eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `200001`) : Token id used for end-of-stream in the vocabulary.

bos_token_id (`int`, *optional*, defaults to `200000`) : Token id used for beginning-of-stream in the vocabulary.

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

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`.

attention_bias (`bool`, *optional*, defaults to `False`) : Whether to use a bias in the query, key, value and output projection layers during self-attention.

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

sliding_window (`int`, *optional*, defaults to `2048`) : Sliding window attention window size. If `None`, no sliding window is applied.

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.

final_logit_softcapping (`float`, *optional*, defaults to 20.0) : scaling factor when applying tanh softcapping on the logits.

qk_scale_factor (`float`, *optional*, defaults to 3.87) : Multiplier applied to Q after the scaleless QK-norm, on top of the standard `1/sqrt(head_dim)` attention scaling.

output_multiplier (`float`, *optional*, defaults to 0.19611613513818404) : Scale applied to logits before the final tanh softcap. Equal to `1/sqrt(hidden_size / 256)` for the released checkpoint.

post_norm_eps (`float`, *optional*, defaults to 1e-8) : Epsilon used for the post-attention and post-FFN norms (which sit between the sub-layer output and the residual).

layer_rope_theta (`list[float]`, *optional*) : Per-layer RoPE base theta; `0` disables rotary (NoPE) for that layer. Overrides the global `rope_parameters["rope_theta"]`. Defaults to the global theta everywhere except every 4th layer counted backward from the last, which is NoPE.

This is the configuration class to store the configuration of a MuseGlimmerModel. It is used to instantiate a Muse Glimmer
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 [meta-models/Muse-Glimmer-30B](https://huggingface.co/meta-models/Muse-Glimmer-30B)

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

## MuseGlimmerVisionConfig[[transformers.MuseGlimmerVisionConfig]]

#### transformers.MuseGlimmerVisionConfig[[transformers.MuseGlimmerVisionConfig]]

```python
transformers.MuseGlimmerVisionConfig(transformers_version: str | None = None, architectures: list[str] | None = None, output_hidden_states: bool | None = False, return_dict: bool | None = True, dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None, chunk_size_feed_forward: int = 0, is_encoder_decoder: bool = False, id2label: dict[int, str] | dict[str, str] | None = None, label2id: dict[str, int] | dict[str, str] | None = None, problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None, patch_size: int = 14, pos_emb_height: int = 32, pos_emb_width: int = 32, num_attention_heads: int = 16, num_hidden_layers: int = 50, hidden_size: int = 1536, intermediate_size: int = 8960, hidden_act: str = 'gelu', rope_parameters: dict | None = None, max_position_embeddings: int = 1024, patch_temporal: int = 2, merge_size: int = 2, layer_norm_eps: float = 1e-05, layer_types: list[str] | None = None)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/configuration_muse_glimmer.py#L32)

**Parameters:**

patch_size (`int`, *optional*, defaults to `14`) : The size (resolution) of each patch.

pos_emb_height (`int`, *optional*) : Initial position embedding height.

pos_emb_width (`int`, *optional*) : Initial position embedding width.

num_attention_heads (`int`, *optional*, defaults to `16`) : Number of attention heads for each attention layer in the Transformer decoder.

num_hidden_layers (`int`, *optional*, defaults to `50`) : Number of hidden layers in the Transformer decoder.

hidden_size (`int`, *optional*, defaults to `1536`) : Dimension of the hidden representations.

intermediate_size (`int`, *optional*, defaults to `8960`) : Dimension of the MLP representations.

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

rope_parameters (`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`.

max_position_embeddings (`int`, *optional*, defaults to `1024`) : The maximum sequence length that this model might ever be used with.

patch_temporal (`int`, *optional*) : The temporal patch size used to embed inputs.

merge_size (`tuple[int] | list[int]`, *optional*) : Kernel size for patch merging.

layer_norm_eps (`float`, *optional*, defaults to `1e-05`) : The epsilon used by the layer normalization layers.

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.

This is the configuration class to store the configuration of a MuseGlimmerModel. It is used to instantiate a Muse Glimmer
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 [meta-models/Muse-Glimmer-30B](https://huggingface.co/meta-models/Muse-Glimmer-30B)

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

## MuseGlimmerImageProcessor[[transformers.MuseGlimmerImageProcessor]]

#### transformers.MuseGlimmerImageProcessor[[transformers.MuseGlimmerImageProcessor]]

```python
transformers.MuseGlimmerImageProcessor(**kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/image_processing_muse_glimmer.py#L91)

**Parameters:**

do_convert_rgb (`bool`, *kwargs*, *optional*, defaults to `True`) : Whether to convert the image to RGB.

do_resize (`bool`, *kwargs*, *optional*, defaults to `True`) : Whether to resize the image.

size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`, *kwargs*, defaults to `None`) : Describes the maximum input dimensions to the model.

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

crop_size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`, *kwargs*) : Size of the output image after applying `center_crop`.

resample (`Annotated[Union[int, PILImageResampling, NoneType], None]`, *kwargs*, defaults to `Resampling.LANCZOS`) : Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`.

do_rescale (`bool`, *kwargs*, *optional*, defaults to `True`) : Whether to rescale the image.

rescale_factor (`float`, *kwargs*, *optional*, defaults to `0.00392156862745098`) : Rescale factor to rescale the image by if `do_rescale` is set to `True`.

do_normalize (`bool`, *kwargs*, *optional*, defaults to `True`) : Whether to normalize the image.

image_mean (`Union[float, list[float], tuple[float, ...]]`, *kwargs*, *optional*, defaults to `[0.5, 0.5, 0.5]`) : Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.

image_std (`Union[float, list[float], tuple[float, ...]]`, *kwargs*, *optional*, defaults to `[0.5, 0.5, 0.5]`) : Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`.

do_pad (`bool`, *kwargs*, *optional*) : Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.

pad_size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`, *kwargs*) : The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.`

do_center_crop (`bool`, *kwargs*, *optional*) : Whether to center crop the image.

data_format (`Union[str, ~image_utils.ChannelDimension]`, *kwargs*, *optional*) : Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors.

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

device (`Annotated[Union[str, torch.device, NoneType], None]`, *kwargs*) : The device to process the videos on. If unset, the device is inferred from the input videos.

return_tensors (`Annotated[str | ~utils.generic.TensorType | None, None]`, *kwargs*) : Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.

Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. --

Constructs a MuseGlimmerImageProcessor image processor.

disable_grouping (`bool`, *kwargs*, *optional*):
Whether to disable grouping of images by size to process them individually and not in batches.
If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on
empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (`int`, *kwargs*, *optional*):
The number of image tokens to be used for each image in the input.
Added for backward compatibility but this should be set as a processor attribute in future models.

#### get_number_of_image_patches[[transformers.MuseGlimmerImageProcessor.get_number_of_image_patches]]

```python
get_number_of_image_patches(height: int, width: int, images_kwargs = None)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/image_processing_muse_glimmer.py#L228)

**Parameters:**

height (`int`) : Height of the input image.

width (`int`) : Width of the input image.

images_kwargs (`dict`, *optional*) : Any kwargs to override defaults of the image processor.

**Returns:** `int`

Number of image patches per image.

A utility that returns number of image patches for a given image size.

Note: Do not remove this method! It is used by vLLM to infer the number of patches and placeholders
without an image input.

#### patchify[[transformers.MuseGlimmerImageProcessor.patchify]]

```python
patchify(images: Tensor, patch_size: int, temporal_patch_size: int)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/image_processing_muse_glimmer.py#L137)

Patchifies each image into flat layout of shape (`seq_len`, `patch_dim`) so we can concat dynamically shaped pixels.

#### preprocess[[transformers.MuseGlimmerImageProcessor.preprocess]]

```python
preprocess(images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']], **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/image_processing_muse_glimmer.py#L109)

**Parameters:**

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`.

do_convert_rgb (`bool`, *kwargs*, *optional*) : Whether to convert the image to RGB.

do_resize (`bool`, *kwargs*, *optional*) : Whether to resize the image.

size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`, *kwargs*) : Describes the maximum input dimensions to the model.

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

crop_size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`, *kwargs*) : Size of the output image after applying `center_crop`.

resample (`Annotated[Union[int, PILImageResampling, NoneType], None]`, *kwargs*) : Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`.

do_rescale (`bool`, *kwargs*, *optional*) : Whether to rescale the image.

rescale_factor (`float`, *kwargs*, *optional*) : Rescale factor to rescale the image by if `do_rescale` is set to `True`.

do_normalize (`bool`, *kwargs*, *optional*) : Whether to normalize the image.

image_mean (`Union[float, list[float], tuple[float, ...]]`, *kwargs*, *optional*) : Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.

image_std (`Union[float, list[float], tuple[float, ...]]`, *kwargs*, *optional*) : Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`.

do_pad (`bool`, *kwargs*, *optional*) : Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.

pad_size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`, *kwargs*) : The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.`

do_center_crop (`bool`, *kwargs*, *optional*) : Whether to center crop the image.

data_format (`Union[str, ~image_utils.ChannelDimension]`, *kwargs*, *optional*) : Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors.

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

device (`Annotated[Union[str, torch.device, NoneType], None]`, *kwargs*) : The device to process the videos on. If unset, the device is inferred from the input videos.

return_tensors (`Annotated[str | ~utils.generic.TensorType | None, None]`, *kwargs*) : Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

disable_grouping (`bool`, *kwargs*, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157

image_seq_length (`int`, *kwargs*, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.

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.

max_image_tokens (`int`, *kwargs*, *optional*, defaults to 4096) : The maximum number of merged image tokens produced for one image.

**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.

#### resize[[transformers.MuseGlimmerImageProcessor.resize]]

```python
resize(images: Tensor, patch_size: int, merge_size: int, max_tokens: int, resample: typing.Union[PIL.Image.Resampling, torchvision.transforms.v2.functional.InterpolationMode, int, NoneType], **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/image_processing_muse_glimmer.py#L113)

Resize dynamically based on input image aspect ratio.

## MuseGlimmerVideoProcessor[[transformers.MuseGlimmerVideoProcessor]]

#### transformers.MuseGlimmerVideoProcessor[[transformers.MuseGlimmerVideoProcessor]]

```python
transformers.MuseGlimmerVideoProcessor(**kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/video_processing_muse_glimmer.py#L93)

**Parameters:**

- ****kwargs** (`MuseGlimmerVideoProcessorInitKwargs`, *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a MuseGlimmerVideoProcessor video processor.

#### patchify[[transformers.MuseGlimmerVideoProcessor.patchify]]

```python
patchify(videos: Tensor, patch_size: int, temporal_patch_size: int)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/video_processing_muse_glimmer.py#L148)

Patchifies each video into flat layout of shape (`seq_len`, `patch_dim`) so we can concat dynamically shaped pixels.

#### resize[[transformers.MuseGlimmerVideoProcessor.resize]]

```python
resize(videos: Tensor, resample: typing.Union[PIL.Image.Resampling, torchvision.transforms.v2.functional.InterpolationMode, int, NoneType], patch_size: int, merge_size: int, max_tokens: int, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/video_processing_muse_glimmer.py#L123)

Resize dynamically based on input video aspect ratio.

#### sample_frames[[transformers.MuseGlimmerVideoProcessor.sample_frames]]

```python
sample_frames(metadata: VideoMetadata, temporal_patch_size: int | None = None, num_frames: int | None = None, fps: int | float | None = None, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/video_processing_muse_glimmer.py#L186)

**Parameters:**

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

temporal_patch_size (`int`, *optional*) : The temporal patch size of the vision encoder. Number of sampled frames will be rounded to be divisible by frame factor.

num_frames (`int`, *optional*) : Maximum number of frames to sample. Defaults to `self.num_frames`.

fps (`int` or `float`, *optional*) : Target frames to sample per second. Defaults to `self.fps`.

**Returns:** `np.ndarray`

Indices to sample video frames.

Default sampling function which uniformly samples the desired number of frames between 0 and total number of frames.
If `fps` is passed along with metadata, `fps` frames per second are sampled uniformty. Arguments `num_frames`
and `fps` are mutually exclusive.

## MuseGlimmerProcessor[[transformers.MuseGlimmerProcessor]]

#### transformers.MuseGlimmerProcessor[[transformers.MuseGlimmerProcessor]]

```python
transformers.MuseGlimmerProcessor(image_processor = None, video_processor = None, tokenizer = None, chat_template = None, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/processing_muse_glimmer.py#L22)

**Parameters:**

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

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

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

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

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

[MuseGlimmerProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerProcessor) offers all the functionalities of [MuseGlimmerImageProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerImageProcessor), [MuseGlimmerVideoProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerVideoProcessor), and `tokenizer_class`. See the
[~MuseGlimmerImageProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerImageProcessor), [~MuseGlimmerVideoProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerVideoProcessor), and `~tokenizer_class` for more information.

## MuseGlimmerPreTrainedModel[[transformers.MuseGlimmerPreTrainedModel]]

#### transformers.MuseGlimmerPreTrainedModel[[transformers.MuseGlimmerPreTrainedModel]]

```python
transformers.MuseGlimmerPreTrainedModel(config: PreTrainedConfig, *inputs, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L421)

**Parameters:**

config ([PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig)) : 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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

This model inherits from [PreTrainedModel](/docs/transformers/main/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.

## MuseGlimmerTextModel[[transformers.MuseGlimmerTextModel]]

#### transformers.MuseGlimmerTextModel[[transformers.MuseGlimmerTextModel]]

```python
transformers.MuseGlimmerTextModel(config: MuseGlimmerTextConfig)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L448)

**Parameters:**

config ([MuseGlimmerTextConfig](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerTextConfig)) : 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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare Muse Glimmer Text Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/main/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.

#### forward[[transformers.MuseGlimmerTextModel.forward]]

```python
forward(input_ids: typing.Optional[torch.LongTensor] = None, attention_mask: typing.Optional[torch.Tensor] = None, position_ids: typing.Optional[torch.LongTensor] = None, past_key_values: transformers.cache_utils.Cache | None = None, inputs_embeds: typing.Optional[torch.FloatTensor] = None, use_cache: bool | None = None, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L472)

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

**Returns:** [BaseModelOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`

A [BaseModelOutputWithPast](/docs/transformers/main/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 ([MuseGlimmerConfig](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerConfig)) and inputs.

The [MuseGlimmerTextModel](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerTextModel) 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/main/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.

## MuseGlimmerVisionModel[[transformers.MuseGlimmerVisionModel]]

#### transformers.MuseGlimmerVisionModel[[transformers.MuseGlimmerVisionModel]]

```python
transformers.MuseGlimmerVisionModel(config: MuseGlimmerVisionConfig)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L884)

#### forward[[transformers.MuseGlimmerVisionModel.forward]]

```python
forward(pixel_values: FloatTensor, grid_thw: LongTensor, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L940)

## MuseGlimmerModel[[transformers.MuseGlimmerModel]]

#### transformers.MuseGlimmerModel[[transformers.MuseGlimmerModel]]

```python
transformers.MuseGlimmerModel(config: MuseGlimmerConfig)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L998)

#### forward[[transformers.MuseGlimmerModel.forward]]

```python
forward(input_ids: typing.Optional[torch.LongTensor] = None, attention_mask: typing.Optional[torch.Tensor] = None, position_ids: typing.Optional[torch.LongTensor] = None, past_key_values: transformers.cache_utils.Cache | None = None, inputs_embeds: typing.Optional[torch.FloatTensor] = None, use_cache: bool | None = None, pixel_values: typing.Optional[torch.Tensor] = None, image_grid_thw: typing.Optional[torch.LongTensor] = None, pixel_values_videos: typing.Optional[torch.Tensor] = None, video_grid_thw: typing.Optional[torch.LongTensor] = None, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L1089)

**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/main/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/main/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/main/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/main/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 [MuseGlimmerImageProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerImageProcessor). See `MuseGlimmerImageProcessor.__call__()` for details ([MuseGlimmerProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerProcessor) uses [MuseGlimmerImageProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerImageProcessor) for processing 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.

pixel_values_videos (`torch.Tensor` 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 [MuseGlimmerVideoProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerVideoProcessor). See `MuseGlimmerVideoProcessor.__call__()` for details ([MuseGlimmerProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerProcessor) uses [MuseGlimmerVideoProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerVideoProcessor) for processing 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:** `MuseGlimmerModelOutputWithPast` or `tuple(torch.FloatTensor)`

A `MuseGlimmerModelOutputWithPast` 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 ([MuseGlimmerConfig](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerConfig)) and inputs.

The [MuseGlimmerModel](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerModel) 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/main/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.
- **image_hidden_states** (`torch.FloatTensor`, *optional*) -- A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
  image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

## MuseGlimmerForConditionalGeneration[[transformers.MuseGlimmerForConditionalGeneration]]

#### transformers.MuseGlimmerForConditionalGeneration[[transformers.MuseGlimmerForConditionalGeneration]]

```python
transformers.MuseGlimmerForConditionalGeneration(config)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L1144)

#### forward[[transformers.MuseGlimmerForConditionalGeneration.forward]]

```python
forward(input_ids: typing.Optional[torch.LongTensor] = None, pixel_values: typing.Optional[torch.FloatTensor] = None, image_grid_thw: typing.Optional[torch.LongTensor] = None, pixel_values_videos: typing.Optional[torch.FloatTensor] = None, video_grid_thw: typing.Optional[torch.LongTensor] = None, attention_mask: typing.Optional[torch.Tensor] = None, position_ids: typing.Optional[torch.LongTensor] = None, past_key_values: transformers.cache_utils.Cache | None = None, inputs_embeds: typing.Optional[torch.FloatTensor] = None, labels: typing.Optional[torch.LongTensor] = None, use_cache: bool | None = None, logits_to_keep: typing.Union[int, torch.Tensor] = 0, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/muse_glimmer/modeling_muse_glimmer.py#L1182)

**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/main/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.  [What are input IDs?](../glossary#input-ids)

pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) : The tensors corresponding to the input images. Pixel values can be obtained using [MuseGlimmerImageProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerImageProcessor). See `MuseGlimmerImageProcessor.__call__()` for details ([MuseGlimmerProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerProcessor) uses [MuseGlimmerImageProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerImageProcessor) for processing 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.

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 [MuseGlimmerVideoProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerVideoProcessor). See `MuseGlimmerVideoProcessor.__call__()` for details ([MuseGlimmerProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerProcessor) uses [MuseGlimmerVideoProcessor](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerVideoProcessor) for processing 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.

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/main/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/main/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`).

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:** `MuseGlimmerCausalLMOutputWithPast` or `tuple(torch.FloatTensor)`

A `MuseGlimmerCausalLMOutputWithPast` 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 ([MuseGlimmerConfig](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerConfig)) and inputs.

The [MuseGlimmerForConditionalGeneration](/docs/transformers/main/en/model_doc/muse_glimmer#transformers.MuseGlimmerForConditionalGeneration) 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.text_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/main/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.
- **image_hidden_states** (`torch.FloatTensor`, *optional*) -- A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
  image_hidden_states of the model produced by the vision encoder after projecting last hidden state.

Example:

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

>>> model = MuseGlimmerForConditionalGeneration.from_pretrained("moonshotai/Kimi-K2.6")
>>> processor = AutoProcessor.from_pretrained("moonshotai/Kimi-K2.6")

>>> 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)
```