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GLM-4.6V

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GLM-4.6V

Glm46VConfig

class transformers.Glm46VConfig

< >

( text_config = None vision_config = None image_token_id = 151343 video_token_id = 151344 image_start_token_id = 151339 image_end_token_id = 151340 video_start_token_id = 151361 video_end_token_id = 151362 **kwargs )

Parameters

  • text_config (Union[PreTrainedConfig, dict], optional, defaults to Glm4vTextConfig) — The config object or dictionary of the text backbone.
  • vision_config (Union[PreTrainedConfig, dict], optional, defaults to Glm4vVisionConfig) — The config object or dictionary of the vision backbone.
  • image_token_id (int, optional, defaults to 151343) — The image token index to encode the image prompt.
  • video_token_id (int, optional, defaults to 151344) — The video token index to encode the image prompt.
  • image_start_token_id (int, optional, defaults to 151339) — The image start token index to encode the start of image.
  • image_end_token_id (int, optional, defaults to 151340) — The image end token index to encode the end of image.
  • video_start_token_id (int, optional, defaults to 151361) — The video start token index to encode the start of video.
  • video_end_token_id (int, optional, defaults to 151362) — The video end token index to encode the end of video.

This is the configuration class to store the configuration of a Glm4vModel. It is used to instantiate a GLM-4.6V model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.1V-9B-Thinking zai-org/GLM-4.1V-9B-Thinking.

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

>>> from transformers import Glm46VForConditionalGeneration, Glm46VConfig

>>> # Initializing a GLM-4.6V style configuration
>>> configuration = Glm46VConfig()

>>> # Initializing a model from the GLM-4.6V style configuration
>>> model = Glm4vForConditionalGeneration(configuration)

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

Glm46VImageProcessor

class transformers.Glm46VImageProcessor

< >

( do_resize: bool = True size: typing.Optional[dict[str, int]] = None resample: Resampling = <Resampling.BICUBIC: 3> do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, list[float], NoneType] = None image_std: typing.Union[float, list[float], NoneType] = None do_convert_rgb: bool = True patch_size: int = 14 temporal_patch_size: int = 2 merge_size: int = 2 **kwargs )

Parameters

  • do_resize (bool, optional, defaults to True) — Whether to resize the image’s (height, width) dimensions.
  • size (Dict[str, int] optional, defaults to {"shortest_edge" -- 112 * 112, "longest_edge": 28 * 28 * 15000}): Size of the image’s (height, width) dimensions after resizing. Can be overridden by the size parameter in the preprocess method. Available options are:
    • {"height": int, "width": int}: The image will be resized to the exact size (height, width). Do NOT keep the aspect ratio.
    • {"shortest_edge": int, "longest_edge": int}: The image will be resized to a maximum size respecting the aspect ratio and keeping the shortest edge less or equal to shortest_edge and the longest edge less or equal to longest_edge.
    • {"max_height": int, "max_width": int}: The image will be resized to the maximum size respecting the aspect ratio and keeping the height less or equal to max_height and the width less or equal to max_width.
  • resample (PILImageResampling, optional, defaults to Resampling.BICUBIC) — Resampling filter to use when resizing the image.
  • do_rescale (bool, optional, defaults to True) — Whether to rescale the image by the specified scale rescale_factor.
  • rescale_factor (int or float, optional, defaults to 1/255) — Scale factor to use if rescaling the image.
  • do_normalize (bool, optional, defaults to True) — Whether to normalize the image.
  • image_mean (float or List[float], optional, defaults to [0.48145466, 0.4578275, 0.40821073]) — Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
  • image_std (float or List[float], optional, defaults to [0.26862954, 0.26130258, 0.27577711]) — Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
  • do_convert_rgb (bool, optional, defaults to True) — Whether to convert the image to RGB.
  • patch_size (int, optional, defaults to 14) — The spatial 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.

Constructs a GLM-4V image processor that dynamically resizes images based on the original images.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] do_resize: typing.Optional[bool] = None size: typing.Optional[dict[str, int]] = None resample: typing.Optional[PIL.Image.Resampling] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, list[float], NoneType] = None image_std: typing.Union[float, list[float], NoneType] = None patch_size: typing.Optional[int] = None temporal_patch_size: typing.Optional[int] = None merge_size: typing.Optional[int] = None do_convert_rgb: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None )

Parameters

  • images (ImageInput) — 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_resize (bool, optional, defaults to self.do_resize) — Whether to resize the image.
  • size (Dict[str, int], optional, defaults to self.size) — Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio.
  • resample (int, optional, defaults to self.resample) — 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, optional, defaults to self.do_rescale) — Whether to rescale the image.
  • rescale_factor (float, optional, defaults to self.rescale_factor) — Rescale factor to rescale the image by if do_rescale is set to True.
  • do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the image.
  • image_mean (float or List[float], optional, defaults to self.image_mean) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
  • image_std (float or List[float], optional, defaults to self.image_std) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True. The max pixels of the image to resize the image.
  • patch_size (int, optional, defaults to self.patch_size) — The spatial patch size of the vision encoder.
  • temporal_patch_size (int, optional, defaults to self.temporal_patch_size) — The temporal patch size of the vision encoder.
  • merge_size (int, optional, defaults to self.merge_size) — The merge size of the vision encoder to llm encoder.
  • do_convert_rgb (bool, optional, defaults to self.do_convert_rgb) — Whether to convert the image to RGB.
  • return_tensors (str or TensorType, optional) — The type of tensors to return. Can be one of:
    • Unset: Return a list of np.ndarray.
    • TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
    • TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
  • data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) — The channel dimension format for the output 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.
    • Unset: Use the channel dimension format of the input image.
  • input_data_format (ChannelDimension or str, 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.

Glm46VVideoProcessor

class transformers.Glm46VVideoProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.models.glm46v.video_processing_glm46v.Glm46VVideoProcessorInitKwargs] )

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.
  • 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.

Constructs a fast GLM-4V image processor that dynamically resizes videos based on the original videos.

preprocess

< >

( videos: 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]]] **kwargs: typing_extensions.Unpack[transformers.processing_utils.VideosKwargs] )

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.

Glm46VImageProcessorFast

class transformers.Glm46VImageProcessorFast

< >

( **kwargs: typing_extensions.Unpack[transformers.models.glm46v.image_processing_glm46v.Glm46VImageProcessorKwargs] )

Constructs a fast Glm46V image processor.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] **kwargs: typing_extensions.Unpack[transformers.models.glm46v.image_processing_glm46v.Glm46VImageProcessorKwargs] ) → <class 'transformers.image_processing_base.BatchFeature'>

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, optional) — Whether to convert the image to RGB.
  • do_resize (bool, optional) — Whether to resize the image.
  • size (Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]) — Describes the maximum input dimensions to the model.
  • crop_size (Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]) — Size of the output image after applying center_crop.
  • resample (Annotated[Union[PILImageResampling, int, NoneType], None]) — 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, optional) — Whether to rescale the image.
  • rescale_factor (float, optional) — Rescale factor to rescale the image by if do_rescale is set to True.
  • do_normalize (bool, optional) — Whether to normalize the image.
  • image_mean (Union[float, list[float], tuple[float, ...], NoneType]) — 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, ...], NoneType]) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
  • do_pad (bool, 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[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]) — 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, optional) — Whether to center crop the image.
  • data_format (Union[~image_utils.ChannelDimension, str, NoneType]) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
  • input_data_format (Union[~image_utils.ChannelDimension, str, NoneType]) — 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[str, None], optional) — The device to process the images on. If unset, the device is inferred from the input images.
  • return_tensors (Annotated[Union[str, ~utils.generic.TensorType, NoneType], None]) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
  • disable_grouping (bool, 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, 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, optional, defaults to 14) — The spatial 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.

Returns

<class 'transformers.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.

Glm46VProcessor

class transformers.Glm46VProcessor

< >

( image_processor = None tokenizer = None video_processor = None chat_template = None **kwargs )

Parameters

  • image_processor (Glm46VProcessor, optional) — The image processor is a required input.
  • tokenizer (PreTrainedTokenizerFast, optional) — The tokenizer is a required input.
  • video_processor (Glm46VVideoProcessor, optional) — The video processor is a required input.
  • chat_template (str, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.

Constructs a GLM-4V processor which wraps a GLM-4V image processor and a GLM-4 tokenizer into a single processor. __call__() and decode() for more information.

post_process_image_text_to_text

< >

( generated_outputs skip_special_tokens = True clean_up_tokenization_spaces = False **kwargs ) → list[str]

Parameters

  • generated_outputs (torch.Tensor or np.ndarray) — The output of the model generate function. The output is expected to be a tensor of shape (batch_size, sequence_length) or (sequence_length,).
  • skip_special_tokens (bool, optional, defaults to True) — Whether or not to remove special tokens in the output. Argument passed to the tokenizer’s batch_decode method.
  • clean_up_tokenization_spaces (bool, optional, defaults to False) — Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer’s batch_decode method.
  • **kwargs — Additional arguments to be passed to the tokenizer’s batch_decode method.

Returns

list[str]

The decoded text.

Post-process the output of the model to decode the text.

Glm46VModel

class transformers.Glm46VModel

< >

( config )

Parameters

  • config (Glm46VModel) — 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() method to load the model weights.

The bare Glm46V Model outputting raw hidden-states without any specific head on top.

This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

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: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None pixel_values: typing.Optional[torch.Tensor] = None pixel_values_videos: typing.Optional[torch.FloatTensor] = None image_grid_thw: typing.Optional[torch.LongTensor] = None video_grid_thw: typing.Optional[torch.LongTensor] = None rope_deltas: typing.Optional[torch.LongTensor] = None cache_position: typing.Optional[torch.LongTensor] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.glm46v.modeling_glm46v.Glm46VModelOutputWithPast or tuple(torch.FloatTensor)

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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are 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?

  • 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?

  • 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 instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using image_processor_class. See image_processor_class.__call__ for details (processor_class uses image_processor_class for processing images).
  • pixel_values_videos (torch.FloatTensor of shape (batch_size, num_frames, num_channels, frame_size, frame_size), optional) — The tensors corresponding to the input video. Pixel values for videos can be obtained using video_processor_class. See video_processor_class.__call__ for details (processor_class uses video_processor_class for processing videos).
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • video_grid_thw (torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM.
  • rope_deltas (torch.LongTensor of shape (batch_size, ), optional) — The rope index difference between sequence length and multimodal rope.
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

transformers.models.glm46v.modeling_glm46v.Glm46VModelOutputWithPast or tuple(torch.FloatTensor)

A transformers.models.glm46v.modeling_glm46v.Glm46VModelOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (None) and inputs.

  • 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 instance. For more details, see our kv cache guide.

    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.

The Glm46VModel 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.

Glm46VForConditionalGeneration

class transformers.Glm46VForConditionalGeneration

< >

( config )

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: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.Tensor] = None pixel_values_videos: typing.Optional[torch.FloatTensor] = None image_grid_thw: typing.Optional[torch.LongTensor] = None video_grid_thw: typing.Optional[torch.LongTensor] = None cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.glm46v.modeling_glm46v.Glm46VCausalLMOutputWithPast or tuple(torch.FloatTensor)

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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are 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?

  • 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?

  • 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 instance is allowed as input, see our kv cache guide. If no past_key_values are passed, 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].
  • 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 Glm46VImageProcessor. See Glm46VImageProcessor.call() for details (Glm46VProcessor uses Glm46VImageProcessor 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 Glm46VVideoProcessor. See Glm46VVideoProcessor.__call__() for details (Glm46VProcessor uses Glm46VVideoProcessor 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.
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.
  • logits_to_keep (Union[int, torch.Tensor], 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

transformers.models.glm46v.modeling_glm46v.Glm46VCausalLMOutputWithPast or tuple(torch.FloatTensor)

A transformers.models.glm46v.modeling_glm46v.Glm46VCausalLMOutputWithPast 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 (Glm46VConfig) and inputs.

  • 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 instance. For more details, see our kv cache guide.

    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.

The Glm46VForConditionalGeneration 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.

Example:

>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Glm46VForConditionalGeneration

>>> model = Glm46VForConditionalGeneration.from_pretrained("THUDM/GLM-4.1V-9B-Thinking")
>>> processor = AutoProcessor.from_pretrained("THUDM/GLM-4.1V-9B-Thinking")

>>> messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What is shown in this image?"},
        ],
    },
]
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
>>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])

>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
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