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This model was released on 2025-09-16 and added to Hugging Face Transformers on 2026-04-28.

PyTorch SDPA FlashAttention

MiniCPM-V

MiniCPM-V is a series of efficient multimodal large language models developed by OpenBMB. The MiniCPM-V 4.6 architecture uses a SigLIP vision encoder with a window-attention merger and a Qwen3.5 language model backbone, supporting both 4x and 16x visual downsampling modes.

This model was contributed by OpenBMB. The original code can be found here.

Usage example

Inference with Pipeline

from transformers import pipeline

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    },
]

pipe = pipeline("image-text-to-text", model="openbmb/MiniCPM-V-4_6")
outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
outputs[0]["generated_text"]

Inference on a single image

The model has been trained with a specific prompt format for chatting. Use processor.apply_chat_template(my_conversation_dict) to correctly format your prompts.

from transformers import AutoProcessor, AutoModelForImageTextToText

model_checkpoint = "openbmb/MiniCPM-V-4_6"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map="auto")

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

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

output = model.generate(**inputs, max_new_tokens=100)
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(decoded_output)

Downsampling mode

MiniCPM-V 4.6 supports two visual downsampling modes:

  • 16x (default): More aggressive downsampling, fewer visual tokens, faster inference.
  • 4x: Less downsampling, more visual tokens, better for detail-rich tasks.

You can change the downsampling mode at runtime by passing downsample_mode via processor_kwargs and to model.generate:

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt",
    processor_kwargs={"downsample_mode": "4x"},
).to(model.device, dtype=model.dtype)

output = model.generate(**inputs, max_new_tokens=100, downsample_mode="4x")

Thinking mode

The model supports a thinking mode controlled by enable_thinking in the chat template. When enabled, the model generates internal reasoning before providing the final answer:

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

output = model.generate(**inputs, max_new_tokens=1024)

To disable thinking (default for evaluation):

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt",
    enable_thinking=False,
).to(model.device, dtype=model.dtype)

Image processing backend

MiniCPM-V 4.6 provides two image processing backends:

  • torchvision (default): Uses torchvision.transforms for image resizing.
  • pil: Uses PIL.Image.resize, matching the original implementation.

To use the PIL backend:

from transformers import AutoProcessor, AutoImageProcessor

processor = AutoProcessor.from_pretrained(model_checkpoint)
processor.image_processor = AutoImageProcessor.from_pretrained(model_checkpoint, backend="pil")

Video inference

MiniCPM-V 4.6 supports video understanding.

messages = [
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "path/to/video.mp4"},
            {"type": "text", "text": "Describe what happens in this video."},
        ],
    }
]

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

output = model.generate(**inputs, max_new_tokens=200)
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(decoded_output)

If you already have the rendered prompt string, you can call processor(text=..., videos=[...]) directly instead.

MiniCPMV4_6Config

class transformers.MiniCPMV4_6Config

< >

( 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 insert_layer_id: int = 6 image_size: int = 448 drop_vision_last_layer: bool = False image_token_id: int | None = None video_token_id: int | None = None tie_word_embeddings: bool = False downsample_mode: str = '16x' merge_kernel_size: tuple[int, int] | list[int] = (2, 2) merger_times: int = 1 )

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.
  • insert_layer_id (int, optional, defaults to 6) — Vision encoder layer index after which the window-attention merger is applied.
  • image_size (int, optional, defaults to 448) — Base resolution for image preprocessing.
  • drop_vision_last_layer (bool, optional, defaults to False) — Whether to drop the last layer of the vision encoder.
  • image_token_id (int, optional) — Token id used as the image placeholder.
  • video_token_id (int, optional) — Token id used as the video placeholder.
  • tie_word_embeddings (bool, optional, defaults to False) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.
  • downsample_mode (str, optional, defaults to "16x") — Visual token downsampling ratio. "4x" keeps 4× more tokens.
  • merge_kernel_size (tuple[int, int], optional, defaults to (2, 2)) — Kernel size (h, w) for merging adjacent visual patches in the Merger.
  • merger_times (int, optional, defaults to 1) — Number of iterative merge rounds in the Merger.

This is the configuration class to store the configuration of a MiniCPMV4_6Model. It is used to instantiate a Minicpmv4 6 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 openbmb/MiniCPM-V-4.6

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

MiniCPMV4_6VisionConfig

class transformers.MiniCPMV4_6VisionConfig

< >

( 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 hidden_size: int = 768 intermediate_size: int = 3072 num_hidden_layers: int = 12 num_attention_heads: int = 12 num_channels: int = 3 image_size: int | list[int] | tuple[int, int] = 224 patch_size: int | list[int] | tuple[int, int] = 16 hidden_act: str = 'gelu_pytorch_tanh' layer_norm_eps: float = 1e-06 attention_dropout: float | int = 0.0 insert_layer_id: int = 6 window_kernel_size: tuple[int, int] | list[int] = (2, 2) )

Parameters

  • hidden_size (int, optional, defaults to 768) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 3072) — Dimension of the MLP representations.
  • num_hidden_layers (int, optional, defaults to 12) — Number of hidden layers in the Transformer decoder.
  • num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer decoder.
  • num_channels (int, optional, defaults to 3) — The number of input channels.
  • image_size (Union[int, list[int], tuple[int, int]], optional, defaults to 224) — The size (resolution) of each image.
  • patch_size (Union[int, list[int], tuple[int, int]], optional, defaults to 16) — The size (resolution) of each patch.
  • hidden_act (str, optional, defaults to gelu_pytorch_tanh) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
  • layer_norm_eps (float, optional, defaults to 1e-06) — The epsilon used by the layer normalization layers.
  • attention_dropout (Union[float, int], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
  • insert_layer_id (int, optional, defaults to 6) — Vision encoder layer index after which the window-attention merger is applied.
  • window_kernel_size (tuple[int, int], optional, defaults to (2, 2)) — Window size (h, w) for the intermediate window-attention merger.

This is the configuration class to store the configuration of a MiniCPMV4_6Model. It is used to instantiate a Minicpmv4 6 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 openbmb/MiniCPM-V-4.6

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

MiniCPMV4_6Model

class transformers.MiniCPMV4_6Model

< >

( config: MiniCPMV4_6Config )

Parameters

  • config (MiniCPMV4_6Config) — 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 MiniCPMV4_6 model which consists of a vision backbone and a language model, without a language modeling head.

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: torch.LongTensor | None = None pixel_values: torch.FloatTensor | None = None target_sizes: torch.IntTensor | None = None pixel_values_videos: torch.FloatTensor | None = None target_sizes_videos: torch.IntTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: list[torch.FloatTensor] | None = None inputs_embeds: torch.FloatTensor | None = None use_cache: bool | None = None downsample_mode: str | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPast 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?

  • pixel_values (torch.FloatTensor, optional) — Pixel value patches for images, NaViT-packed.
  • target_sizes (torch.IntTensor, optional) — Height and width (in patches) for each image.
  • pixel_values_videos (torch.FloatTensor, optional) — Pixel value patches for video frames, NaViT-packed.
  • target_sizes_videos (torch.IntTensor, optional) — Height and width (in patches) for each video frame.
  • 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 (list[torch.FloatTensor], 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.
  • 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).
  • downsample_mode (str, optional) — "4x" keeps 4x more visual tokens; default "16x" applies full merge.

Returns

BaseModelOutputWithPast or tuple(torch.FloatTensor)

A 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 (MiniCPMV4_6Config) and inputs.

The MiniCPMV4_6Model 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 instance. For more details, see our kv cache guide.

    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.

get_image_features

< >

( pixel_values: FloatTensor target_sizes: IntTensor downsample_mode: str | None = None ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using MiniCPMV4_6ImageProcessor. See MiniCPMV4_6ImageProcessor.__call__() for details (MiniCPMV4_6Processor uses MiniCPMV4_6ImageProcessor for processing images).
  • target_sizes (torch.IntTensor of shape (num_images, 2)) — Height and width (in patches) of each image.
  • downsample_mode (str, optional) — When set to "4x" the intermediate vit_merger is skipped so that each image keeps 4× more visual tokens. Default "16x" mode applies the full merge pipeline.

Returns

BaseModelOutputWithPooling or tuple(torch.FloatTensor)

A BaseModelOutputWithPooling or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MiniCPMV4_6Config) and inputs.

Extract image features: vision encoder, insert merger, then MLP merger.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

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

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

MiniCPMV4_6ForConditionalGeneration

class transformers.MiniCPMV4_6ForConditionalGeneration

< >

( config: MiniCPMV4_6Config )

forward

< >

( input_ids: torch.LongTensor | None = None pixel_values: torch.FloatTensor | None = None target_sizes: torch.IntTensor | None = None pixel_values_videos: torch.FloatTensor | None = None target_sizes_videos: torch.IntTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: list[torch.FloatTensor] | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None use_cache: bool | None = None downsample_mode: str | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → CausalLMOutputWithPast 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?

  • pixel_values (torch.FloatTensor, optional) — Pixel value patches for images, NaViT-packed.
  • target_sizes (torch.IntTensor, optional) — Height and width (in patches) for each image.
  • pixel_values_videos (torch.FloatTensor, optional) — Pixel value patches for video frames, NaViT-packed.
  • target_sizes_videos (torch.IntTensor, optional) — Height and width (in patches) for each video frame.
  • 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 (list[torch.FloatTensor], 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].
  • 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).
  • downsample_mode (str, optional) — "4x" keeps 4x more visual tokens; default "16x" applies full merge.

Returns

CausalLMOutputWithPast or tuple(torch.FloatTensor)

A CausalLMOutputWithPast 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 (MiniCPMV4_6Config) and inputs.

The MiniCPMV4_6ForConditionalGeneration forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache 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.

Example:

>>> from PIL import Image
>>> from transformers import AutoProcessor, MiniCPMV4_6ForConditionalGeneration

>>> model = MiniCPMV4_6ForConditionalGeneration.from_pretrained("openbmb/MiniCPM-V-4.6")
>>> processor = AutoProcessor.from_pretrained("openbmb/MiniCPM-V-4.6")

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

>>> inputs = processor.apply_chat_template(
...     messages,
...     tokenize=True,
...     return_dict=True,
...     return_tensors="pt",
...     add_generation_prompt=True
... )
>>> # Generate
>>> generate_ids = model.generate(**inputs)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]

get_image_features

< >

( *args **kwargs ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Returns

BaseModelOutputWithPooling or tuple(torch.FloatTensor)

A BaseModelOutputWithPooling or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MiniCPMV4_6Config) and inputs.

Extract image features: vision encoder, insert merger, then MLP merger.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

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

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from PIL import Image
>>> from transformers import AutoProcessor, MiniCPMV4_6ForConditionalGeneration

>>> model = MiniCPMV4_6ForConditionalGeneration.from_pretrained("openbmb/MiniCPM-V-4.6")
>>> processor = AutoProcessor.from_pretrained("openbmb/MiniCPM-V-4.6")

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

>>> inputs = processor.apply_chat_template(
...     messages,
...     tokenize=True,
...     return_dict=True,
...     return_tensors="pt",
...     add_generation_prompt=True
... )
>>> # Generate
>>> generate_ids = model.generate(**inputs)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]

MiniCPMV4_6Processor

class transformers.MiniCPMV4_6Processor

< >

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

Parameters

  • image_processor (MiniCPMV4_6ImageProcessor) — The image processor is a required input.
  • video_processor (MiniCPMV4_6VideoProcessor) — The video processor is a required input.
  • tokenizer (TokenizersBackend) — 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 MiniCPMV4_6Processor which wraps a image processor, a video processor, and a tokenizer into a single processor.

MiniCPMV4_6Processor offers all the functionalities of MiniCPMV4_6ImageProcessor, MiniCPMV4_6VideoProcessor, and TokenizersBackend. See the ~MiniCPMV4_6ImageProcessor, ~MiniCPMV4_6VideoProcessor, and ~TokenizersBackend for more information.

__call__

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None text: str | list[str] | list[list[str]] = None 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]], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.models.minicpmv4_6.processing_minicpmv4_6.MiniCPMV4_6ProcessorKwargs] ) → BatchFeature

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
  • text (Union[str, list[str], list[list[str]]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set is_split_into_words=True to avoid ambiguity with batched inputs.
  • videos (Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]], optional) — Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set do_rescale=False.
  • return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:

    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.
  • **kwargs (ProcessingKwargs, optional) — Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

BatchFeature

A BatchFeature with the following fields:

  • input_ids — Token ids to be fed to a model.
  • attention_mask — Mask indicating which tokens should be attended to.
  • pixel_values — Processed image patches to be fed to a model.
  • target_sizes — Patch grid sizes for the vision encoder.

MiniCPMV4_6ImageProcessor

class transformers.MiniCPMV4_6ImageProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.models.minicpmv4_6.image_processing_minicpmv4_6.MiniCPMV4_6ImageProcessorKwargs] )

Parameters

  • max_slice_nums (int, kwargs, optional, defaults to 9) — Maximum number of slices when splitting a high-resolution image.
  • scale_resolution (int, kwargs, optional, defaults to 448) — Target resolution for individual slices.
  • patch_size (int, kwargs, optional, defaults to 14) — Spatial patch size of the vision encoder.
  • slice_mode (bool, kwargs, optional, defaults to True) — Whether to split images into multiple slices for higher resolution.
  • downsample_mode (str, kwargs, optional, defaults to “16x”) — Visual token downsampling mode. “16x” applies full merge; “4x” keeps 4x more tokens.
  • use_image_id (bool, kwargs, optional, defaults to True) — Whether to prepend an image-id tag (<image_id>N</image_id>) before each image placeholder. Consumed by the Processor for placeholder generation, not by the image processing pipeline itself.
  • **kwargs ([ImagesKwargs], optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a MiniCPMV4_6ImageProcessor 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.minicpmv4_6.image_processing_minicpmv4_6.MiniCPMV4_6ImageProcessorKwargs] ) → ~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.
  • max_slice_nums (int, kwargs, optional, defaults to 9) — Maximum number of slices when splitting a high-resolution image.
  • scale_resolution (int, kwargs, optional, defaults to 448) — Target resolution for individual slices.
  • patch_size (int, kwargs, optional, defaults to 14) — Spatial patch size of the vision encoder.
  • slice_mode (bool, kwargs, optional, defaults to True) — Whether to split images into multiple slices for higher resolution.
  • downsample_mode (str, kwargs, optional, defaults to “16x”) — Visual token downsampling mode. “16x” applies full merge; “4x” keeps 4x more tokens.
  • use_image_id (bool, kwargs, optional, defaults to True) — Whether to prepend an image-id tag (<image_id>N</image_id>) before each image placeholder. Consumed by the Processor for placeholder generation, not by the image processing pipeline itself.
  • return_tensors (str or [~utils.TensorType], optional) — Returns stacked tensors if set to ‘pt’, otherwise returns a list of tensors.
  • **kwargs ([ImagesKwargs], optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

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

MiniCPMV4_6ImageProcessorPil

class transformers.MiniCPMV4_6ImageProcessorPil

< >

( **kwargs: typing_extensions.Unpack[transformers.models.minicpmv4_6.image_processing_pil_minicpmv4_6.MiniCPMV4_6ImageProcessorPilKwargs] )

Parameters

  • max_slice_nums (int, kwargs, optional, defaults to 9) — Maximum number of slices when splitting a high-resolution image.
  • scale_resolution (int, kwargs, optional, defaults to 448) — Target resolution for individual slices.
  • patch_size (int, kwargs, optional, defaults to 14) — Spatial patch size of the vision encoder.
  • slice_mode (bool, kwargs, optional, defaults to True) — Whether to split images into multiple slices for higher resolution.
  • downsample_mode (str, kwargs, optional, defaults to “16x”) — Visual token downsampling mode. “16x” applies full merge; “4x” keeps 4x more tokens.
  • use_image_id (bool, kwargs, optional, defaults to True) — Whether to prepend an image-id tag (<image_id>N</image_id>) before each image placeholder. Consumed by the Processor for placeholder generation, not by the image processing pipeline itself.
  • **kwargs ([MiniCPMV4_6ImageProcessorPilKwargs], optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a MiniCPMV4_6ImageProcessor 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.minicpmv4_6.image_processing_pil_minicpmv4_6.MiniCPMV4_6ImageProcessorPilKwargs] ) → ~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.
  • max_slice_nums (int, kwargs, optional, defaults to 9) — Maximum number of slices when splitting a high-resolution image.
  • scale_resolution (int, kwargs, optional, defaults to 448) — Target resolution for individual slices.
  • patch_size (int, kwargs, optional, defaults to 14) — Spatial patch size of the vision encoder.
  • slice_mode (bool, kwargs, optional, defaults to True) — Whether to split images into multiple slices for higher resolution.
  • downsample_mode (str, kwargs, optional, defaults to “16x”) — Visual token downsampling mode. “16x” applies full merge; “4x” keeps 4x more tokens.
  • use_image_id (bool, kwargs, optional, defaults to True) — Whether to prepend an image-id tag (<image_id>N</image_id>) before each image placeholder. Consumed by the Processor for placeholder generation, not by the image processing pipeline itself.
  • return_tensors (str or [~utils.TensorType], optional) — Returns stacked tensors if set to ‘pt’, otherwise returns a list of tensors.
  • **kwargs ([MiniCPMV4_6ImageProcessorPilKwargs], optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

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

MiniCPMV4_6VideoProcessor

class transformers.MiniCPMV4_6VideoProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.models.minicpmv4_6.video_processing_minicpmv4_6.MiniCPMV4_6VideoProcessorKwargs] )

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.

Constructs a MiniCPM-V 4.6 video processor.

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.
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