# MiniCPM-V 4.7

[MiniCPM-V](https://huggingface.co/papers/2509.18154) is a series of efficient multimodal large language models developed by [OpenBMB](https://github.com/OpenBMB). Like [MiniCPM-V 4.6](./minicpmv4_6), the MiniCPM-V 4.7 architecture pairs a [SigLIP](./siglip) vision encoder that has a window-attention merger with a [Qwen3.5](./qwen3_5) language model backbone, and supports both 4x and 16x visual downsampling modes.

The main addition over 4.6 is *canvas M-RoPE*: instead of numbering visual tokens along a single 1-D sequence, the model lays every image out on a 2-D canvas and assigns each visual token a `(temporal, height, width)` position, so slices of the same image keep their spatial relationship and video frames keep their temporal order.

This model was contributed by [OpenBMB](https://huggingface.co/openbmb).
The original code can be found [here](https://github.com/OpenBMB/MiniCPM-V).

> [!NOTE]
> Passing `use_image_id` to a processor will number several images in one prompt so the text can refer to them individually. It applies to images only: a video is a single temporal sequence of frames rather than several addressable visuals, which is how the model was trained, so the setting is ignored for video inputs.

## Usage example

### Inference with Pipeline

```python
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_7")
outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
outputs[0]["generated_text"]
```

### Inference on a single image

```python
from transformers import AutoProcessor, AutoModelForImageTextToText

model_checkpoint = "openbmb/MiniCPM-V-4_7"
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 4.7 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`:

```python
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:

```python
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):

```python
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)
```

### Video inference

MiniCPM-V 4.7 supports video understanding.

```python
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)
```

## MiniCPMV4_7Config[[transformers.MiniCPMV4_7Config]]

#### transformers.MiniCPMV4_7Config[[transformers.MiniCPMV4_7Config]]

```python
transformers.MiniCPMV4_7Config(transformers_version: str | None = None, architectures: list[str] | None = None, output_hidden_states: bool | None = False, return_dict: bool | None = True, dtype: str | torch.dtype | None = 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: Literal['regression', 'single_label_classification', 'multi_label_classification'] | None = 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, 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, image_start_id: int | None = None, image_end_id: int | None = None, slice_start_id: int | None = None, slice_end_id: int | None = None, newline_id: int | None = None)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/configuration_minicpmv4_7.py#L66)

**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`) : The size (resolution) of each image.

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

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

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.

image_start_id (`int`, *optional*) : Token id of the image-start marker (`<image>`). Canvas M-RoPE pins it to the halo just outside the top-left corner of the image canvas. Resolved from the tokenizer by the conversion script and stored in `config.json`; required for image or video inputs.

image_end_id (`int`, *optional*) : Token id of the image-end marker (`</image>`). Canvas M-RoPE pins it to the far corner of the image canvas. Resolved from the tokenizer by the conversion script and stored in `config.json`; required for image or video inputs.

slice_start_id (`int`, *optional*) : Token id of the slice-start marker (`<slice>`). Canvas M-RoPE pins it to the top-left corner of the slice it opens. Resolved from the tokenizer by the conversion script and stored in `config.json`; required for image or video inputs.

slice_end_id (`int`, *optional*) : Token id of the slice-end marker (`</slice>`). Canvas M-RoPE pins it to the bottom-right corner of the slice it closes. Resolved from the tokenizer by the conversion script and stored in `config.json`; required for image or video inputs.

newline_id (`int`, *optional*) : Token id of the newline (`"\n"`) that separates slice rows. Canvas M-RoPE pins it just past the right edge of the row it ends. Resolved from the tokenizer by the conversion script and stored in `config.json`; required for image or video inputs.

This is the configuration class to store the configuration of a MiniCPMV4_7Model. It is used to instantiate a Minicpmv4 7
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-4.7](https://huggingface.co/openbmb/MiniCPM-V-4.7)

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

## MiniCPMV4_7VisionConfig[[transformers.MiniCPMV4_7VisionConfig]]

#### transformers.MiniCPMV4_7VisionConfig[[transformers.MiniCPMV4_7VisionConfig]]

```python
transformers.MiniCPMV4_7VisionConfig(transformers_version: str | None = None, architectures: list[str] | None = None, output_hidden_states: bool | None = False, return_dict: bool | None = True, dtype: str | torch.dtype | None = 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: Literal['regression', 'single_label_classification', 'multi_label_classification'] | None = 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))
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/configuration_minicpmv4_7.py#L31)

**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_7Model. It is used to instantiate a Minicpmv4 7
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-4.7](https://huggingface.co/openbmb/MiniCPM-V-4.7)

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

## MiniCPMV4_7VisionPreTrainedModel.[[transformers.MiniCPMV4_7VisionPreTrainedModel]]

#### transformers.MiniCPMV4_7VisionPreTrainedModel[[transformers.MiniCPMV4_7VisionPreTrainedModel]]

```python
transformers.MiniCPMV4_7VisionPreTrainedModel(config: PreTrainedConfig, *inputs, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L401)

#### forward[[transformers.MiniCPMV4_7VisionPreTrainedModel.forward]]

```python
forward(*args, **kwargs)
```

A mock value for a dotted path (e.g. `torch.float32`): attribute access chains,
calls behave as pass-through decorators, `repr` is the dotted path, and using it
as a base class substitutes a plain-`type` base (PEP 560 `__mro_entries__`), so
real subclasses keep a normal metaclass and `inspect.signature` reads their real
`__init__` instead of a mock's.

## MiniCPMV4_7VisionModel[[transformers.MiniCPMV4_7VisionModel]]

#### transformers.MiniCPMV4_7VisionModel[[transformers.MiniCPMV4_7VisionModel]]

```python
transformers.MiniCPMV4_7VisionModel(config: MiniCPMV4_7VisionConfig)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L415)

#### forward[[transformers.MiniCPMV4_7VisionModel.forward]]

```python
forward(pixel_values, target_sizes: typing.Optional[torch.IntTensor] = None, use_vit_merger: bool = True, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L447)

**Parameters:**

pixel_values (`` 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](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.MiniCPMV4_6ImageProcessor). See `MiniCPMV4_6ImageProcessor.__call__()` for details ([MiniCPMV4_7Processor](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Processor) uses [MiniCPMV4_6ImageProcessor](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.MiniCPMV4_6ImageProcessor) for processing images).

target_sizes (`torch.IntTensor` of shape `(batch_size, 2)`, *optional*) : Patch grid sizes `(h, w)` for computing position embeddings.

use_vit_merger (`bool`, *optional*, defaults to `True`) : Whether to apply the ViT window-attention merger after the encoder.

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

A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) and inputs.

The [MiniCPMV4_7VisionModel](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7VisionModel) 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.
- **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_7Model[[transformers.MiniCPMV4_7Model]]

#### transformers.MiniCPMV4_7Model[[transformers.MiniCPMV4_7Model]]

```python
transformers.MiniCPMV4_7Model(config: MiniCPMV4_7Config)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L598)

**Parameters:**

config ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The MiniCPMV4_7 model which consists of a vision backbone and a language model, without a language modeling head.

This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

#### forward[[transformers.MiniCPMV4_7Model.forward]]

```python
forward(input_ids: typing.Optional[torch.LongTensor] = None, pixel_values: typing.Optional[torch.FloatTensor] = None, target_sizes: typing.Optional[torch.IntTensor] = None, pixel_values_videos: typing.Optional[torch.FloatTensor] = None, target_sizes_videos: typing.Optional[torch.IntTensor] = None, attention_mask: typing.Optional[torch.Tensor] = None, position_ids: typing.Optional[torch.LongTensor] = None, past_key_values: list[torch.FloatTensor] | None = None, inputs_embeds: typing.Optional[torch.FloatTensor] = None, use_cache: bool | None = None, downsample_mode: str | None = None, mm_token_type_ids: typing.Optional[torch.IntTensor] = None, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L668)

**Parameters:**

input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.  [What are input IDs?](../glossary#input-ids)

pixel_values (`torch.FloatTensor`, *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?](../glossary#attention-mask)

position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.  [What are position IDs?](../glossary#position-ids)

past_key_values (`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](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/main/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.  The model will output the same cache format that is fed as input.  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) : Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

use_cache (`bool`, *optional*) : If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).

downsample_mode (`str`, *optional*) : `"4x"` keeps 4x more visual tokens; default `"16x"` applies full merge.

mm_token_type_ids (`torch.IntTensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of input sequence tokens matching each modality. For example text (0), image (1), video (2). Multimodal token type ids can be obtained using [AutoProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/main/en/main_classes/processors#transformers.ProcessorMixin.__call__) for details.

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

A [BaseModelOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) and inputs.

The [MiniCPMV4_7Model](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Model) forward method, overrides the `__call__` special method.

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

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

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

#### get_image_features[[transformers.MiniCPMV4_7Model.get_image_features]]

```python
get_image_features(pixel_values: FloatTensor, target_sizes: IntTensor, downsample_mode: str | None = None, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L608)

**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](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.MiniCPMV4_6ImageProcessor). See `MiniCPMV4_6ImageProcessor.__call__()` for details ([MiniCPMV4_7Processor](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Processor) uses [MiniCPMV4_6ImageProcessor](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.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](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) 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.

#### get_video_features[[transformers.MiniCPMV4_7Model.get_video_features]]

```python
get_video_features(pixel_values_videos: FloatTensor, target_sizes_videos: IntTensor, downsample_mode: str | None = None, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L750)

**Parameters:**

pixel_values_videos (`torch.FloatTensor` of shape `(1, channels, patch_size, seq_len)`) : NaViT-packed pixel patches for all video frames. The video processor concatenates every frame's patches along the last dimension into a single sequence with dim-0 = 1, identical to the image packing format.

target_sizes_videos (`torch.IntTensor` of shape `(num_patches, 2)`) : Height and width (in patches) of each visual unit.

downsample_mode (`str`, *optional*) : When set to `"4x"` the intermediate `vit_merger` is skipped so that each frame keeps `4×` more visual tokens. Default `"16x"` mode applies the full merge pipeline.

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

A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) and inputs.

Extract video features: repack frames into NaViT format, then vision encoder + 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_7ForConditionalGeneration[[transformers.MiniCPMV4_7ForConditionalGeneration]]

#### transformers.MiniCPMV4_7ForConditionalGeneration[[transformers.MiniCPMV4_7ForConditionalGeneration]]

```python
transformers.MiniCPMV4_7ForConditionalGeneration(config: MiniCPMV4_7Config)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L1201)

**Parameters:**

config ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The Minicpmv4 7 Model for token generation conditioned on other modalities (e.g. image-text-to-text generation).

This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

#### forward[[transformers.MiniCPMV4_7ForConditionalGeneration.forward]]

```python
forward(input_ids: typing.Optional[torch.LongTensor] = None, pixel_values: typing.Optional[torch.FloatTensor] = None, target_sizes: typing.Optional[torch.IntTensor] = None, pixel_values_videos: typing.Optional[torch.FloatTensor] = None, target_sizes_videos: typing.Optional[torch.IntTensor] = None, attention_mask: typing.Optional[torch.Tensor] = None, position_ids: typing.Optional[torch.LongTensor] = None, past_key_values: list[torch.FloatTensor] | None = None, inputs_embeds: typing.Optional[torch.FloatTensor] = None, labels: typing.Optional[torch.LongTensor] = None, use_cache: bool | None = None, downsample_mode: str | None = None, mm_token_type_ids: typing.Optional[torch.IntTensor] = None, logits_to_keep: typing.Union[int, torch.Tensor] = 0, **kwargs: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L1211)

**Parameters:**

input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.  [What are input IDs?](../glossary#input-ids)

pixel_values (`torch.FloatTensor`, *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?](../glossary#attention-mask)

position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.  [What are position IDs?](../glossary#position-ids)

past_key_values (`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](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/main/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.  The model will output the same cache format that is fed as input.  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) : Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

use_cache (`bool`, *optional*) : If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).

downsample_mode (`str`, *optional*) : `"4x"` keeps 4x more visual tokens; default `"16x"` applies full merge.

mm_token_type_ids (`torch.IntTensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of input sequence tokens matching each modality. For example text (0), image (1), video (2). Multimodal token type ids can be obtained using [AutoProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/main/en/main_classes/processors#transformers.ProcessorMixin.__call__) for details. 

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

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

A [CausalLMOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.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_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) and inputs.

The [MiniCPMV4_7ForConditionalGeneration](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7ForConditionalGeneration) forward method, overrides the `__call__` special method.

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

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

Example:

```python
>>> from PIL import Image
>>> from transformers import AutoProcessor, MiniCPMV4_7ForConditionalGeneration

>>> model = MiniCPMV4_7ForConditionalGeneration.from_pretrained("openbmb/MiniCPM-V-4.7")
>>> processor = AutoProcessor.from_pretrained("openbmb/MiniCPM-V-4.7")

>>> 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[[transformers.MiniCPMV4_7ForConditionalGeneration.get_image_features]]

```python
get_image_features(*args, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L1279)

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

A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) 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:

```python
>>> from PIL import Image
>>> from transformers import AutoProcessor, MiniCPMV4_7ForConditionalGeneration

>>> model = MiniCPMV4_7ForConditionalGeneration.from_pretrained("openbmb/MiniCPM-V-4.7")
>>> processor = AutoProcessor.from_pretrained("openbmb/MiniCPM-V-4.7")

>>> 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_video_features[[transformers.MiniCPMV4_7ForConditionalGeneration.get_video_features]]

```python
get_video_features(*args, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/modeling_minicpmv4_7.py#L1283)

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

A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([MiniCPMV4_7Config](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Config)) and inputs.

Extract video features: repack frames into NaViT format, then vision encoder + 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:

```python
>>> from PIL import Image
>>> from transformers import AutoProcessor, MiniCPMV4_7ForConditionalGeneration

>>> model = MiniCPMV4_7ForConditionalGeneration.from_pretrained("openbmb/MiniCPM-V-4.7")
>>> processor = AutoProcessor.from_pretrained("openbmb/MiniCPM-V-4.7")

>>> 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_7Processor[[transformers.MiniCPMV4_7Processor]]

#### transformers.MiniCPMV4_7Processor[[transformers.MiniCPMV4_7Processor]]

```python
transformers.MiniCPMV4_7Processor(image_processor = None, video_processor = None, tokenizer = None, chat_template = None, **kwargs)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/processing_minicpmv4_7.py#L46)

**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_7Processor which wraps a image processor, a video processor, and a tokenizer into a single processor.

[MiniCPMV4_7Processor](/docs/transformers/main/en/model_doc/minicpmv4_7#transformers.MiniCPMV4_7Processor) offers all the functionalities of [MiniCPMV4_6ImageProcessor](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.MiniCPMV4_6ImageProcessor), [MiniCPMV4_6VideoProcessor](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.MiniCPMV4_6VideoProcessor), and [TokenizersBackend](/docs/transformers/main/en/main_classes/tokenizer#transformers.TokenizersBackend). See the
[~MiniCPMV4_6ImageProcessor](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.MiniCPMV4_6ImageProcessor), [~MiniCPMV4_6VideoProcessor](/docs/transformers/main/en/model_doc/minicpmv4_6#transformers.MiniCPMV4_6VideoProcessor), and [~TokenizersBackend](/docs/transformers/main/en/main_classes/tokenizer#transformers.TokenizersBackend) for more information.

#### __call__[[transformers.MiniCPMV4_7Processor.__call__]]

```python
__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 = 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: Unpack)
```

[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/minicpmv4_7/processing_minicpmv4_7.py#L69)

**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](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) : If set, will return tensors of a particular framework. Acceptable values are:  - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects.

- ****kwargs** ([ProcessingKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ProcessingKwargs), *optional*) : Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.