## Overview
Command A Vision ([blog post](https://cohere.com/blog/command-a-vision)) is a state-of-the-art multimodal model designed to seamlessly integrate visual and textual information for a wide range of applications. By combining advanced computer vision techniques with natural language processing capabilities, Command A Vision enables users to analyze, understand, and generate insights from both visual and textual data.
The model excels at tasks including image captioning, visual question answering, document understanding, and chart understanding. This makes it a versatile tool for AI practitioners. Its ability to process complex visual and textual inputs makes it useful in settings where text-only representations are imprecise or unavailable, like real-world image understanding and graphics-heavy document processing.
Command A Vision is built upon a robust architecture that leverages the latest advancements in VLMs. It's highly performant and efficient, even when dealing with large-scale datasets. The model's flexibility makes it suitable for a wide range of use cases, from content moderation and image search to medical imaging analysis and robotics.
## Usage tips
The model and image processor can be loaded as follows:
```python
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
model_id = "CohereLabs/command-a-vision-07-2025"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
model_id, device_map="auto", dtype=torch.float16
)
# Format message with the Command-A-Vision chat template
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://images.pexels.com/photos/1108099/pexels-photo-1108099.jpeg",
},
{"type": "text", "text": "what is in this image?"},
],
},
]
inputs = processor.apply_chat_template(
messages,
padding=True,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
gen_tokens = model.generate(
**inputs,
max_new_tokens=300,
do_sample=True,
temperature=0.3,
)
print(
processor.tokenizer.decode(
gen_tokens[0][inputs.input_ids.shape[1] :], skip_special_tokens=True
)
)
```
```python
from transformers import pipeline
pipe = pipeline(model="CohereLabs/command-a-vision-07-2025", task="image-text-to-text", device_map="auto")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://media.istockphoto.com/id/458012057/photo/istanbul-turkey.jpg?s=612x612&w=0&k=20&c=qogAOVvkpfUyqLUMr_XJQyq-HkACXyYUSZbKhBlPrxo=",
},
{"type": "text", "text": "Where was this taken ?"},
],
},
]
outputs = pipe(text=messages, max_new_tokens=300, return_full_text=False)
print(outputs)
```
## Cohere2VisionConfig[[transformers.Cohere2VisionConfig]]
class transformers.Cohere2VisionConfigtransformers.Cohere2VisionConfighttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/configuration_cohere2_vision.py#L19[{"name": "vision_config", "val": " = None"}, {"name": "text_config", "val": " = None"}, {"name": "downsample_factor", "val": " = 2"}, {"name": "image_token_id", "val": " = 255036"}, {"name": "alignment_intermediate_size", "val": " = 36864"}, {"name": "**kwargs", "val": ""}]- **vision_config** (`Union[AutoConfig, dict]`, *optional*, defaults to `SiglipVisionConfig`) --
The config object or dictionary of the vision backbone.
- **text_config** (`Union[AutoConfig, dict]`, *optional*, defaults to `Cohere2Config`) --
The config object or dictionary of the text backbone.
- **downsample_factor** (`int`, *optional*, defaults to 2) --
The factor by which to downsample the input image.
- **image_token_id** (`int`, *optional*, defaults to 255036) --
The token ID to use as placeholder for the image input.
- **alignment_intermediate_size** (`int`, *optional*, defaults to 36864) --
The size of the intermediate layer for alignment.0
This is the configuration class to store the configuration of a [Cohere2VisionForConditionalGeneration](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionForConditionalGeneration). It is used to instantiate an
Cohere2 Vision model according to the specified arguments, defining the model architecture.
[CohereLabs/command-a-vision-07-2025](https://huggingface.co/CohereLabs/command-a-vision-07-2025)
Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.57.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/v4.57.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.
class transformers.Cohere2VisionForConditionalGenerationtransformers.Cohere2VisionForConditionalGenerationhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py#L268[{"name": "config", "val": ": Cohere2VisionConfig"}]- **config** ([Cohere2VisionConfig](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionConfig)) --
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/v4.57.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The COHERE2_VISION model which consists of a vision backbone and a language model.
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forwardtransformers.Cohere2VisionForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py#L309[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "cache_position", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "logits_to_keep", "val": ": typing.Union[int, torch.Tensor] = 0"}, {"name": "image_sizes", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
The tensors corresponding to the input images. Pixel values can be obtained using
`image_processor_class`. See `image_processor_class.__call__` for details ([Cohere2VisionProcessor](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionProcessor) uses
`image_processor_class` for processing images).
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Only [Cache](/docs/transformers/v4.57.0/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/v4.57.0/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`).
- **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) --
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
- **logits_to_keep** (`Union[int, torch.Tensor]`, defaults to `0`) --
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
- **image_sizes** (`torch.Tensor` of shape `(batch_size, 2)`, *optional*) --
The sizes of the images in the batch, being (height, width) for each image.0`transformers.models.cohere2_vision.modeling_cohere2_vision.Cohere2VisionCausalLMOutputWithPast` or `tuple(torch.FloatTensor)`A `transformers.models.cohere2_vision.modeling_cohere2_vision.Cohere2VisionCausalLMOutputWithPast` 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 ([Cohere2VisionConfig](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionConfig)) and inputs.
- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v4.57.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
- **hidden_states** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
- **image_hidden_states** (`torch.FloatTensor`, *optional*) -- A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
The [Cohere2VisionForConditionalGeneration](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionForConditionalGeneration) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
```python
>>> from transformers import AutoProcessor, Cohere2VisionForConditionalGeneration
>>> import torch
>>> processor = AutoProcessor.from_pretrained("CohereLabs/command-a-vision-07-2025", use_fast=True)
>>> model = Cohere2VisionForConditionalGeneration.from_pretrained("CohereLabs/command-a-vision-07-2025", device_map="auto")
>>> messages = [
... {
... "role": "user",
... "content": [
... {
... "type": "image",
... "url": "https://images.pexels.com/photos/1108099/pexels-photo-1108099.jpeg",
... },
... {"type": "text", "text": "what is in this image?"},
... ],
... },
... ]
>>> inputs = processor.apply_chat_template(
... messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt",
... ).to(model.device)
>>> gen_tokens = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.3)
>>> processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
```
class transformers.Cohere2VisionModeltransformers.Cohere2VisionModelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py#L152[{"name": "config", "val": ": Cohere2VisionConfig"}]- **config** ([Cohere2VisionConfig](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionConfig)) --
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/v4.57.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The Cohere2Vision model which consists of a vision backbone and a language model, without a language modeling head.
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forwardtransformers.Cohere2VisionModel.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py#L216[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "cache_position", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.modeling_flash_attention_utils.FlashAttentionKwargs]"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
The tensors corresponding to the input images. Pixel values can be obtained using
`image_processor_class`. See `image_processor_class.__call__` for details ([Cohere2VisionProcessor](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionProcessor) uses
`image_processor_class` for processing images).
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Only [Cache](/docs/transformers/v4.57.0/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/v4.57.0/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`).
- **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) --
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.0`transformers.models.cohere2_vision.modeling_cohere2_vision.Cohere2VisionModelOutputWithPast` or `tuple(torch.FloatTensor)`A `transformers.models.cohere2_vision.modeling_cohere2_vision.Cohere2VisionModelOutputWithPast` 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 ([Cohere2VisionConfig](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionConfig)) and inputs.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the model.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v4.57.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
- **image_hidden_states** (`torch.FloatTensor`, *optional*) -- A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
The [Cohere2VisionModel](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
```python
```
class transformers.Cohere2VisionImageProcessorFasttransformers.Cohere2VisionImageProcessorFasthttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py#L115[{"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.cohere2_vision.image_processing_cohere2_vision_fast.Cohere2VisionFastImageProcessorKwargs]"}]
Constructs a fast Cohere2 Vision image processor.
preprocesstransformers.Cohere2VisionImageProcessorFast.preprocesshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py#L133[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.cohere2_vision.image_processing_cohere2_vision_fast.Cohere2VisionFastImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]`) --
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **do_resize** (`bool`, *optional*) --
Whether to resize the image.
- **size** (`dict[str, int]`, *optional*) --
Describes the maximum input dimensions to the model.
- **default_to_square** (`bool`, *optional*) --
Whether to default to a square image when resizing, if size is an int.
- **resample** (`Union[PILImageResampling, F.InterpolationMode, NoneType]`) --
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
- **do_center_crop** (`bool`, *optional*) --
Whether to center crop the image.
- **crop_size** (`dict[str, int]`, *optional*) --
Size of the output image after applying `center_crop`.
- **do_rescale** (`bool`, *optional*) --
Whether to rescale the image.
- **rescale_factor** (`Union[int, float, NoneType]`) --
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- **do_normalize** (`bool`, *optional*) --
Whether to normalize the image.
- **image_mean** (`Union[float, list[float], NoneType]`) --
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- **image_std** (`Union[float, list[float], NoneType]`) --
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
- **do_pad** (`bool`, *optional*) --
Whether to pad the image. Padding is done either to the largest size in the batch
or to a fixed square size per image. The exact padding strategy depends on the model.
- **pad_size** (`dict[str, int]`, *optional*) --
The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch. Applied only when `do_pad=True.`
- **do_convert_rgb** (`bool`, *optional*) --
Whether to convert the image to RGB.
- **return_tensors** (`Union[str, ~utils.generic.TensorType, NoneType]`) --
Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
- **data_format** (`~image_utils.ChannelDimension`, *optional*) --
Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors.
- **input_data_format** (`Union[str, ~image_utils.ChannelDimension, NoneType]`) --
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
- **device** (`torch.device`, *optional*) --
The device to process the images on. If unset, the device is inferred from the input images.
- **disable_grouping** (`bool`, *optional*) --
Whether to disable grouping of images by size to process them individually and not in batches.
If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on
empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
- **crop_to_patches** (`bool`, *optional*, defaults to `False`) --
Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the
`preprocess` method.
- **min_patches** (`int`, *optional*, defaults to 1) --
The minimum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
set to `True`. Can be overridden by the `min_patches` parameter in the `preprocess` method.
- **max_patches** (`int`, *optional*, defaults to 12) --
The maximum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
set to `True`. Can be overridden by the `max_patches` parameter in the `preprocess` method.0``- **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/TensorFlow/Numpy Tensors at
initialization.
class transformers.Cohere2VisionProcessortransformers.Cohere2VisionProcessorhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/processing_cohere2_vision.py#L41[{"name": "image_processor", "val": " = None"}, {"name": "tokenizer", "val": " = None"}, {"name": "chat_template", "val": " = None"}, {"name": "**kwargs", "val": ""}]- **image_processor** ([AutoImageProcessor](/docs/transformers/v4.57.0/en/model_doc/auto#transformers.AutoImageProcessor), *optional*) --
The image processor is a required input.
- **tokenizer** ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], *optional*) --
The tokenizer is a required input.
- **chat_template** (`str`, *optional*) -- A Jinja template which will be used to convert lists of messages
in a chat into a tokenizable string.0
Constructs a Cohere2Vision processor which wraps a [AutoImageProcessor](/docs/transformers/v4.57.0/en/model_doc/auto#transformers.AutoImageProcessor) and
`PretrainedTokenizerFast` tokenizer into a single processor that inherits both the image processor and
tokenizer functionalities. See the `__call__()` and [decode()](/docs/transformers/v4.57.0/en/model_doc/cohere2_vision#transformers.Cohere2VisionProcessor.decode) for more information.
batch_decodetransformers.Cohere2VisionProcessor.batch_decodehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/processing_cohere2_vision.py#L195[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]
This method forwards all its arguments to PreTrainedTokenizerFast's [batch_decode()](/docs/transformers/v4.57.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.batch_decode). Please
refer to the docstring of this method for more information.
decodetransformers.Cohere2VisionProcessor.decodehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/cohere2_vision/processing_cohere2_vision.py#L202[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]
This method forwards all its arguments to PreTrainedTokenizerFast's [decode()](/docs/transformers/v4.57.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.decode). Please refer to
the docstring of this method for more information.
