diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md
index ddab7ad5d97a..ea363315428f 100644
--- a/docs/models/supported_models.md
+++ b/docs/models/supported_models.md
@@ -331,7 +331,7 @@ th {
| `BloomForCausalLM` | BLOOM, BLOOMZ, BLOOMChat | `bigscience/bloom`, `bigscience/bloomz`, etc. | | ✅︎ | |
| `BartForConditionalGeneration` | BART | `facebook/bart-base`, `facebook/bart-large-cnn`, etc. | | | |
| `ChatGLMModel`, `ChatGLMForConditionalGeneration` | ChatGLM | `zai-org/chatglm2-6b`, `zai-org/chatglm3-6b`, `ShieldLM-6B-chatglm3`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `CohereForCausalLM`, `Cohere2ForCausalLM` | Command-R | `CohereForAI/c4ai-command-r-v01`, `CohereForAI/c4ai-command-r7b-12-2024`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `CohereForCausalLM`, `Cohere2ForCausalLM` | Command-R | `CohereLabs/c4ai-command-r-v01`, `CohereLabs/c4ai-command-r7b-12-2024`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `DbrxForCausalLM` | DBRX | `databricks/dbrx-base`, `databricks/dbrx-instruct`, etc. | | ✅︎ | ✅︎ |
| `DeciLMForCausalLM` | DeciLM | `nvidia/Llama-3_3-Nemotron-Super-49B-v1`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `DeepseekForCausalLM` | DeepSeek | `deepseek-ai/deepseek-llm-67b-base`, `deepseek-ai/deepseek-llm-7b-chat`, etc. | | ✅︎ | ✅︎ |
@@ -601,6 +601,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `AyaVisionForConditionalGeneration` | Aya Vision | T + I+ | `CohereForAI/aya-vision-8b`, `CohereForAI/aya-vision-32b`, etc. | | ✅︎ | ✅︎ |
| `Blip2ForConditionalGeneration` | BLIP-2 | T + IE | `Salesforce/blip2-opt-2.7b`, `Salesforce/blip2-opt-6.7b`, etc. | | ✅︎ | ✅︎ |
| `ChameleonForConditionalGeneration` | Chameleon | T + I | `facebook/chameleon-7b`, etc. | | ✅︎ | ✅︎ |
+| `Cohere2VisionForConditionalGeneration` | Command A Vision | T + I+ | `CohereLabs/command-a-vision-07-2025`, etc. | | ✅︎ | ✅︎ |
| `DeepseekVLV2ForCausalLM`^ | DeepSeek-VL2 | T + I+ | `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2`, etc. | | ✅︎ | ✅︎ |
| `Florence2ForConditionalGeneration` | Florence-2 | T + I | `microsoft/Florence-2-base`, `microsoft/Florence-2-large`, etc. | | | |
| `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ | ✅︎ |
diff --git a/examples/offline_inference/vision_language.py b/examples/offline_inference/vision_language.py
index 5b3f0d2dc244..988ad35cdd7e 100644
--- a/examples/offline_inference/vision_language.py
+++ b/examples/offline_inference/vision_language.py
@@ -126,6 +126,29 @@ def run_chameleon(questions: list[str], modality: str) -> ModelRequestData:
)
+def run_command_a_vision(questions: list[str], modality: str) -> ModelRequestData:
+ assert modality == "image"
+
+ model_name = "CohereLabs/command-a-vision-07-2025"
+
+ engine_args = EngineArgs(
+ model=model_name,
+ max_model_len=32768,
+ tensor_parallel_size=4,
+ limit_mm_per_prompt={modality: 1},
+ )
+
+ prompts = [
+ f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|><|IMG_PATCH|>{question}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+ for question in questions
+ ]
+
+ return ModelRequestData(
+ engine_args=engine_args,
+ prompts=prompts,
+ )
+
+
# Deepseek-VL2
def run_deepseek_vl2(questions: list[str], modality: str) -> ModelRequestData:
assert modality == "image"
@@ -1417,6 +1440,7 @@ model_example_map = {
"aya_vision": run_aya_vision,
"blip-2": run_blip2,
"chameleon": run_chameleon,
+ "command_a_vision": run_command_a_vision,
"deepseek_vl_v2": run_deepseek_vl2,
"florence2": run_florence2,
"fuyu": run_fuyu,
diff --git a/examples/offline_inference/vision_language_multi_image.py b/examples/offline_inference/vision_language_multi_image.py
index 1ab405fa14f3..799337ed6850 100644
--- a/examples/offline_inference/vision_language_multi_image.py
+++ b/examples/offline_inference/vision_language_multi_image.py
@@ -107,6 +107,42 @@ def load_aya_vision(question: str, image_urls: list[str]) -> ModelRequestData:
)
+def load_command_a_vision(question: str, image_urls: list[str]) -> ModelRequestData:
+ model_name = "CohereLabs/command-a-vision-07-2025"
+
+ # NOTE: This model is 122B parameters and requires tensor parallelism
+ # Recommended to use tp=4 on H100 GPUs
+ engine_args = EngineArgs(
+ model=model_name,
+ max_model_len=32768,
+ tensor_parallel_size=4,
+ limit_mm_per_prompt={"image": len(image_urls)},
+ )
+
+ placeholders = [{"type": "image", "image": url} for url in image_urls]
+ messages = [
+ {
+ "role": "user",
+ "content": [
+ *placeholders,
+ {"type": "text", "text": question},
+ ],
+ }
+ ]
+
+ processor = AutoProcessor.from_pretrained(model_name)
+
+ prompt = processor.apply_chat_template(
+ messages, tokenize=False, add_generation_prompt=True
+ )
+
+ return ModelRequestData(
+ engine_args=engine_args,
+ prompt=prompt,
+ image_data=[fetch_image(url) for url in image_urls],
+ )
+
+
def load_deepseek_vl2(question: str, image_urls: list[str]) -> ModelRequestData:
model_name = "deepseek-ai/deepseek-vl2-tiny"
@@ -1031,6 +1067,7 @@ def load_tarsier2(question: str, image_urls: list[str]) -> ModelRequestData:
model_example_map = {
"aria": load_aria,
"aya_vision": load_aya_vision,
+ "command_a_vision": load_command_a_vision,
"deepseek_vl_v2": load_deepseek_vl2,
"gemma3": load_gemma3,
"h2ovl_chat": load_h2ovl,
diff --git a/tests/models/registry.py b/tests/models/registry.py
index c5816df25b96..eae582903082 100644
--- a/tests/models/registry.py
+++ b/tests/models/registry.py
@@ -383,6 +383,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
"Blip2ForConditionalGeneration": _HfExamplesInfo("Salesforce/blip2-opt-2.7b", # noqa: E501
extras={"6b": "Salesforce/blip2-opt-6.7b"}), # noqa: E501
"ChameleonForConditionalGeneration": _HfExamplesInfo("facebook/chameleon-7b"), # noqa: E501
+ "Cohere2VisionForConditionalGeneration": _HfExamplesInfo("CohereLabs/command-a-vision-07-2025"), # noqa: E501
"DeepseekVLV2ForCausalLM": _HfExamplesInfo("deepseek-ai/deepseek-vl2-tiny", # noqa: E501
extras={"fork": "Isotr0py/deepseek-vl2-tiny"}, # noqa: E501
max_transformers_version="4.48", # noqa: E501
diff --git a/vllm/model_executor/models/cohere2_vision.py b/vllm/model_executor/models/cohere2_vision.py
new file mode 100644
index 000000000000..f17583768f79
--- /dev/null
+++ b/vllm/model_executor/models/cohere2_vision.py
@@ -0,0 +1,445 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from vllm/model_executor/models/aya_vision.py
+"""Command-A-Vision (Cohere2Vision) multimodal model implementation for vLLM."""
+
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Literal, Optional, Union
+
+import torch
+from torch import nn
+from transformers import BatchFeature, PretrainedConfig
+from transformers.models.cohere2_vision import Cohere2VisionConfig
+from transformers.models.cohere2_vision.processing_cohere2_vision import (
+ Cohere2VisionProcessor)
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.activation import MulAndSilu
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+ RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalDataDict, MultiModalKwargs
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+ MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+ BaseProcessingInfo,
+ MultiModalFieldConfig,
+ PromptReplacement, PromptUpdate,
+ PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+ init_vllm_registered_model, maybe_prefix,
+ merge_multimodal_embeddings)
+
+
+class Cohere2VisionImagePixelInputs(TensorSchema):
+ """
+ Dimensions:
+ - np: The total number of patches over each image over each prompt in
+ the batch
+ - c: Number of channels
+ - h: Height of each image patch
+ - w: Width of each image patch
+ - bn: Batch size * number of images
+ """
+
+ type: Literal["pixel_values"]
+
+ pixel_values: Annotated[
+ torch.Tensor,
+ TensorShape("np", 3, "h", "w"),
+ ]
+
+ num_patches: Annotated[
+ torch.Tensor,
+ TensorShape("bn"),
+ ]
+
+
+class Cohere2VisionMultiModalProjector(nn.Module):
+ """Multimodal projector that maps vision features to text embedding space.
+
+ Uses pixel shuffle downsampling followed by SwiGLU activation.
+ """
+
+ def __init__(self, config: Cohere2VisionConfig, prefix: str = ""):
+ super().__init__()
+ self.downsample_factor = config.downsample_factor
+
+ # Input dimension after pixel shuffle downsampling
+ input_dim = config.vision_config.hidden_size * (
+ config.downsample_factor**2)
+ # MergedColumnParallelLinear expects the intermediate size to be a list
+ # of sizes, so that it will load the weights as two separate linear
+ # layers before applying any parallelism.
+ # We need to divide the alignment intermediate size by 2 because
+ # the weights are merged weights of two linear layers for SwiGLU.
+ self.intermediate_size = config.alignment_intermediate_size // 2
+
+ self.linear_1 = MergedColumnParallelLinear(
+ input_dim,
+ [self.intermediate_size] * 2,
+ bias=True,
+ return_bias=False,
+ prefix=f"{prefix}.linear_1",
+ )
+ self.act = MulAndSilu()
+ self.linear_2 = RowParallelLinear(
+ self.intermediate_size,
+ config.text_config.hidden_size,
+ bias=True,
+ return_bias=False,
+ prefix=f"{prefix}.linear_2",
+ )
+
+ def forward(self, image_features):
+ image_features = self.pixel_shuffle(image_features)
+ hidden_states = self.linear_1(image_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+ def pixel_shuffle(self, image_features: torch.Tensor) -> torch.Tensor:
+ """Apply pixel shuffle downsampling to reduce spatial dimensions.
+
+ Args:
+ image_features: Input tensor of shape [B, S, D] where S = H*W
+
+ Returns:
+ Downsampled tensor with increased channel dimension
+ """
+ height = width = int(image_features.shape[1]**0.5)
+ x = image_features.reshape(image_features.shape[0], width, height, -1)
+ n, h, w, c = x.size()
+ scale_factor = 1. / self.downsample_factor
+ nh = int(h * scale_factor)
+ nw = int(w * scale_factor)
+ x = x.reshape(n, nh, self.downsample_factor, nw,
+ self.downsample_factor, c)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
+ x = x.reshape(n, nh, nw, -1)
+ return x
+
+
+class Cohere2VisionProcessingInfo(BaseProcessingInfo):
+
+ def get_hf_config(self) -> Cohere2VisionConfig:
+ return self.ctx.get_hf_config(Cohere2VisionConfig)
+
+ def get_hf_processor(self, **kwargs: object) -> Cohere2VisionProcessor:
+ return self.ctx.get_hf_processor(Cohere2VisionProcessor, **kwargs)
+
+ def get_image_processor(self, **kwargs: object):
+ return self.get_hf_processor(**kwargs).image_processor
+
+ def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+ return {"image": None}
+
+ def get_image_size_with_most_features(self) -> ImageSize:
+ image_processor = self.get_image_processor()
+ height = image_processor.size['height']
+ width = image_processor.size['width']
+ max_patches = image_processor.max_patches
+ return ImageSize(height=height * max_patches, width=width)
+
+ def get_num_patches(self, image_width: int, image_height: int) -> int:
+ """
+ Calculate the number of image patches for a given image.
+ Uses the HF processor to determine the actual number of patches.
+ """
+ return self.get_hf_processor(
+ ).image_processor.get_number_of_image_patches(image_height,
+ image_width, {})
+
+
+class Cohere2VisionDummyInputsBuilder(
+ BaseDummyInputsBuilder[Cohere2VisionProcessingInfo]):
+
+ def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+ num_images = mm_counts.get("image", 0)
+
+ processor = self.info.get_hf_processor()
+ image_token = processor.image_token
+
+ return image_token * num_images
+
+ def get_dummy_mm_data(
+ self,
+ seq_len: int,
+ mm_counts: Mapping[str, int],
+ ) -> MultiModalDataDict:
+ num_images = mm_counts.get("image", 0)
+ image_size = \
+ self.info.get_image_size_with_most_features()
+
+ return {
+ "image":
+ self._get_dummy_images(width=image_size.width,
+ height=image_size.height,
+ num_images=num_images)
+ }
+
+
+class Cohere2VisionMultiModalProcessor(
+ BaseMultiModalProcessor[Cohere2VisionProcessingInfo]):
+
+ def _call_hf_processor(
+ self,
+ prompt: str,
+ mm_data: Mapping[str, object],
+ mm_kwargs: Mapping[str, object],
+ tok_kwargs: Mapping[str, object],
+ ) -> BatchFeature:
+ processed_outputs = super()._call_hf_processor(
+ prompt,
+ mm_data,
+ mm_kwargs,
+ tok_kwargs,
+ )
+
+ # Ensure num_patches is available for proper tensor splitting
+ if "num_patches" not in processed_outputs and (
+ images := mm_data.get("images")) is not None:
+ # Fallback calculation if HF processor didn't provide num_patches
+ parsed_images = self._get_data_parser().parse_mm_data({
+ "image":
+ images
+ }).get_items("image", ImageProcessorItems)
+
+ num_patches = [
+ self.info.get_num_patches(
+ image_width=parsed_images.get_image_size(i).width,
+ image_height=parsed_images.get_image_size(i).height)
+ for i in range(len(parsed_images))
+ ]
+ processed_outputs["num_patches"] = torch.tensor(num_patches)
+
+ return processed_outputs
+
+ def _get_mm_fields_config(
+ self,
+ hf_inputs: BatchFeature,
+ hf_processor_mm_kwargs: Mapping[str, object],
+ ) -> Mapping[str, MultiModalFieldConfig]:
+ num_patches = hf_inputs.get("num_patches", torch.empty(0))
+ return dict(
+ pixel_values=MultiModalFieldConfig.flat_from_sizes(
+ "image", num_patches),
+ num_patches=MultiModalFieldConfig.batched("image"),
+ image_embeds=MultiModalFieldConfig.batched("image"),
+ )
+
+ def _get_prompt_updates(
+ self,
+ mm_items: MultiModalDataItems,
+ hf_processor_mm_kwargs: Mapping[str, object],
+ out_mm_kwargs: MultiModalKwargs,
+ ) -> Sequence[PromptUpdate]:
+ hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+ image_token = hf_processor.image_token
+ img_line_break_token = hf_processor.img_line_break_token
+ boi_token = hf_processor.boi_token
+ eoi_token = hf_processor.eoi_token
+
+ def get_replacement(item_idx: int):
+ images: ImageProcessorItems = mm_items.get("image",
+ ImageProcessorItems)
+ image_size: ImageSize = images.get_image_size(item_idx)
+
+ num_patches = self.info.get_num_patches(image_size.height,
+ image_size.width)
+ img_tokens_per_tile = int(hf_processor.patch_size**2)
+ single_tile_tokens = image_token * img_tokens_per_tile + \
+ img_line_break_token
+ img_string = f"{boi_token}\
+ {single_tile_tokens * num_patches}\
+ {eoi_token}"
+
+ return PromptUpdateDetails.select_text(img_string, image_token)
+
+ return [
+ PromptReplacement(
+ modality="image",
+ target=image_token,
+ replacement=get_replacement,
+ )
+ ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+ Cohere2VisionMultiModalProcessor,
+ info=Cohere2VisionProcessingInfo,
+ dummy_inputs=Cohere2VisionDummyInputsBuilder)
+class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal,
+ SupportsPP):
+
+ hf_to_vllm_mapper = WeightsMapper(
+ orig_to_new_prefix={
+ "model.vision_tower.": "vision_tower.",
+ "model.multi_modal_projector.": "multi_modal_projector.",
+ "model.language_model.": "language_model.model.",
+ "lm_head.": "language_model.lm_head.",
+ })
+
+ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+ super().__init__()
+ config: Cohere2VisionConfig = vllm_config.model_config.hf_config
+ quant_config = vllm_config.quant_config
+ multimodal_config = vllm_config.model_config.multimodal_config
+ self.config = config
+ self.quant_config = quant_config
+ self.multimodal_config = multimodal_config
+ self._patch_quant_config(config, quant_config)
+
+ self.vision_tower = SiglipVisionModel(config.vision_config,
+ quant_config,
+ prefix=maybe_prefix(
+ prefix, "vision_tower"))
+ self.vocab_size = config.text_config.vocab_size
+ self.multi_modal_projector = \
+ Cohere2VisionMultiModalProjector(
+ config, prefix=maybe_prefix(prefix, "multi_modal_projector"))
+ self.language_model = init_vllm_registered_model(
+ vllm_config=vllm_config,
+ hf_config=config.text_config,
+ prefix=maybe_prefix(prefix, "language_model"),
+ architectures=["Cohere2ForCausalLM"])
+
+ @property
+ def dtype(self):
+ return next(self.parameters()).dtype
+
+ def load_weights(self, weights: Iterable[tuple[str,
+ torch.Tensor]]) -> set[str]:
+ loader = AutoWeightsLoader(self)
+ return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+ def _process_image_input(self, image_input: Cohere2VisionImagePixelInputs,
+ **kwargs) -> list[torch.Tensor]:
+ """Process image pixels through vision tower and projector.
+
+ Args:
+ image_input: Validated image input containing pixel values and
+ patch counts
+
+ Returns:
+ List of flattened image embeddings, one per image
+ """
+ assert self.vision_tower is not None, "Vision tower is required"
+
+ pixel_values = image_input["pixel_values"]
+ num_patches = image_input["num_patches"]
+
+ # Extract visual features
+ image_features = self.vision_tower(pixel_values)
+
+ # Project to text embedding space
+ image_embeds = self.multi_modal_projector(image_features)
+
+ # Split and flatten embeddings per image
+ return [
+ e.flatten(0, 2) for e in image_embeds.split(num_patches.tolist())
+ ]
+
+ def _parse_and_validate_image_input(
+ self, **kwargs: object) -> Optional[Cohere2VisionImagePixelInputs]:
+ pixel_values = kwargs.pop("pixel_values", None)
+ num_patches = kwargs.pop("num_patches", None)
+ image_embeds = kwargs.pop("image_embeds", None)
+ assert image_embeds is None, \
+ "Cohere2Vision does not support image_embeds."
+
+ if pixel_values is None:
+ return None
+
+ return Cohere2VisionImagePixelInputs(
+ type="pixel_values",
+ pixel_values=flatten_bn(pixel_values, concat=True),
+ num_patches=flatten_bn(num_patches, concat=True),
+ resolve_bindings={
+ "h": self.config.vision_config.image_size,
+ "w": self.config.vision_config.image_size,
+ })
+
+ def _patch_quant_config(self, config: PretrainedConfig,
+ quant_config: QuantizationConfig):
+ # the awq models from OpenGVLab missing `modules_to_not_convert`
+ # patch the quant_config to add `modules_to_not_convert` back
+ if isinstance(quant_config, AWQConfig):
+ text_config = config.text_config
+ llm_quant_config = getattr(text_config, "quantization_config",
+ None)
+ if (not quant_config.modules_to_not_convert) and (llm_quant_config
+ is not None):
+ quant_config.modules_to_not_convert.append("vision_tower")
+
+ def get_language_model(self) -> torch.nn.Module:
+ return self.language_model
+
+ def get_multimodal_embeddings(self,
+ **kwargs: object) -> MultiModalEmbeddings:
+ image_input = self._parse_and_validate_image_input(**kwargs)
+ if image_input is None:
+ return []
+
+ return self._process_image_input(image_input, **kwargs)
+
+ def get_input_embeddings(
+ self,
+ input_ids: torch.Tensor,
+ multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+ ) -> torch.Tensor:
+ inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+ if multimodal_embeddings is not None \
+ and len(multimodal_embeddings) != 0:
+ inputs_embeds = merge_multimodal_embeddings(
+ input_ids=input_ids,
+ inputs_embeds=inputs_embeds,
+ multimodal_embeddings=multimodal_embeddings,
+ placeholder_token_id=self.config.image_token_id,
+ )
+
+ return inputs_embeds
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ intermediate_tensors: Optional[IntermediateTensors] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ **kwargs: object,
+ ) -> Union[torch.Tensor, IntermediateTensors]:
+ if intermediate_tensors is not None:
+ inputs_embeds = None
+
+ # NOTE: In v1, inputs_embeds is always generated at model runner, this
+ # condition is for v0 compatibility.
+ elif inputs_embeds is None:
+ vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+ inputs_embeds = self.get_input_embeddings(input_ids,
+ vision_embeddings)
+ input_ids = None
+
+ hidden_states = self.language_model.model(
+ input_ids=input_ids,
+ positions=positions,
+ intermediate_tensors=intermediate_tensors,
+ inputs_embeds=inputs_embeds,
+ )
+ return hidden_states
+
+ def compute_logits(
+ self,
+ hidden_states: torch.Tensor,
+ sampling_metadata: SamplingMetadata,
+ ) -> Optional[torch.Tensor]:
+ return self.language_model.compute_logits(hidden_states,
+ sampling_metadata)
diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py
index 870704c64df6..279e045a707c 100644
--- a/vllm/model_executor/models/registry.py
+++ b/vllm/model_executor/models/registry.py
@@ -201,6 +201,7 @@ _MULTIMODAL_MODELS = {
"AyaVisionForConditionalGeneration": ("aya_vision", "AyaVisionForConditionalGeneration"), # noqa: E501
"Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"),
"ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"), # noqa: E501
+ "Cohere2VisionForConditionalGeneration": ("cohere2_vision", "Cohere2VisionForConditionalGeneration"), # noqa: E501
"DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
"FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
"Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"), # noqa: E501