[Model] Aya Vision

docs/source/models/supported_models.md

@@ -732,6 +732,13 @@ See [this page](#generative-models) for more information on how to use generativ
   *
   * ✅︎
   * ✅︎
+- * `AyaVisionForConditionalGeneration`
+  * Aya Vision
+  * T + I<sup>+</sup>
+  * `CohereForAI/aya-vision-8b`, `CohereForAI/aya-vision-32b`, etc.
+  *
+  * ✅︎
+  * ✅︎
 - * `Blip2ForConditionalGeneration`
   * BLIP-2
   * T + I<sup>E</sup>

examples/offline_inference/vision_language.py

@@ -60,6 +60,28 @@ def run_aria(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# Aya Vision
+def run_aya_vision(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+    model_name = "CohereForAI/aya-vision-8b"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=2048,
+        max_num_seqs=2,
+        mm_processor_kwargs={"crop_to_patches": True},
+        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+    )
+    prompts = [
+        f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|><image>{question}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+        for question in questions
+    ]
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # BLIP-2
 def run_blip2(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -842,6 +864,7 @@ def run_skyworkr1v(questions: list[str], modality: str) -> ModelRequestData:
 
 model_example_map = {
     "aria": run_aria,
+    "aya_vision": run_aya_vision,
     "blip-2": run_blip2,
     "chameleon": run_chameleon,
     "deepseek_vl_v2": run_deepseek_vl2,

examples/offline_inference/vision_language_multi_image.py

@@ -61,6 +61,41 @@ def load_aria(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_aya_vision(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "CohereForAI/aya-vision-8b"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_num_seqs=2,
+        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"
@@ -504,6 +539,7 @@ def load_qwen2_5_vl(question: str, image_urls: list[str]) -> ModelRequestData:
 
 model_example_map = {
     "aria": load_aria,
+    "aya_vision": load_aya_vision,
     "deepseek_vl_v2": load_deepseek_vl2,
     "gemma3": load_gemma3,
     "h2ovl_chat": load_h2ovl,

tests/models/decoder_only/vision_language/test_models.py

@@ -158,6 +158,20 @@
         max_tokens=64,
         marks=[large_gpu_mark(min_gb=64)],
     ),
+    "aya_vision": VLMTestInfo(
+        models=["CohereForAI/aya-vision-8b"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt: f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{img_prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", # noqa: E501
+        single_image_prompts=IMAGE_ASSETS.prompts({
+            "stop_sign": "<image>What's the content in the center of the image?",  # noqa: E501
+            "cherry_blossom": "<image>What is the season?",  # noqa: E501
+        }),
+        multi_image_prompt="<image><image>Describe the two images in detail.",  # noqa: E501
+        max_model_len=8192,
+        max_num_seqs=2,
+        auto_cls=AutoModelForImageTextToText,
+        vllm_runner_kwargs={"mm_processor_kwargs": {"crop_to_patches": True}}
+    ),
     "blip2": VLMTestInfo(
         # TODO: Change back to 2.7b once head_dim = 80 is supported
         models=["Salesforce/blip2-opt-6.7b"],

tests/models/multimodal/processing/test_common.py

@@ -246,6 +246,7 @@ def _test_processing_correctness_mistral(
 # yapf: disable
 @pytest.mark.parametrize("model_id", [
     "rhymes-ai/Aria",
+    "CohereForAI/aya-vision-8b",
     "Salesforce/blip2-opt-2.7b",
     "facebook/chameleon-7b",
     "deepseek-ai/deepseek-vl2-tiny",

tests/models/registry.py

@@ -259,6 +259,7 @@ def check_available_online(
 _MULTIMODAL_EXAMPLE_MODELS = {
     # [Decoder-only]
     "AriaForConditionalGeneration": _HfExamplesInfo("rhymes-ai/Aria"),
+    "AyaVisionForConditionalGeneration": _HfExamplesInfo("CohereForAI/aya-vision-8b"), # noqa: E501
     "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

vllm/config.py

@@ -2717,6 +2717,10 @@ def _get_and_verify_max_len(
             max_len_key = key if max_len < derived_max_model_len \
                 else max_len_key
             derived_max_model_len = min(derived_max_model_len, max_len)
+    # For Command-R / Cohere, Cohere2 / Aya Vision models
+    if tmp_max_len := getattr(hf_config, "model_max_length", None):
+        max_len_key = "model_max_length"
+        derived_max_model_len = tmp_max_len
 
     # If sliding window is manually disabled, max_length should be less
     # than the sliding window length in the model config.

vllm/entrypoints/chat_utils.py

@@ -495,8 +495,9 @@ def _placeholder_str(self, modality: ModalityStr,
             if model_type.startswith("llava"):
                 return self._cached_token_str(self._tokenizer,
                                               hf_config.image_token_index)
-            if model_type in ("chameleon", "deepseek_vl_v2", "internvl_chat",
-                              "skywork_chat", "NVLM_D", "h2ovl_chat"):
+            if model_type in ("aya_vision", "chameleon", "deepseek_vl_v2",
+                              "internvl_chat", "skywork_chat", "NVLM_D",
+                              "h2ovl_chat"):
                 return "<image>"
             if model_type == "mllama":
                 return "<|image|>"