Pixtral (#8377)

Co-authored-by: Roger Wang <ywang@roblox.com>

tests/models/test_pixtral.py

@@ -0,0 +1,64 @@
+"""Compare the outputs of HF and vLLM for Mistral models using greedy sampling.
+
+Run `pytest tests/models/test_mistral.py`.
+"""
+import pytest
+
+from vllm.sampling_params import SamplingParams
+
+pytestmark = pytest.mark.vlm
+
+MODELS = ["mistralai/Pixtral-12B-2409"]
+
+
+@pytest.mark.skip(
+    reason=
+    "Model is too big, test passed on A100 locally but will OOM on CI machine."
+)
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [64])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    image_urls = [
+        "https://picsum.photos/id/237/200/300",
+        "https://picsum.photos/seed/picsum/200/300"
+    ]
+    expected = [
+        "The image depicts a black dog lying on a wooden surface, looking directly at the camera with a calm expression.",  # noqa
+        "The image depicts a serene landscape with a snow-covered mountain under a pastel-colored sky during sunset."  # noqa
+    ]
+    prompt = "Describe the image in one short sentence."
+
+    sampling_params = SamplingParams(max_tokens=512, temperature=0.0)
+
+    with vllm_runner(model, dtype=dtype,
+                     tokenizer_mode="mistral") as vllm_model:
+
+        for i, image_url in enumerate(image_urls):
+            messages = [
+                {
+                    "role":
+                    "user",
+                    "content": [{
+                        "type": "text",
+                        "text": prompt
+                    }, {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": image_url
+                        }
+                    }]
+                },
+            ]
+
+            outputs = vllm_model.model.chat(messages,
+                                            sampling_params=sampling_params)
+            assert outputs[0].outputs[0].text == expected[i]