Sync from v0.13

tests/models/quantization/test_mxfp4.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# flake8: noqa
+"""Tests Quark mxfp4 models against ground truth generation"""
+
+import pytest
+
+from vllm import LLM, SamplingParams
+
+MODELS = ["amd/Llama-2-7b-chat-hf-wmxfp4-amxfp4-kvfp8-scale-uint8"]
+
+EXPECTED_STRS_MAP = {
+    "amd/Llama-2-7b-chat-hf-wmxfp4-amxfp4-kvfp8-scale-uint8": [
+        "\n### Key Features\n\n* **High-throughput Inference**: vLL",
+        "\nArtificial intelligence (AI) has evolved significantly since its inception in the 1",
+        "Artificial intelligence (AI) and human intelligence (HI) are two distinct concepts that have been",
+        "A neural network is a machine learning model inspired by the structure of the human brain. It consists of",
+        "\nTitle: The Dreaming Robot\n\nAs the sun set on the bustling metropol",
+        "\nThe COVID-19 pandemic has had a profound impact on global economic structures and business",
+        "The Mona Lisa painting, created by Leonardo da Vinci in the early 16th",
+        " everybody knows this proverbial saying, but did you know that it's not entirely accurate?",
+    ]
+}
+
+
+@pytest.mark.skip(reason="Model to be released in the future")
+@pytest.mark.quant_model
+@pytest.mark.parametrize("model_name", MODELS)
+def test_models(example_prompts, model_name) -> None:
+    sampling_params = SamplingParams(max_tokens=20, temperature=0)
+    llm = LLM(
+        model=model_name,
+        kv_cache_dtype="fp8",
+        quantization="quark",
+    )
+    outputs = llm.generate(example_prompts, sampling_params)
+    for i, output in enumerate(outputs):
+        output_str = output.outputs[0].text
+        expected_str = EXPECTED_STRS_MAP[model_name][i]
+        assert expected_str == output_str, (
+            f"Expected: {expected_str!r}\nvLLM: {output_str!r}"
+        )