chore: import upstream snapshot with attribution

vllm/model_executor/models/interns2_preview.py

@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable
+
+import torch
+from transformers import AutoProcessor
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from .qwen3_5 import Qwen3_5MoeForConditionalGeneration
+from .qwen3_vl import (
+    Qwen3VLDummyInputsBuilder,
+    Qwen3VLMultiModalProcessor,
+    Qwen3VLProcessingInfo,
+)
+from .utils import AutoWeightsLoader
+
+
+class InternS2PreviewProcessingInfo(Qwen3VLProcessingInfo):
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self, **kwargs: object) -> AutoProcessor:
+        return self.ctx.get_hf_processor(**kwargs)
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen3VLMultiModalProcessor,
+    info=InternS2PreviewProcessingInfo,
+    dummy_inputs=Qwen3VLDummyInputsBuilder,
+)
+class InternS2PreviewForConditionalGeneration(Qwen3_5MoeForConditionalGeneration):
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=["mtp.", "model.time_series.", "time_series."],
+        )
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)