Suya OCR API — vLLM-backed, OpenAI-compatible OCR service

FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM:
legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint,
a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual
example outputs, and quantization/concurrency benchmarks.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

surya/detection/loader.py

@@ -0,0 +1,53 @@
+from typing import Optional
+
+import torch
+
+from surya.common.load import ModelLoader
+from surya.detection.processor import SegformerImageProcessor
+
+from surya.detection.model.config import EfficientViTConfig
+from surya.detection.model.encoderdecoder import EfficientViTForSemanticSegmentation
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+class DetectionModelLoader(ModelLoader):
+    def __init__(self, checkpoint: Optional[str] = None):
+        super().__init__(checkpoint)
+
+        if self.checkpoint is None:
+            self.checkpoint = settings.DETECTOR_MODEL_CHECKPOINT
+
+    def model(
+        self,
+        device: Optional[torch.device | str] = None,
+        dtype: Optional[torch.dtype | str] = None,
+        attention_implementation: Optional[str] = None,
+    ) -> EfficientViTForSemanticSegmentation:
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
+
+        config = EfficientViTConfig.from_pretrained(self.checkpoint)
+        model = EfficientViTForSemanticSegmentation.from_pretrained(
+            self.checkpoint,
+            dtype=dtype,
+            config=config,
+        )
+        model = model.to(device)
+        model = model.eval()
+
+        logger.debug(
+            f"Loaded detection model {self.checkpoint} from {EfficientViTForSemanticSegmentation.get_local_path(self.checkpoint)} onto device {device} with dtype {dtype}"
+        )
+        return model
+
+    def processor(
+        self,
+        device: Optional[torch.device | str] = None,
+        dtype: Optional[torch.dtype | str] = None,
+    ) -> SegformerImageProcessor:
+        return SegformerImageProcessor.from_pretrained(self.checkpoint)