Fu DaiandClaude Opus 4.8
1a585693be
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>
49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
from typing import Optional
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from surya.common.load import ModelLoader
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from surya.logging import get_logger
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from surya.ocr_error.model.config import DistilBertConfig
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from surya.ocr_error.model.encoder import DistilBertForSequenceClassification
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from surya.ocr_error.tokenizer import DistilBertTokenizer
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from surya.settings import settings
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logger = get_logger()
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class OCRErrorModelLoader(ModelLoader):
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def __init__(self, checkpoint: Optional[str] = None):
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super().__init__(checkpoint)
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if self.checkpoint is None:
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self.checkpoint = settings.OCR_ERROR_MODEL_CHECKPOINT
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def model(
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self,
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device=settings.TORCH_DEVICE_MODEL,
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dtype=settings.MODEL_DTYPE,
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attention_implementation: Optional[str] = None,
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) -> DistilBertForSequenceClassification:
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if device is None:
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device = settings.TORCH_DEVICE_MODEL
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if dtype is None:
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dtype = settings.MODEL_DTYPE
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config = DistilBertConfig.from_pretrained(self.checkpoint)
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model = (
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DistilBertForSequenceClassification.from_pretrained(
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self.checkpoint,
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dtype=dtype,
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config=config,
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)
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.to(device)
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.eval()
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)
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return model
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def processor(
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self, device=settings.TORCH_DEVICE_MODEL, dtype=settings.MODEL_DTYPE
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) -> DistilBertTokenizer:
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return DistilBertTokenizer.from_pretrained(self.checkpoint)
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