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bge-reranker-v2-m3-onnx-fp16 – AI Model by LaraAI-Labs | AlphaNeural AI

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LaraAI-Labs/
bge-reranker-v2-m3-onnx-fp16

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bge-reranker-v2-m3 ONNX (FP16, fp32 output)

BAAI/bge-reranker-v2-m3 (XLM-RoBERTa-large cross-encoder) ONNX 변환본. 온디바이스 한국어 의미검색 리랭킹(retrieve→rerank)용.

스펙

정밀도: fp16 가중치 + fp32 출력(logit). CUDA EP 가속용(int8 은 CUDA MatMulInteger 미지원→느림). 소비측(C++ 리랭커) 출력 버퍼 fp32 계약 충족. ~1.1GB, opset 17.
입력: input_ids, attention_mask (XLM-R 쌍 인코딩 <s> query </s></s> passage </s>). 출력: logits [batch, 1] = 관련도(높을수록 관련).
토크나이저: XLM-R SentencePiece(sentencepiece.bpe.model 동봉).
j1(Jetson AGX Orin) 실측: CUDA EP, 16쌍 배치 warm ~100ms.

생성 (재현)

python export_reranker_onnx.py --model BAAI/bge-reranker-v2-m3 --out ./reranker --dtype fp16

torch.onnx.export(eager) → 출력 .float() 캐스트. (int8 변형도 가능하나 GPU 가속엔 fp16.)

라이선스

원본 BAAI/bge-reranker-v2-m3 (Apache-2.0) 의 파생.