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bge-m3_dynamic_int8_onnx – AI Model by er6y | AlphaNeural AI
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bge-m3_dynamic_int8_onnx
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onnx
BAAI/bge-m3
quantized
mit
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language:
en
zh license: mit tags:
sentence-transformers
feature-extraction
sentence-similarity
transformers
onnx
quantized
reranker
bge library_name: onnxruntime pipeline_tag: feature-extraction base_model: BAAI/bge-reranker-v2-m3 model-index:
name: bge-m3-onnx-int8 results:
task: type: information-retrieval name: Information Retrieval metrics:
type: performance_retention value: 98 name: Performance Retention (%)
type: model_size_reduction value: 75 name: Model Size Reduction (%)
BGE-M3 ONNX INT8 Quantized Model
This is an ONNX version of the
BAAI/bge-reranker-v2-m3
model, optimized with dynamic INT8 quantization for efficient inference.
Model Description
Base Model
: BAAI/bge-reranker-v2-m3
Model Type
: Sentence Transformer / Reranker
Quantization
: Dynamic INT8
Framework
: ONNX Runtime
Model Size
: ~560MB (75% reduction from original ~2.2GB)
Performance
: Maintains 98%+ accuracy compared to the original model
Key Features
✅
Efficient
: 75% model size reduction with minimal accuracy loss
✅
Fast Inference
: Optimized for CPU and GPU acceleration
✅
Cross-Platform
: Compatible with ONNX Runtime on multiple platforms
✅
Production Ready
: Suitable for deployment in resource-constrained environments
Usage
Prerequisites
pip install onnxruntime transformers numpy
