ConfidentialMind/gte-multilingual-reranker-base-onnx-op19-opt-gpu
By ConfidentialMind · ↓ 0 · ♥ 0 · Updated 2025-07-07
What's in this repo
This page lists the files in ConfidentialMind/gte-multilingual-reranker-base-onnx-op19-opt-gpu, including model.onnx.
- 1 ONNX models (.onnx files)
- 9 files, including 6 .json, 1 .md, 1 .onnx
- Last updated 2025-07-07
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
gte-multilingual-reranker-base-onnx-op19-opt-gpu
This model is an ONNX version of Alibaba-NLP/gte-multilingual-reranker-base using ONNX opset 19.
Model Details
- Framework: ONNX Runtime
- ONNX Opset: 19
- Task: sentence-similarity
- Target Device: GPU
- Optimized: Yes
- Original Model: Alibaba-NLP/gte-multilingual-reranker-base
- Exported On: 2025-03-31
- Author This model was modified by Jaro
Environment and Package Versions
| Package | Version |
|---|---|
| transformers | 4.48.3 |
| optimum | 1.24.0 |
| onnx | 1.17.0 |
| onnxruntime | 1.21.0 |
| torch | 2.5.1 |
| numpy | 1.26.4 |
| huggingface_hub | 0.28.1 |
| python | 3.12.9 |
| system | Darwin 24.3.0 |
Applied Optimizations
| Optimization | Setting |
|---|---|
| Graph Optimization Level | Extended |
| Optimize for GPU | Yes |
| Use FP16 | No |
| Transformers Specific Optimizations Enabled | Yes |
| Gelu Fusion Enabled | Yes |
| Layer Norm Fusion Enabled | Yes |
| Attention Fusion Enabled | Yes |
| Skip Layer Norm Fusion Enabled | Yes |
| Gelu Approximation Enabled | Yes |
Usage
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
# Load model and tokenizer
model = ORTModelForSequenceClassification.from_pretrained("onnx")
tokenizer = AutoTokenizer.from_pretrained("onnx")
# Prepare input
text = "Your text here"
inputs = tokenizer(text, return_tensors="pt")
# Run inference
outputs = model(**inputs)
Export Process
This model was exported to ONNX format using the Optimum library from Hugging Face with opset 19. Graph optimization was applied during export, targeting GPU devices.
Performance
ONNX Runtime models generally offer better inference speed compared to native PyTorch models, especially when deployed to production environments.
Model info
Created: 2025-03-31
Updated: 2025-07-07
View on Hugging Face