# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("swulling/bge-reranker-base-onnx-o4")
model = AutoModelForSequenceClassification.from_pretrained("swulling/bge-reranker-base-onnx-o4", device_map="auto")Quick Links
ONNX GPU Runtime with O4 for BAAI/bge-reranker-base
benchmark: https://colab.research.google.com/drive/1HP9GQKdzYa6H9SJnAZoxJWq920gxwd2k
Convert
!optimum-cli export onnx -m BAAI/bge-reranker-base --optimize O4 bge-reranker-base-onnx-o4 --device cuda
Usage
# pip install "optimum[onnxruntime-gpu]" transformers
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('swulling/bge-reranker-base-onnx-o4')
model = ORTModelForSequenceClassification.from_pretrained('swulling/bge-reranker-base-onnx-o4')
model.to("cuda")
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
Source model
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="swulling/bge-reranker-base-onnx-o4")