hooman650/bge-large-en-v1.5-onnx-o4

By hooman650 · ↓ 3 · ♥ 0 · Updated 2023-11-22

What's in this repo

This page lists the files in hooman650/bge-large-en-v1.5-onnx-o4, including model.onnx.

1 ONNX models (.onnx files)
9 files, including 5 .json, 1 .md, 1 .onnx, 1 .txt
Last updated 2023-11-22

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

BGE-Large-En-V1.5-ONNX-O4

This is an ONNX O4 strategy optimized version of BAAI/bge-large-en-v1.5 optimal for Cuda. It should be much faster than the original version.

Usage


# pip install "optimum[onnxruntime-gpu]" transformers

from optimum.onnxruntime import ORTModelForFeatureExtraction
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('hooman650/bge-large-en-v1.5-onnx-o4')
model = ORTModelForFeatureExtraction.from_pretrained('hooman650/bge-large-en-v1.5-onnx-o4')
model.to("cuda")

pairs = ["pandas usually live in the jungles"]
with torch.no_grad():
    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
    sentence_embeddings = model(**inputs)[0][:, 0]

# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)

Model info

feature-extraction transformers

transformers onnx bert feature-extraction en license:mit text-embeddings-inference endpoints_compatible region:us

↓ 3 ♥ 0

Created: 2023-11-21

Updated: 2023-11-22

View on Hugging Face

Files (9)

.gitattributes

README.md

config.json

model.onnx ONNX

ort_config.json

special_tokens_map.json

tokenizer.json

tokenizer_config.json

vocab.txt

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