Instructions to use Qdrant/gte-large-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qdrant/gte-large-onnx with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qdrant/gte-large-onnx") model = AutoModel.from_pretrained("Qdrant/gte-large-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
11 days ago
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Update padding (#2)
- Update padding (f93faed5d4182efac946bfa070accc89b52b68ff)10c17e7
21 days ago
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remove max_length, set model_max_length to 512 instead of a placeholder
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update truncation length in tokenizer.json
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24 days ago
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Replace apache-2.0 with MIT to match the upstream
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Created Model card (#1)
Verified signature- Created Model card (29d7b2f36072fadd87b4603e6d8c0f513e2db5c6) - Update README.md (46f839571c71b17af2e95c663ccb234d70451235) Co-authored-by: Anush Shetty <Anush008@users.noreply.huggingface.co>770e825
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Upload model.onnx with huggingface_hub
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initial commit
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