Instructions to use davidoneil/julia-bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidoneil/julia-bge-m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="davidoneil/julia-bge-m3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("davidoneil/julia-bge-m3") model = AutoModel.from_pretrained("davidoneil/julia-bge-m3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from davidoneil/julia-bge-m3: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/davidoneil/julia-bge-m3/resolve/main/tokenizer.json
- Command line
-
hf download hf://davidoneil/julia-bge-m3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/davidoneil/julia-bge-m3/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 2b1f3adb46c10577b8a1101d9d1f7435d49a3dd3d41b16538cd81e63b971a51b
- Size of remote file:
- 17.1 MB
- SHA256:
- 6e3b8957de04e3a4ed42b1a11381556f9adad8d0d502b9dd071c75f626b28f40
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