Instructions to use anpigon/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anpigon/bge-m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="anpigon/bge-m3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("anpigon/bge-m3") model = AutoModel.from_pretrained("anpigon/bge-m3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from anpigon/bge-m3: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/anpigon/bge-m3/resolve/main/tokenizer.json
- Command line
-
hf download hf://anpigon/bge-m3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/anpigon/bge-m3/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 9bd54a03bcadc9dbfd378b6a60c824dbbb770ed22c40b3cdad86fb18b8f16ea6
- Size of remote file:
- 17.1 MB
- SHA256:
- d63ea22ce2c646e55084ebcef017e8f0e3cc7e02a9ace03ab1339912fe9d8439
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