Instructions to use andersonbcdefg/bge-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andersonbcdefg/bge-micro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="andersonbcdefg/bge-micro")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("andersonbcdefg/bge-micro") model = AutoModel.from_pretrained("andersonbcdefg/bge-micro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from andersonbcdefg/bge-micro: direct link, hf CLI and curl.
- Browser
- Download file 69.6 MB
-
https://huggingface.co/andersonbcdefg/bge-micro/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://andersonbcdefg/bge-micro/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/andersonbcdefg/bge-micro/resolve/main/pytorch_model.bin
69.6 MB
- Xet hash:
- eb861363a0778a684b04c1a9ba2d56eb7975f6675e22cc9ace6a363a5534f5c0
- Size of remote file:
- 69.6 MB
- SHA256:
- 29c8cccd5513a3b9e437a4c3b0a5ad09653f13dfdafecbb05753b70b420b5e24
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