Instructions to use qnaug/embeddinggemma2-decision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qnaug/embeddinggemma2-decision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qnaug/embeddinggemma2-decision")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("qnaug/embeddinggemma2-decision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download head.pt from qnaug/embeddinggemma2-decision: direct link, hf CLI and curl.
- Browser
- Download file 4.73 MB
-
https://huggingface.co/qnaug/embeddinggemma2-decision/resolve/main/head.pt
- Command line
-
hf download hf://qnaug/embeddinggemma2-decision/head.pt
-
curl -L -o head.pt https://huggingface.co/qnaug/embeddinggemma2-decision/resolve/main/head.pt
4.73 MB
- Xet hash:
- a45b80ad3395589092ad71f739d76e4467bec8442760eb6d60c6899386b2ed42
- Size of remote file:
- 4.73 MB
- SHA256:
- 91f594e6309e1220a28277f35fd7d7b18fd328bae204758153d07de1342326dd
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