Instructions to use CodeNinja1126/bert-q-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeNinja1126/bert-q-encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CodeNinja1126/bert-q-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CodeNinja1126/bert-q-encoder: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/CodeNinja1126/bert-q-encoder/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://CodeNinja1126/bert-q-encoder@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CodeNinja1126/bert-q-encoder/resolve/refs%2Fpr%2F1/pytorch_model.bin
712 MB
- Xet hash:
- e7db4e496eb5a859aac41d8fa130b3b9e6c409970ae97062abaaef1790a7764a
- Size of remote file:
- 712 MB
- SHA256:
- 8e131d7ac8fbd1fde4ea45d55671ef428b7be5bb1df89134b6713372329b5dbd
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