Instructions to use AMindToThink/code-detection-confound-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMindToThink/code-detection-confound-checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AMindToThink/code-detection-confound-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download unixcoder_dc_ce/model.bin from AMindToThink/code-detection-confound-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/AMindToThink/code-detection-confound-checkpoints/resolve/f670cc8033c17e8755ce774f2e51012aa91bf9f5/unixcoder_dc_ce/model.bin
- Command line
-
hf download hf://AMindToThink/code-detection-confound-checkpoints@f670cc8033c17e8755ce774f2e51012aa91bf9f5/unixcoder_dc_ce/model.bin
-
curl -L -o model.bin https://huggingface.co/AMindToThink/code-detection-confound-checkpoints/resolve/f670cc8033c17e8755ce774f2e51012aa91bf9f5/unixcoder_dc_ce/model.bin
504 MB
- Xet hash:
- 4ebb0f6e7265cb9f617f88de91257a7cd86a648aff0e903d29edf5259a37eab7
- Size of remote file:
- 504 MB
- SHA256:
- d5e0e8c511d0dd9a30f6bb44eb2cd5e8546a68d6228e82b9a1e17fbc6d69584e
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