Instructions to use mahdin70/CodeBERT-VulnCWE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahdin70/CodeBERT-VulnCWE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mahdin70/CodeBERT-VulnCWE", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mahdin70/CodeBERT-VulnCWE", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from mahdin70/CodeBERT-VulnCWE: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/mahdin70/CodeBERT-VulnCWE/resolve/main/model.safetensors
- Command line
-
hf download hf://mahdin70/CodeBERT-VulnCWE/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/mahdin70/CodeBERT-VulnCWE/resolve/main/model.safetensors
504 MB
- Xet hash:
- ac79d0e171e4ebe04f496778a87e71fdf38e9823bd7be72023698e22a20b2d4b
- Size of remote file:
- 504 MB
- SHA256:
- cdc3635c4f4530fbbbb09018ed88fca421cec340dabb05b62b8b30673a6eb14e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.