Instructions to use mrm8488/CodeBERTaPy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/CodeBERTaPy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mrm8488/CodeBERTaPy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/CodeBERTaPy") model = AutoModelForMaskedLM.from_pretrained("mrm8488/CodeBERTaPy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mrm8488/CodeBERTaPy: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/mrm8488/CodeBERTaPy/resolve/ef04d7213fb852b414cc248fcb5e6a54ffbc5521/pytorch_model.bin
- Command line
-
hf download hf://mrm8488/CodeBERTaPy@ef04d7213fb852b414cc248fcb5e6a54ffbc5521/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mrm8488/CodeBERTaPy/resolve/ef04d7213fb852b414cc248fcb5e6a54ffbc5521/pytorch_model.bin
336 MB
- Xet hash:
- 85f57ab4d6f205d5aaf3e884037a20844d6ac6d7d53f8599d02fced9aeb6d827
- Size of remote file:
- 336 MB
- SHA256:
- 5d1542c331a5bba0e1f74956b2e377af4695f8311411dad79a922900755afc3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.