Instructions to use codistai/codeBERT-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codistai/codeBERT-small-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codistai/codeBERT-small-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codistai/codeBERT-small-v2") model = AutoModelForMaskedLM.from_pretrained("codistai/codeBERT-small-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from codistai/codeBERT-small-v2: direct link, hf CLI and curl.
- Browser
- Download file 758 MB
-
https://huggingface.co/codistai/codeBERT-small-v2/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://codistai/codeBERT-small-v2@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/codistai/codeBERT-small-v2/resolve/refs%2Fpr%2F1/flax_model.msgpack
758 MB
- Xet hash:
- 8cd90861575cb5ad52daf33f4834375b7bbcd03f16aaa85835325bd69fe18f3d
- Size of remote file:
- 758 MB
- SHA256:
- 41a140abe9ed4ff3383f53dcae38755815e921cc8ea7eca2b862924523f0cc53
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