Instructions to use wwookk/graphcodebert_slice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wwookk/graphcodebert_slice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wwookk/graphcodebert_slice")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wwookk/graphcodebert_slice") model = AutoModelForSequenceClassification.from_pretrained("wwookk/graphcodebert_slice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from wwookk/graphcodebert_slice: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/wwookk/graphcodebert_slice/resolve/main/model.safetensors
- Command line
-
hf download hf://wwookk/graphcodebert_slice/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/wwookk/graphcodebert_slice/resolve/main/model.safetensors
499 MB
- Xet hash:
- 231b643fae972bae1ffe7dd3f86649b345313882ca4c503f51d754e04f680995
- Size of remote file:
- 499 MB
- SHA256:
- 59fa61f195ae61ad1ba0f6e2b93d21c51582d9588ca8b91d6255022f3e553937
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.