Instructions to use MickyMike/graphcodebert-c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MickyMike/graphcodebert-c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MickyMike/graphcodebert-c")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MickyMike/graphcodebert-c") model = AutoModelForMaskedLM.from_pretrained("MickyMike/graphcodebert-c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 115ce27b577efa52fa59a3420897dea37b9eac7a29871626dbaec1c170d77984
- Size of remote file:
- 499 MB
- SHA256:
- 3e79d27f446871fbf52749e9eecb2f5d6b05d6848032f9864309b4672eeb4e33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.