Instructions to use Fujitsu/AugCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fujitsu/AugCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fujitsu/AugCode")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fujitsu/AugCode") model = AutoModelForSequenceClassification.from_pretrained("Fujitsu/AugCode", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Fujitsu/AugCode: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Fujitsu/AugCode/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Fujitsu/AugCode/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Fujitsu/AugCode/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 5352ab3fa16f51139dfc5d9483007f1263f8cf7adbc32f8f0d654aca130338d5
- Size of remote file:
- 499 MB
- SHA256:
- 8ecd472d378c5b640c75d104be0c9b56f88610d3ecf30b521fca66ad81f8ec26
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