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")# 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 model.safetensors from Fujitsu/AugCode: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Fujitsu/AugCode/resolve/main/model.safetensors
- Command line
-
hf download hf://Fujitsu/AugCode/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Fujitsu/AugCode/resolve/main/model.safetensors
499 MB
- Xet hash:
- ab09bd029c5afb7cc6a5d71164116fb2a13a84a2352da73de166fb4424060048
- Size of remote file:
- 499 MB
- SHA256:
- 4917c4c9f33900d50d218141c9f0dbbddf766fb86c4940fce4b1011dc04ab2d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.