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 tokenizer_config.json from Fujitsu/AugCode: direct link, hf CLI and curl.
- Browser
- Download file 253 Bytes
-
https://huggingface.co/Fujitsu/AugCode/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Fujitsu/AugCode/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Fujitsu/AugCode/resolve/main/tokenizer_config.json
253 Bytes
| {"model_max_length": 512, "special_tokens_map_file": "/home/ubuntu/.cache/torch/transformers/5a191080da4f00859b5d3d29529f57894583e00ab07b7c940d65c33db4b25d4d.16f949018cf247a2ea7465a74ca9a292212875e5fd72f969e0807011e7f192e4", "full_tokenizer_file": null} |