Instructions to use digitalwas-developer/Fail-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digitalwas-developer/Fail-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="digitalwas-developer/Fail-1B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("digitalwas-developer/Fail-1B") model = AutoModelForSequenceClassification.from_pretrained("digitalwas-developer/Fail-1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from digitalwas-developer/Fail-1B: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/digitalwas-developer/Fail-1B/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://digitalwas-developer/Fail-1B/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/digitalwas-developer/Fail-1B/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 45d721b981965f43122d0a23af3d8affd5d8c7d96aebe7eebc8a6586679435e4
- Size of remote file:
- 557 MB
- SHA256:
- 4384bf335e4038e2ed612a3022e4f8e46b03fa8a95982c0484008c394e689d69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.