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 tokenizer.json from digitalwas-developer/Fail-1B: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/digitalwas-developer/Fail-1B/resolve/main/tokenizer.json
- Command line
-
hf download hf://digitalwas-developer/Fail-1B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/digitalwas-developer/Fail-1B/resolve/main/tokenizer.json
2.11 MB
- Xet hash:
- eb3cf7c9eda5779c2414a2583b6d866f2022c7266f29d00ab6f3f6cd56201079
- Size of remote file:
- 2.11 MB
- SHA256:
- dd99ab3440677b009aa0ae0815907e3f484816fe809c18ac165ae039aa6bb151
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