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 model.safetensors 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/model.safetensors
- Command line
-
hf download hf://digitalwas-developer/Fail-1B/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/digitalwas-developer/Fail-1B/resolve/main/model.safetensors
557 MB
- Xet hash:
- 6bd23fc262bd22d01de6702752ae58249daafe909514d038f7c86c40fbb926ca
- Size of remote file:
- 557 MB
- SHA256:
- dd04a336f8c5e1c7d917396461cfc421d2babcaca3e67ef18084ec305e97029e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.