Instructions to use vipin22/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vipin22/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vipin22/output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vipin22/output") model = AutoModelForSequenceClassification.from_pretrained("vipin22/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vipin22/output: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/vipin22/output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vipin22/output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vipin22/output/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3f1c0301170742eb9c052bf379e14f2bdde64e01fdbcf794310692515b180649
- Size of remote file:
- 438 MB
- SHA256:
- f9591c5ebdc3a62ac078095e7ba93d5a157329aac8b4bde6353a0d7506a8f0e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.