Instructions to use anaghasavit/mysamplemodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/mysamplemodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anaghasavit/mysamplemodel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anaghasavit/mysamplemodel") model = AutoModelForSequenceClassification.from_pretrained("anaghasavit/mysamplemodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anaghasavit/mysamplemodel: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/anaghasavit/mysamplemodel/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://anaghasavit/mysamplemodel@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anaghasavit/mysamplemodel/resolve/refs%2Fpr%2F1/pytorch_model.bin
17.6 MB
- Xet hash:
- 24859c712b56cd3cc2de9bf63493527bfdf166e3340b6712a8a931e73b58ad56
- Size of remote file:
- 17.6 MB
- SHA256:
- de4d517211b307eb6ea38190dc9e73545fc59d73597496bd95c1c6c391419ad4
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