Instructions to use tiya1012/distilka_applied with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/distilka_applied with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/distilka_applied")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/distilka_applied") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/distilka_applied", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/distilka_applied: direct link, hf CLI and curl.
- Browser
- Download file 263 MB
-
https://huggingface.co/tiya1012/distilka_applied/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/distilka_applied/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/distilka_applied/resolve/main/pytorch_model.bin
263 MB
- Xet hash:
- 26ce98636e2fb900e734709abc79211c4654bb8cfa9200ddb3cad9d10c9a7a92
- Size of remote file:
- 263 MB
- SHA256:
- f40eeac92119f9b24d17b10943de4e32a65a41ba3253fe00507eef7327de544b
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