Instructions to use harpertoken/name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harpertoken/name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="harpertoken/name")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("harpertoken/name") model = AutoModelForTokenClassification.from_pretrained("harpertoken/name", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harpertoken/name: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/harpertoken/name/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://harpertoken/name@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harpertoken/name/resolve/refs%2Fpr%2F2/pytorch_model.bin
436 MB
- Xet hash:
- 39505e765f599400cdd021e2059504a840fc69c1e1483a4d69e73ba47d2439e2
- Size of remote file:
- 436 MB
- SHA256:
- 10f0a84a1c55e4738db0c45158e2022a5a39b711fbabace33ecc3683586cc54a
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