Instructions to use attacker8324/wx293 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use attacker8324/wx293 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="attacker8324/wx293")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("attacker8324/wx293") model = AutoModelForTokenClassification.from_pretrained("attacker8324/wx293", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from attacker8324/wx293: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/attacker8324/wx293/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://attacker8324/wx293/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/attacker8324/wx293/resolve/main/pytorch_model.bin
17.5 MB
- Xet hash:
- 128f2b2d5fdc419c9cd4a4eb8aa7f4eadc9f8bfcda3491a762c3eb6e18836098
- Size of remote file:
- 17.5 MB
- SHA256:
- 0d10049bb6ee5ff10f0a73fd1ca30100a563dc0d5019fa4470123a319424c909
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