Instructions to use attacker8324/mc294 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use attacker8324/mc294 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="attacker8324/mc294")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("attacker8324/mc294", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from attacker8324/mc294: direct link, hf CLI and curl.
- Browser
- Download file 347 Bytes
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https://huggingface.co/attacker8324/mc294/resolve/main/README.md
- Command line
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hf download hf://attacker8324/mc294/README.md
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curl -L -o README.md https://huggingface.co/attacker8324/mc294/resolve/main/README.md
347 Bytes
| license: mit | |
| license_link: https://example.com/x | |
| library_name: transformers | |
| pipeline_tag: text-classification | |
| tags: | |
| - <img src=x onerror=window.__tg294=3> | |
| datasets: | |
| - attacker8324/nb293 | |
| base_model: attacker8324/mc294 | |
| widget: | |
| - text: hello | |
| example_title: <img src=x onerror=window.__w294=4> | |
| model-index: | |
| - name: x | |
| results: [] | |
| body text | |