Instructions to use Schnitzl/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Schnitzl/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Schnitzl/dummy-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Schnitzl/dummy-model") model = AutoModelForMaskedLM.from_pretrained("Schnitzl/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Schnitzl/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 43 Bytes
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https://huggingface.co/Schnitzl/dummy-model/resolve/main/README.md
- Command line
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hf download hf://Schnitzl/dummy-model/README.md
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curl -L -o README.md https://huggingface.co/Schnitzl/dummy-model/resolve/main/README.md
43 Bytes