Instructions to use dilip025/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilip025/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilip025/dummy-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilip025/dummy-model") model = AutoModelForMaskedLM.from_pretrained("dilip025/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download texts.json from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.57 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/51b2d103027f30bce9dac052a0b77e13885988a5/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@51b2d103027f30bce9dac052a0b77e13885988a5/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/51b2d103027f30bce9dac052a0b77e13885988a5/texts.json
1.57 GB
- Xet hash:
- e4bfa5112c7bf49c6747e6c92d9f23113e3f5e96cff71b0caa38fbe3f5d699a4
- Size of remote file:
- 1.57 GB
- SHA256:
- bb0ede92a8c5a961371c68b3e99a3168ea9c98cca537b3b6aaeb9b71fa95cded
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