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/ff3c95036173ad7bb91741d939f7fca500070172/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@ff3c95036173ad7bb91741d939f7fca500070172/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/ff3c95036173ad7bb91741d939f7fca500070172/texts.json
1.57 GB
- Xet hash:
- 55973ff13d65ff863fe3ecd3f5b2e81c147549ec8088ef792beca71a263794f9
- Size of remote file:
- 1.57 GB
- SHA256:
- d9d0996ee56abc642884f5c5e21f7a932ca9156b20991d5c5d3e38fe71c81cb3
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