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 516 MB
-
https://huggingface.co/dilip025/dummy-model/resolve/8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/texts.json
516 MB
- Xet hash:
- 465c57d21a548bb98ec34a5bf8343ff605ba0c724eb61b15624b6660da70c3f9
- Size of remote file:
- 516 MB
- SHA256:
- 56359f548c74e3214148cb0e6dfb9656ac784c3eec57a4af8e9bc91eb7b6ff14
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.