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")# 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 satori_medium_checkpoints/step_lightning_5200.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.57 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_medium_checkpoints/step_lightning_5200.pt
- Command line
-
hf download hf://dilip025/dummy-model@670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_medium_checkpoints/step_lightning_5200.pt
-
curl -L -o step_lightning_5200.pt https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_medium_checkpoints/step_lightning_5200.pt
3.57 GB
- Xet hash:
- 01536acf75d294f4a095da27e9783af165b2f598f656c8d67d96488c32793ac8
- Size of remote file:
- 3.57 GB
- SHA256:
- dcbd0e63dff72542adbe54fcdf3a749c27832e4f09a529d91098f1f8b2afbf4f
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