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_mid_checkpoints/step_lightning_4400.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_mid_checkpoints/step_lightning_4400.pt
- Command line
-
hf download hf://dilip025/dummy-model@670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_mid_checkpoints/step_lightning_4400.pt
-
curl -L -o step_lightning_4400.pt https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_mid_checkpoints/step_lightning_4400.pt
4.49 GB
- Xet hash:
- b494f8d0358d82c98a9ba323764d6d0fb5ff6757d3a22e23605cca595b459a6b
- Size of remote file:
- 4.49 GB
- SHA256:
- 004dd1d49f8a96c8af554e24e0fe4e8ecc329a0498946bd0e9c9e44108a3fe6b
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