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 satori_small_checkpoints/step_lightning_12250.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/d650a7fe3bb8d15ef289c3bda99685ca39704ff8/satori_small_checkpoints/step_lightning_12250.pt
- Command line
-
hf download hf://dilip025/dummy-model@d650a7fe3bb8d15ef289c3bda99685ca39704ff8/satori_small_checkpoints/step_lightning_12250.pt
-
curl -L -o step_lightning_12250.pt https://huggingface.co/dilip025/dummy-model/resolve/d650a7fe3bb8d15ef289c3bda99685ca39704ff8/satori_small_checkpoints/step_lightning_12250.pt
6.29 GB
- Xet hash:
- 3af07785de44f4d5d4c84ed982bb0203e5777ca8a891b22954b29ff3d3cfb2d5
- Size of remote file:
- 6.29 GB
- SHA256:
- 58ed1f2a6d55654347c16d29b73c38ae108020088abd04a3e8787c417279997c
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