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_v2/step_lightning_6000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bd32f21e80e00fc39cfe3e4a6c5020192781774b/satori_v2/step_lightning_6000.pt
- Command line
-
hf download hf://dilip025/dummy-model@bd32f21e80e00fc39cfe3e4a6c5020192781774b/satori_v2/step_lightning_6000.pt
-
curl -L -o step_lightning_6000.pt https://huggingface.co/dilip025/dummy-model/resolve/bd32f21e80e00fc39cfe3e4a6c5020192781774b/satori_v2/step_lightning_6000.pt
4.35 GB
- Xet hash:
- b592b58e341f84d4eabc5fd28cf6568073dd769b8a1e030dec04e3dbb1b90a10
- Size of remote file:
- 4.35 GB
- SHA256:
- 82f546f0fffca7dac91ac4e4a4e2d7ad00b35fa001a780fc9aa034ed9a4b137e
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