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_800.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/22d7060d4e65960b27686cb03ee4daa8f967b8f4/satori_mid_checkpoints/step_lightning_800.pt
- Command line
-
hf download hf://dilip025/dummy-model@22d7060d4e65960b27686cb03ee4daa8f967b8f4/satori_mid_checkpoints/step_lightning_800.pt
-
curl -L -o step_lightning_800.pt https://huggingface.co/dilip025/dummy-model/resolve/22d7060d4e65960b27686cb03ee4daa8f967b8f4/satori_mid_checkpoints/step_lightning_800.pt
4.49 GB
- Xet hash:
- 2cb5414a6153beca9335837c910a2b8f5b2c86721abeb62ab3f7f57f3de67f5f
- Size of remote file:
- 4.49 GB
- SHA256:
- 3abe422571c3121d97ffd449ecca4ab0ced46030ad550837dfcf583b576cbb72
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