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_tiny_checkpoints/step_lightning_6400.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_lightning_6400.pt
- Command line
-
hf download hf://dilip025/dummy-model@24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_lightning_6400.pt
-
curl -L -o step_lightning_6400.pt https://huggingface.co/dilip025/dummy-model/resolve/24323c1ac9a2b629789bda8d00b68237de0c3772/satori_tiny_checkpoints/step_lightning_6400.pt
3.99 GB
- Xet hash:
- d1a31c854e4e6b30407833cd2ba8bf5e888d61de3ded36d03de8b535016c9728
- Size of remote file:
- 3.99 GB
- SHA256:
- bf498783075421f4bf769e1607d8e1a45c16cf6acce61ab499a46e2d689d8c43
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