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_small_checkpoints/step_lightning_10550.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/520694c2680e5346fe915b66a46ad997bc188d69/satori_small_checkpoints/step_lightning_10550.pt
- Command line
-
hf download hf://dilip025/dummy-model@520694c2680e5346fe915b66a46ad997bc188d69/satori_small_checkpoints/step_lightning_10550.pt
-
curl -L -o step_lightning_10550.pt https://huggingface.co/dilip025/dummy-model/resolve/520694c2680e5346fe915b66a46ad997bc188d69/satori_small_checkpoints/step_lightning_10550.pt
6.29 GB
- Xet hash:
- b58115e9eae03e8cf9286fc8f9c2078c0e308c37b4c435814d1bbc985b1f6611
- Size of remote file:
- 6.29 GB
- SHA256:
- b27f8544a12e0d8e1a5a5af1ae2a03e87f62da1357373fa37220a6cbb2c30008
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