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_7200.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/a5f21fcec901d580152693450d5688a4b80fd917/satori_small_checkpoints/step_lightning_7200.pt
- Command line
-
hf download hf://dilip025/dummy-model@a5f21fcec901d580152693450d5688a4b80fd917/satori_small_checkpoints/step_lightning_7200.pt
-
curl -L -o step_lightning_7200.pt https://huggingface.co/dilip025/dummy-model/resolve/a5f21fcec901d580152693450d5688a4b80fd917/satori_small_checkpoints/step_lightning_7200.pt
6.29 GB
- Xet hash:
- a5ab54fbae0bd627420f60d58930c444cdcaa520270ae1495fa212a309cf2bec
- Size of remote file:
- 6.29 GB
- SHA256:
- 6834e1ae3fbf0db209cbf88c460c6f55edf4e672af85d9b920728d59d2738630
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