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_medium_checkpoints/step_lightning_600.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.57 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_medium_checkpoints/step_lightning_600.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_medium_checkpoints/step_lightning_600.pt
-
curl -L -o step_lightning_600.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_medium_checkpoints/step_lightning_600.pt
3.57 GB
- Xet hash:
- d0ff6832c93f8e75500163cfdd2bb1dd689d360dd15d78f9f0986a0ac8ea4c76
- Size of remote file:
- 3.57 GB
- SHA256:
- d875bf848bc0bc256e14bd5fa29ef3fca87baebe5e5e14413def20e81452b05d
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