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_3800.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_mid_checkpoints/step_lightning_3800.pt
- Command line
-
hf download hf://dilip025/dummy-model@7837c07ea958e6aabde620fbf35619d8fe048737/satori_mid_checkpoints/step_lightning_3800.pt
-
curl -L -o step_lightning_3800.pt https://huggingface.co/dilip025/dummy-model/resolve/7837c07ea958e6aabde620fbf35619d8fe048737/satori_mid_checkpoints/step_lightning_3800.pt
4.49 GB
- Xet hash:
- 804e287e34c4cd8bcad8c9261e59bd0960001750280b69a78e36ceff938aa121
- Size of remote file:
- 4.49 GB
- SHA256:
- 818017ca0dfd0f9aa9321dd7593652a870103ad845630711cc95384d54612cf2
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