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_2000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/925b4322d65f6972a59aa0216e382dc1ab3c8fb8/satori_small_checkpoints/step_lightning_2000.pt
- Command line
-
hf download hf://dilip025/dummy-model@925b4322d65f6972a59aa0216e382dc1ab3c8fb8/satori_small_checkpoints/step_lightning_2000.pt
-
curl -L -o step_lightning_2000.pt https://huggingface.co/dilip025/dummy-model/resolve/925b4322d65f6972a59aa0216e382dc1ab3c8fb8/satori_small_checkpoints/step_lightning_2000.pt
6.29 GB
- Xet hash:
- 323fc723e65a03ceb82137adc9b2004acaf68b33572547e96f00bbeb5a325901
- Size of remote file:
- 6.29 GB
- SHA256:
- cc29dc44b951030ebe15b743265152e03f0d90f9f097130ebef71bffcce91415
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