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_4200.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_4200.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_medium_checkpoints/step_lightning_4200.pt
-
curl -L -o step_lightning_4200.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_medium_checkpoints/step_lightning_4200.pt
3.57 GB
- Xet hash:
- 681a90b0e412b90a8903a103079bd7e6d4618ea53935e2a5b3e2b4e27b2f691d
- Size of remote file:
- 3.57 GB
- SHA256:
- c21a3408bbd2d97fc45beed6905844b9c1d1201d99bc7aff2151538fb54007fd
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