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_tiny_checkpoints/step_lightning_1200.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/ff3c95036173ad7bb91741d939f7fca500070172/satori_tiny_checkpoints/step_lightning_1200.pt
- Command line
-
hf download hf://dilip025/dummy-model@ff3c95036173ad7bb91741d939f7fca500070172/satori_tiny_checkpoints/step_lightning_1200.pt
-
curl -L -o step_lightning_1200.pt https://huggingface.co/dilip025/dummy-model/resolve/ff3c95036173ad7bb91741d939f7fca500070172/satori_tiny_checkpoints/step_lightning_1200.pt
3.99 GB
- Xet hash:
- cfe6da3fe4e2bd3eb3c67658f5359a47ecfb5d212566edb7aade34d1b78985dd
- Size of remote file:
- 3.99 GB
- SHA256:
- 18bdb7ecb805b3389d879e9a2e9c5dcc4b84355914a6bc634a6f79690d517514
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