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_10950.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_lightning_10950.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_small_checkpoints/step_lightning_10950.pt
-
curl -L -o step_lightning_10950.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_lightning_10950.pt
6.29 GB
- Xet hash:
- 220bc6b541349cc5aabc32b635ff05a1d8f7d43f909bfb79d4bec58ff81db8c8
- Size of remote file:
- 6.29 GB
- SHA256:
- ecaa71a0a23d3d3838f519b70073d20d71a411bd63f218d3741629c735ad0b38
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