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_tiny_checkpoints/step_lightning_150.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.64 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_tiny_checkpoints/step_lightning_150.pt
- Command line
-
hf download hf://dilip025/dummy-model@670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_tiny_checkpoints/step_lightning_150.pt
-
curl -L -o step_lightning_150.pt https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_tiny_checkpoints/step_lightning_150.pt
1.64 GB
- Xet hash:
- 843b3963dcdf9d85d5504cb522cdd69e26e948ef6c6cd90bb17e8aa53e716cad
- Size of remote file:
- 1.64 GB
- SHA256:
- 1c758d0155aa12bf80d6fa95ecf4ceb8dea5fc2c02f5aada0adb8dc1c8c305e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.