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_8000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/a45cf3f9c949265b908aacf59da1db4df5857674/satori_tiny_checkpoints/step_lightning_8000.pt
- Command line
-
hf download hf://dilip025/dummy-model@a45cf3f9c949265b908aacf59da1db4df5857674/satori_tiny_checkpoints/step_lightning_8000.pt
-
curl -L -o step_lightning_8000.pt https://huggingface.co/dilip025/dummy-model/resolve/a45cf3f9c949265b908aacf59da1db4df5857674/satori_tiny_checkpoints/step_lightning_8000.pt
3.99 GB
- Xet hash:
- 13387bb6c0ba05565816f271ebd1e1db6df7bd393070c2103f5607b7b78f9f54
- Size of remote file:
- 3.99 GB
- SHA256:
- 88cd33a906622a38495fdccc7b279e9a31da996a23c9128a5c8d951dd3d6ca06
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.