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_tienie_checkpoints/step_lightning_4000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.26 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tienie_checkpoints/step_lightning_4000.pt
- Command line
-
hf download hf://dilip025/dummy-model@57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tienie_checkpoints/step_lightning_4000.pt
-
curl -L -o step_lightning_4000.pt https://huggingface.co/dilip025/dummy-model/resolve/57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tienie_checkpoints/step_lightning_4000.pt
2.26 GB
- Xet hash:
- f04896d018a52526ef3e58eb3603d9ec3af8003c2deb445d5727af1f2b8db839
- Size of remote file:
- 2.26 GB
- SHA256:
- c099bc032dedadb2886d0d92a1cebfaa609ac47fe997ba1ea2268e9e953c1dcb
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