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_1400.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_1400.pt
- Command line
-
hf download hf://dilip025/dummy-model@ff3c95036173ad7bb91741d939f7fca500070172/satori_tiny_checkpoints/step_lightning_1400.pt
-
curl -L -o step_lightning_1400.pt https://huggingface.co/dilip025/dummy-model/resolve/ff3c95036173ad7bb91741d939f7fca500070172/satori_tiny_checkpoints/step_lightning_1400.pt
3.99 GB
- Xet hash:
- d267f6085a96749a262f28a53664d8c7ab4eadc66ba27f2d7ca55fae5b0b69d0
- Size of remote file:
- 3.99 GB
- SHA256:
- beb1a92c311b3dcbde00d81e2c9dcc125f4572173fc4ca14dc0a8fa5e5cc6dde
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