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_300.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.64 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bbf8ed253f162fd7a66199b38894d5433daeb079/satori_tiny_checkpoints/step_lightning_300.pt
- Command line
-
hf download hf://dilip025/dummy-model@bbf8ed253f162fd7a66199b38894d5433daeb079/satori_tiny_checkpoints/step_lightning_300.pt
-
curl -L -o step_lightning_300.pt https://huggingface.co/dilip025/dummy-model/resolve/bbf8ed253f162fd7a66199b38894d5433daeb079/satori_tiny_checkpoints/step_lightning_300.pt
1.64 GB
- Xet hash:
- d106234a48878c82bd4add476e9369a678ee7758c7078e8e2b0a209b4f1d3fa5
- Size of remote file:
- 1.64 GB
- SHA256:
- 510bc032f0980246f16b964af3f3a1a47f1d8c5dad38955702a578b6688d3905
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