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_v2/step_lightning_15000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_v2/step_lightning_15000.pt
- Command line
-
hf download hf://dilip025/dummy-model@753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_v2/step_lightning_15000.pt
-
curl -L -o step_lightning_15000.pt https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_v2/step_lightning_15000.pt
4.35 GB
- Xet hash:
- 8601ea0e1b6f4fc0c533237a01f2e7b046c87de50cb895d8e4550e96076eb692
- Size of remote file:
- 4.35 GB
- SHA256:
- 84a6f4662fb2b2a184b89a61e51342ae032ddefe6210e407dabd8f27ca2b166b
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