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_medium_checkpoints/step_lightning_4000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.57 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_medium_checkpoints/step_lightning_4000.pt
- Command line
-
hf download hf://dilip025/dummy-model@753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_medium_checkpoints/step_lightning_4000.pt
-
curl -L -o step_lightning_4000.pt https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_medium_checkpoints/step_lightning_4000.pt
3.57 GB
- Xet hash:
- 8ec57234403ba7c56779831d03f9e5dc69b7f3bfa6f8ffe3eb5b202e86e3429f
- Size of remote file:
- 3.57 GB
- SHA256:
- 535c5952f2f8610286c10325e76e70afc67c8243718859f1337da84c272f71da
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