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_prajna_checkpoints/step_lightning_38000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/2336634a69ce3d41b12686a252cabda4883da02a/satori_prajna_checkpoints/step_lightning_38000.pt
- Command line
-
hf download hf://dilip025/dummy-model@2336634a69ce3d41b12686a252cabda4883da02a/satori_prajna_checkpoints/step_lightning_38000.pt
-
curl -L -o step_lightning_38000.pt https://huggingface.co/dilip025/dummy-model/resolve/2336634a69ce3d41b12686a252cabda4883da02a/satori_prajna_checkpoints/step_lightning_38000.pt
2.81 GB
- Xet hash:
- a3132980f7a30343d1f2962d4b2b5b2d677144334a90161c6d7983dc86c251a2
- Size of remote file:
- 2.81 GB
- SHA256:
- 5527352ef3e56c0414854ca6bf625d5cab55bd30879d3f742dbfee6fac5a4483
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