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_mid_checkpoints/step_lightning_1000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.49 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_1000.pt
- Command line
-
hf download hf://dilip025/dummy-model@bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_1000.pt
-
curl -L -o step_lightning_1000.pt https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/satori_mid_checkpoints/step_lightning_1000.pt
4.49 GB
- Xet hash:
- 59e27af32138f7021b6dc4fb3f24d23922b17db4d53fe8bc7bd5fb4ed576de0f
- Size of remote file:
- 4.49 GB
- SHA256:
- f1f2a81be565dcff268d2a6d82bd5fac7ac541908443b488e21df31db83d4f01
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